diff --git a/.gitignore b/.gitignore
index 02f0c972..8fc3298a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -45,3 +45,5 @@ flask_session
 tmp**cwd
 /tmp_images
 nul
+/.github/plans
+*.xlsx
diff --git a/CLAUDE.md b/CLAUDE.md
index 4e9c2830..2ad07a8a 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -58,6 +58,7 @@ return render_template('page.html', settings=public_settings)
 
 ## Version Management
 
+- Its important to update the version at the end of every plan
 - Version is stored in `config.py`: `VERSION = "X.XXX.XXX"`
 - When incrementing, only change the third segment (e.g., `0.238.024` -> `0.238.025`)
 - Include the current version in functional test file headers and documentation files
@@ -83,7 +84,7 @@ return render_template('page.html', settings=public_settings)
 
 ## Release Notes
 
-After completing code changes, offer to update `docs/explanation/release_notes.md`.
+After completing plans and code changes, offer to update `docs/explanation/release_notes.md`.
 
 - Add entries under the current version from `config.py`
 - If the version was bumped, create a new section at the top: `### **(vX.XXX.XXX)**`
diff --git a/README.md b/README.md
index 31ea020b..cffbeedc 100644
--- a/README.md
+++ b/README.md
@@ -144,6 +144,7 @@ azd up
 - **Metadata Extraction (Optional)**: Apply an AI model (configurable GPT model via Admin Settings) to automatically generate keywords, two-sentence summaries, and infer author/date for uploaded documents. Allows manual override for richer search context.
 - **File Processing Logs (Optional)**: Enable verbose logging for all ingestion pipelines (workspaces and ephemeral chat uploads) to aid in debugging, monitoring, and auditing file processing steps.
 - **Redis Cache (Optional)**: Integrate Azure Cache for Redis to provide a distributed, high-performance session store. This enables true horizontal scaling and high availability by decoupling user sessions from individual app instances.
+- **SQL Database Agents (Optional)**: Connect agents to Azure SQL or other SQL databases through configurable SQL Query and SQL Schema plugins. Database schema is automatically discovered and injected into agent instructions at load time, enabling agents to answer natural language questions by generating and executing SQL queries without requiring users to know table or column names.
 - **Authentication & RBAC**: Secure access via Azure Active Directory (Entra ID) using MSAL. Supports Managed Identities for Azure service authentication, group-based controls, and custom application roles (`Admin`, `User`, `CreateGroup`, `SafetyAdmin`, `FeedbackAdmin`).
 - **Supported File Types**:
 
diff --git a/application/single_app/app.py b/application/single_app/app.py
index 805137d4..594d245d 100644
--- a/application/single_app/app.py
+++ b/application/single_app/app.py
@@ -75,6 +75,7 @@
 from route_backend_public_prompts import *
 from route_backend_user_agreement import register_route_backend_user_agreement
 from route_backend_conversation_export import register_route_backend_conversation_export
+from route_backend_thoughts import register_route_backend_thoughts
 from route_backend_speech import register_route_backend_speech
 from route_backend_tts import register_route_backend_tts
 from route_enhanced_citations import register_enhanced_citations_routes
@@ -657,6 +658,9 @@ def list_semantic_kernel_plugins():
 # ------------------- API User Agreement Routes ----------
 register_route_backend_user_agreement(app)
 
+# ------------------- API Thoughts Routes ----------------
+register_route_backend_thoughts(app)
+
 # ------------------- Extenral Health Routes ----------
 register_route_external_health(app)
 
diff --git a/application/single_app/config.py b/application/single_app/config.py
index 08c0adf1..d510618b 100644
--- a/application/single_app/config.py
+++ b/application/single_app/config.py
@@ -94,7 +94,7 @@
 EXECUTOR_TYPE = 'thread'
 EXECUTOR_MAX_WORKERS = 30
 SESSION_TYPE = 'filesystem'
-VERSION = "0.239.005"
+VERSION = "0.239.112"
 
 SECRET_KEY = os.getenv('SECRET_KEY', 'dev-secret-key-change-in-production')
 
@@ -257,6 +257,8 @@ def get_redis_cache_infrastructure_endpoint(redis_hostname: str) -> str:
 storage_account_user_documents_container_name = "user-documents"
 storage_account_group_documents_container_name = "group-documents"
 storage_account_public_documents_container_name = "public-documents"
+storage_account_personal_chat_container_name = "personal-chat"
+storage_account_group_chat_container_name = "group-chat"
 
 # Initialize Azure Cosmos DB client
 cosmos_endpoint = os.getenv("AZURE_COSMOS_ENDPOINT")
@@ -459,6 +461,18 @@ def get_redis_cache_infrastructure_endpoint(redis_hostname: str) -> str:
     default_ttl=-1  # TTL disabled by default, enabled per-document for auto-cleanup
 )
 
+cosmos_thoughts_container_name = "thoughts"
+cosmos_thoughts_container = cosmos_database.create_container_if_not_exists(
+    id=cosmos_thoughts_container_name,
+    partition_key=PartitionKey(path="/user_id")
+)
+
+cosmos_archived_thoughts_container_name = "archive_thoughts"
+cosmos_archived_thoughts_container = cosmos_database.create_container_if_not_exists(
+    id=cosmos_archived_thoughts_container_name,
+    partition_key=PartitionKey(path="/user_id")
+)
+
 def ensure_custom_logo_file_exists(app, settings):
     """
     If custom_logo_base64 or custom_logo_dark_base64 is present in settings, ensure the appropriate
@@ -745,9 +759,11 @@ def initialize_clients(settings):
                 # This addresses the issue where the application assumes containers exist
                 if blob_service_client:
                     for container_name in [
-                        storage_account_user_documents_container_name, 
-                        storage_account_group_documents_container_name, 
-                        storage_account_public_documents_container_name
+                        storage_account_user_documents_container_name,
+                        storage_account_group_documents_container_name,
+                        storage_account_public_documents_container_name,
+                        storage_account_personal_chat_container_name,
+                        storage_account_group_chat_container_name
                         ]:
                         try:
                             container_client = blob_service_client.get_container_client(container_name)
diff --git a/application/single_app/functions_activity_logging.py b/application/single_app/functions_activity_logging.py
index 2a653a47..efb6e780 100644
--- a/application/single_app/functions_activity_logging.py
+++ b/application/single_app/functions_activity_logging.py
@@ -1393,3 +1393,332 @@ def log_retention_policy_force_push(
             level=logging.ERROR
         )
         debug_print(f"⚠️  Warning: Failed to log retention policy force push: {str(e)}")
+
+
+# === AGENT & ACTION ACTIVITY LOGGING ===
+
+def log_agent_creation(
+    user_id: str,
+    agent_id: str,
+    agent_name: str,
+    agent_display_name: Optional[str] = None,
+    scope: str = 'personal',
+    group_id: Optional[str] = None
+) -> None:
+    """
+    Log an agent creation activity.
+
+    Args:
+        user_id: The ID of the user who created the agent
+        agent_id: The unique ID of the new agent
+        agent_name: The name of the agent
+        agent_display_name: The display name of the agent
+        scope: 'personal', 'group', or 'global'
+        group_id: The group ID (only for group scope)
+    """
+    try:
+        activity_record = {
+            'id': str(uuid.uuid4()),
+            'user_id': user_id,
+            'activity_type': 'agent_creation',
+            'timestamp': datetime.utcnow().isoformat(),
+            'created_at': datetime.utcnow().isoformat(),
+            'entity_type': 'agent',
+            'operation': 'create',
+            'entity': {
+                'id': agent_id,
+                'name': agent_name,
+                'display_name': agent_display_name or agent_name
+            },
+            'workspace_type': scope,
+            'workspace_context': {}
+        }
+        if scope == 'group' and group_id:
+            activity_record['workspace_context']['group_id'] = group_id
+
+        cosmos_activity_logs_container.create_item(body=activity_record)
+        log_event(
+            message=f"Agent created: {agent_name} ({scope}) by user {user_id}",
+            extra=activity_record,
+            level=logging.INFO
+        )
+        debug_print(f"✅ Agent creation logged: {agent_name} ({scope})")
+    except Exception as e:
+        log_event(
+            message=f"Error logging agent creation: {str(e)}",
+            extra={'user_id': user_id, 'agent_id': agent_id, 'scope': scope, 'error': str(e)},
+            level=logging.ERROR
+        )
+        debug_print(f"⚠️  Warning: Failed to log agent creation: {str(e)}")
+
+
+def log_agent_update(
+    user_id: str,
+    agent_id: str,
+    agent_name: str,
+    agent_display_name: Optional[str] = None,
+    scope: str = 'personal',
+    group_id: Optional[str] = None
+) -> None:
+    """
+    Log an agent update activity.
+
+    Args:
+        user_id: The ID of the user who updated the agent
+        agent_id: The unique ID of the agent
+        agent_name: The name of the agent
+        agent_display_name: The display name of the agent
+        scope: 'personal', 'group', or 'global'
+        group_id: The group ID (only for group scope)
+    """
+    try:
+        activity_record = {
+            'id': str(uuid.uuid4()),
+            'user_id': user_id,
+            'activity_type': 'agent_update',
+            'timestamp': datetime.utcnow().isoformat(),
+            'created_at': datetime.utcnow().isoformat(),
+            'entity_type': 'agent',
+            'operation': 'update',
+            'entity': {
+                'id': agent_id,
+                'name': agent_name,
+                'display_name': agent_display_name or agent_name
+            },
+            'workspace_type': scope,
+            'workspace_context': {}
+        }
+        if scope == 'group' and group_id:
+            activity_record['workspace_context']['group_id'] = group_id
+
+        cosmos_activity_logs_container.create_item(body=activity_record)
+        log_event(
+            message=f"Agent updated: {agent_name} ({scope}) by user {user_id}",
+            extra=activity_record,
+            level=logging.INFO
+        )
+        debug_print(f"✅ Agent update logged: {agent_name} ({scope})")
+    except Exception as e:
+        log_event(
+            message=f"Error logging agent update: {str(e)}",
+            extra={'user_id': user_id, 'agent_id': agent_id, 'scope': scope, 'error': str(e)},
+            level=logging.ERROR
+        )
+        debug_print(f"⚠️  Warning: Failed to log agent update: {str(e)}")
+
+
+def log_agent_deletion(
+    user_id: str,
+    agent_id: str,
+    agent_name: str,
+    scope: str = 'personal',
+    group_id: Optional[str] = None
+) -> None:
+    """
+    Log an agent deletion activity.
+
+    Args:
+        user_id: The ID of the user who deleted the agent
+        agent_id: The unique ID of the agent
+        agent_name: The name of the agent
+        scope: 'personal', 'group', or 'global'
+        group_id: The group ID (only for group scope)
+    """
+    try:
+        activity_record = {
+            'id': str(uuid.uuid4()),
+            'user_id': user_id,
+            'activity_type': 'agent_deletion',
+            'timestamp': datetime.utcnow().isoformat(),
+            'created_at': datetime.utcnow().isoformat(),
+            'entity_type': 'agent',
+            'operation': 'delete',
+            'entity': {
+                'id': agent_id,
+                'name': agent_name
+            },
+            'workspace_type': scope,
+            'workspace_context': {}
+        }
+        if scope == 'group' and group_id:
+            activity_record['workspace_context']['group_id'] = group_id
+
+        cosmos_activity_logs_container.create_item(body=activity_record)
+        log_event(
+            message=f"Agent deleted: {agent_name} ({scope}) by user {user_id}",
+            extra=activity_record,
+            level=logging.INFO
+        )
+        debug_print(f"✅ Agent deletion logged: {agent_name} ({scope})")
+    except Exception as e:
+        log_event(
+            message=f"Error logging agent deletion: {str(e)}",
+            extra={'user_id': user_id, 'agent_id': agent_id, 'scope': scope, 'error': str(e)},
+            level=logging.ERROR
+        )
+        debug_print(f"⚠️  Warning: Failed to log agent deletion: {str(e)}")
+
+
+def log_action_creation(
+    user_id: str,
+    action_id: str,
+    action_name: str,
+    action_type: Optional[str] = None,
+    scope: str = 'personal',
+    group_id: Optional[str] = None
+) -> None:
+    """
+    Log an action/plugin creation activity.
+
+    Args:
+        user_id: The ID of the user who created the action
+        action_id: The unique ID of the new action
+        action_name: The name of the action
+        action_type: The type of the action (e.g., 'openapi', 'sql_query')
+        scope: 'personal', 'group', or 'global'
+        group_id: The group ID (only for group scope)
+    """
+    try:
+        activity_record = {
+            'id': str(uuid.uuid4()),
+            'user_id': user_id,
+            'activity_type': 'action_creation',
+            'timestamp': datetime.utcnow().isoformat(),
+            'created_at': datetime.utcnow().isoformat(),
+            'entity_type': 'action',
+            'operation': 'create',
+            'entity': {
+                'id': action_id,
+                'name': action_name,
+                'type': action_type
+            },
+            'workspace_type': scope,
+            'workspace_context': {}
+        }
+        if scope == 'group' and group_id:
+            activity_record['workspace_context']['group_id'] = group_id
+
+        cosmos_activity_logs_container.create_item(body=activity_record)
+        log_event(
+            message=f"Action created: {action_name} ({scope}) by user {user_id}",
+            extra=activity_record,
+            level=logging.INFO
+        )
+        debug_print(f"✅ Action creation logged: {action_name} ({scope})")
+    except Exception as e:
+        log_event(
+            message=f"Error logging action creation: {str(e)}",
+            extra={'user_id': user_id, 'action_id': action_id, 'scope': scope, 'error': str(e)},
+            level=logging.ERROR
+        )
+        debug_print(f"⚠️  Warning: Failed to log action creation: {str(e)}")
+
+
+def log_action_update(
+    user_id: str,
+    action_id: str,
+    action_name: str,
+    action_type: Optional[str] = None,
+    scope: str = 'personal',
+    group_id: Optional[str] = None
+) -> None:
+    """
+    Log an action/plugin update activity.
+
+    Args:
+        user_id: The ID of the user who updated the action
+        action_id: The unique ID of the action
+        action_name: The name of the action
+        action_type: The type of the action
+        scope: 'personal', 'group', or 'global'
+        group_id: The group ID (only for group scope)
+    """
+    try:
+        activity_record = {
+            'id': str(uuid.uuid4()),
+            'user_id': user_id,
+            'activity_type': 'action_update',
+            'timestamp': datetime.utcnow().isoformat(),
+            'created_at': datetime.utcnow().isoformat(),
+            'entity_type': 'action',
+            'operation': 'update',
+            'entity': {
+                'id': action_id,
+                'name': action_name,
+                'type': action_type
+            },
+            'workspace_type': scope,
+            'workspace_context': {}
+        }
+        if scope == 'group' and group_id:
+            activity_record['workspace_context']['group_id'] = group_id
+
+        cosmos_activity_logs_container.create_item(body=activity_record)
+        log_event(
+            message=f"Action updated: {action_name} ({scope}) by user {user_id}",
+            extra=activity_record,
+            level=logging.INFO
+        )
+        debug_print(f"✅ Action update logged: {action_name} ({scope})")
+    except Exception as e:
+        log_event(
+            message=f"Error logging action update: {str(e)}",
+            extra={'user_id': user_id, 'action_id': action_id, 'scope': scope, 'error': str(e)},
+            level=logging.ERROR
+        )
+        debug_print(f"⚠️  Warning: Failed to log action update: {str(e)}")
+
+
+def log_action_deletion(
+    user_id: str,
+    action_id: str,
+    action_name: str,
+    action_type: Optional[str] = None,
+    scope: str = 'personal',
+    group_id: Optional[str] = None
+) -> None:
+    """
+    Log an action/plugin deletion activity.
+
+    Args:
+        user_id: The ID of the user who deleted the action
+        action_id: The unique ID of the action
+        action_name: The name of the action
+        action_type: The type of the action
+        scope: 'personal', 'group', or 'global'
+        group_id: The group ID (only for group scope)
+    """
+    try:
+        activity_record = {
+            'id': str(uuid.uuid4()),
+            'user_id': user_id,
+            'activity_type': 'action_deletion',
+            'timestamp': datetime.utcnow().isoformat(),
+            'created_at': datetime.utcnow().isoformat(),
+            'entity_type': 'action',
+            'operation': 'delete',
+            'entity': {
+                'id': action_id,
+                'name': action_name,
+                'type': action_type
+            },
+            'workspace_type': scope,
+            'workspace_context': {}
+        }
+        if scope == 'group' and group_id:
+            activity_record['workspace_context']['group_id'] = group_id
+
+        cosmos_activity_logs_container.create_item(body=activity_record)
+        log_event(
+            message=f"Action deleted: {action_name} ({scope}) by user {user_id}",
+            extra=activity_record,
+            level=logging.INFO
+        )
+        debug_print(f"✅ Action deletion logged: {action_name} ({scope})")
+    except Exception as e:
+        log_event(
+            message=f"Error logging action deletion: {str(e)}",
+            extra={'user_id': user_id, 'action_id': action_id, 'scope': scope, 'error': str(e)},
+            level=logging.ERROR
+        )
+        debug_print(f"⚠️  Warning: Failed to log action deletion: {str(e)}")
diff --git a/application/single_app/functions_agent_payload.py b/application/single_app/functions_agent_payload.py
index 09f1f343..2a7935a5 100644
--- a/application/single_app/functions_agent_payload.py
+++ b/application/single_app/functions_agent_payload.py
@@ -45,6 +45,21 @@
     "azure_agent_apim_gpt_deployment",
     "azure_agent_apim_gpt_api_version",
 ]
+_SERVER_MANAGED_FIELDS = [
+    "_attachments",
+    "_etag",
+    "_rid",
+    "_self",
+    "_ts",
+    "created_at",
+    "created_by",
+    "modified_at",
+    "modified_by",
+    "updated_at",
+    "last_updated",
+    "user_id",
+    "group_id",
+]
 
 _MAX_FIELD_LENGTHS = {
     "name": 100,
@@ -146,12 +161,18 @@ def _validate_foundry_field_lengths(foundry_settings: Dict[str, Any]) -> None:
         if isinstance(value, str) and len(value) > max_len:
             raise AgentPayloadError(f"azure_ai_foundry.{field} exceeds maximum length of {max_len}.")
 
+
+def _strip_server_managed_fields(payload: Dict[str, Any]) -> None:
+    for field in _SERVER_MANAGED_FIELDS:
+        payload.pop(field, None)
+
 def sanitize_agent_payload(agent: Dict[str, Any]) -> Dict[str, Any]:
     """Return a sanitized copy of the agent payload or raise AgentPayloadError."""
     if not isinstance(agent, dict):
         raise AgentPayloadError("Agent payload must be an object.")
 
     sanitized = deepcopy(agent)
+    _strip_server_managed_fields(sanitized)
     _normalize_text_fields(sanitized)
 
     for field in _STRING_DEFAULT_FIELDS:
diff --git a/application/single_app/functions_content.py b/application/single_app/functions_content.py
index 376d23f4..3116ed82 100644
--- a/application/single_app/functions_content.py
+++ b/application/single_app/functions_content.py
@@ -352,7 +352,7 @@ def generate_embedding(
                 embedding_model = selected_embedding_model['deploymentName']
 
     while True:
-        random_delay = random.uniform(0.5, 2.0)
+        random_delay = random.uniform(0.05, 0.2)
         time.sleep(random_delay)
 
         try:
@@ -385,3 +385,102 @@ def generate_embedding(
 
         except Exception as e:
             raise
+
+def generate_embeddings_batch(
+    texts,
+    batch_size=16,
+    max_retries=5,
+    initial_delay=1.0,
+    delay_multiplier=2.0
+):
+    """Generate embeddings for multiple texts in batches.
+
+    Azure OpenAI embeddings API accepts a list of strings as input.
+    This reduces per-call overhead and delay significantly.
+
+    Args:
+        texts: List of text strings to embed.
+        batch_size: Number of texts per API call (default 16).
+        max_retries: Max retries on rate limit errors.
+        initial_delay: Initial retry delay in seconds.
+        delay_multiplier: Multiplier for exponential backoff.
+
+    Returns:
+        list of (embedding, token_usage) tuples, one per input text.
+    """
+    settings = get_settings()
+
+    enable_embedding_apim = settings.get('enable_embedding_apim', False)
+
+    if enable_embedding_apim:
+        embedding_model = settings.get('azure_apim_embedding_deployment')
+        embedding_client = AzureOpenAI(
+            api_version=settings.get('azure_apim_embedding_api_version'),
+            azure_endpoint=settings.get('azure_apim_embedding_endpoint'),
+            api_key=settings.get('azure_apim_embedding_subscription_key'))
+    else:
+        if (settings.get('azure_openai_embedding_authentication_type') == 'managed_identity'):
+            token_provider = get_bearer_token_provider(DefaultAzureCredential(), cognitive_services_scope)
+
+            embedding_client = AzureOpenAI(
+                api_version=settings.get('azure_openai_embedding_api_version'),
+                azure_endpoint=settings.get('azure_openai_embedding_endpoint'),
+                azure_ad_token_provider=token_provider
+            )
+
+            embedding_model_obj = settings.get('embedding_model', {})
+            if embedding_model_obj and embedding_model_obj.get('selected'):
+                selected_embedding_model = embedding_model_obj['selected'][0]
+                embedding_model = selected_embedding_model['deploymentName']
+        else:
+            embedding_client = AzureOpenAI(
+                api_version=settings.get('azure_openai_embedding_api_version'),
+                azure_endpoint=settings.get('azure_openai_embedding_endpoint'),
+                api_key=settings.get('azure_openai_embedding_key')
+            )
+
+            embedding_model_obj = settings.get('embedding_model', {})
+            if embedding_model_obj and embedding_model_obj.get('selected'):
+                selected_embedding_model = embedding_model_obj['selected'][0]
+                embedding_model = selected_embedding_model['deploymentName']
+
+    results = []
+    for i in range(0, len(texts), batch_size):
+        batch = texts[i:i + batch_size]
+        retries = 0
+        current_delay = initial_delay
+
+        while True:
+            random_delay = random.uniform(0.05, 0.2)
+            time.sleep(random_delay)
+
+            try:
+                response = embedding_client.embeddings.create(
+                    model=embedding_model,
+                    input=batch
+                )
+
+                for item in response.data:
+                    token_usage = None
+                    if hasattr(response, 'usage') and response.usage:
+                        token_usage = {
+                            'prompt_tokens': response.usage.prompt_tokens // len(batch),
+                            'total_tokens': response.usage.total_tokens // len(batch),
+                            'model_deployment_name': embedding_model
+                        }
+                    results.append((item.embedding, token_usage))
+                break
+
+            except RateLimitError as e:
+                retries += 1
+                if retries > max_retries:
+                    raise
+
+                wait_time = current_delay * random.uniform(1.0, 1.5)
+                time.sleep(wait_time)
+                current_delay *= delay_multiplier
+
+            except Exception as e:
+                raise
+
+    return results
diff --git a/application/single_app/functions_documents.py b/application/single_app/functions_documents.py
index ce08066d..87c9ba34 100644
--- a/application/single_app/functions_documents.py
+++ b/application/single_app/functions_documents.py
@@ -1646,6 +1646,191 @@ def save_chunks(page_text_content, page_number, file_name, user_id, document_id,
     # Return token usage information for accumulation
     return token_usage
 
+def save_chunks_batch(chunks_data, user_id, document_id, group_id=None, public_workspace_id=None):
+    """
+    Save multiple chunks at once using batch embedding and batch AI Search upload.
+    Significantly faster than calling save_chunks() per chunk.
+
+    Args:
+        chunks_data: list of dicts with keys: page_text_content, page_number, file_name
+        user_id: The user ID
+        document_id: The document ID
+        group_id: Optional group ID for group documents
+        public_workspace_id: Optional public workspace ID for public documents
+
+    Returns:
+        dict with 'total_tokens', 'prompt_tokens', 'model_deployment_name'
+    """
+    from functions_content import generate_embeddings_batch
+
+    current_time = datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ')
+    is_group = group_id is not None
+    is_public_workspace = public_workspace_id is not None
+
+    # Retrieve metadata once for all chunks
+    try:
+        if is_public_workspace:
+            metadata = get_document_metadata(
+                document_id=document_id,
+                user_id=user_id,
+                public_workspace_id=public_workspace_id
+            )
+        elif is_group:
+            metadata = get_document_metadata(
+                document_id=document_id,
+                user_id=user_id,
+                group_id=group_id
+            )
+        else:
+            metadata = get_document_metadata(
+                document_id=document_id,
+                user_id=user_id
+            )
+
+        if not metadata:
+            raise ValueError(f"No metadata found for document {document_id}")
+
+        version = metadata.get("version") if metadata.get("version") else 1
+    except Exception as e:
+        log_event(f"[save_chunks_batch] Error retrieving metadata for document {document_id}: {repr(e)}", level=logging.ERROR)
+        raise
+
+    # Generate all embeddings in batches
+    texts = [c['page_text_content'] for c in chunks_data]
+    try:
+        embedding_results = generate_embeddings_batch(texts)
+    except Exception as e:
+        log_event(f"[save_chunks_batch] Error generating batch embeddings for document {document_id}: {e}", level=logging.ERROR)
+        raise
+
+    # Check for vision analysis once
+    vision_analysis = metadata.get('vision_analysis')
+    vision_text = ""
+    if vision_analysis:
+        vision_text_parts = []
+        vision_text_parts.append("\n\n=== AI Vision Analysis ===")
+        vision_text_parts.append(f"Model: {vision_analysis.get('model', 'unknown')}")
+        if vision_analysis.get('description'):
+            vision_text_parts.append(f"\nDescription: {vision_analysis['description']}")
+        if vision_analysis.get('objects'):
+            objects_list = vision_analysis['objects']
+            if isinstance(objects_list, list):
+                vision_text_parts.append(f"\nObjects Detected: {', '.join(objects_list)}")
+            else:
+                vision_text_parts.append(f"\nObjects Detected: {objects_list}")
+        if vision_analysis.get('text'):
+            vision_text_parts.append(f"\nVisible Text: {vision_analysis['text']}")
+        if vision_analysis.get('analysis'):
+            vision_text_parts.append(f"\nContextual Analysis: {vision_analysis['analysis']}")
+        vision_text = "\n".join(vision_text_parts)
+
+    # Build all chunk documents
+    chunk_documents = []
+    total_token_usage = {'total_tokens': 0, 'prompt_tokens': 0, 'model_deployment_name': None}
+
+    for idx, chunk_info in enumerate(chunks_data):
+        embedding, token_usage = embedding_results[idx]
+        page_number = chunk_info['page_number']
+        file_name = chunk_info['file_name']
+        page_text_content = chunk_info['page_text_content']
+
+        if token_usage:
+            total_token_usage['total_tokens'] += token_usage.get('total_tokens', 0)
+            total_token_usage['prompt_tokens'] += token_usage.get('prompt_tokens', 0)
+            if not total_token_usage['model_deployment_name']:
+                total_token_usage['model_deployment_name'] = token_usage.get('model_deployment_name')
+
+        chunk_id = f"{document_id}_{page_number}"
+        enhanced_chunk_text = page_text_content + vision_text if vision_text else page_text_content
+
+        if is_public_workspace:
+            chunk_document = {
+                "id": chunk_id,
+                "document_id": document_id,
+                "chunk_id": str(page_number),
+                "chunk_text": enhanced_chunk_text,
+                "embedding": embedding,
+                "file_name": file_name,
+                "chunk_keywords": [],
+                "chunk_summary": "",
+                "page_number": page_number,
+                "author": [],
+                "title": "",
+                "document_classification": "None",
+                "document_tags": metadata.get('tags', []),
+                "chunk_sequence": page_number,
+                "upload_date": current_time,
+                "version": version,
+                "public_workspace_id": public_workspace_id
+            }
+        elif is_group:
+            shared_group_ids = metadata.get('shared_group_ids', []) if metadata else []
+            chunk_document = {
+                "id": chunk_id,
+                "document_id": document_id,
+                "chunk_id": str(page_number),
+                "chunk_text": enhanced_chunk_text,
+                "embedding": embedding,
+                "file_name": file_name,
+                "chunk_keywords": [],
+                "chunk_summary": "",
+                "page_number": page_number,
+                "author": [],
+                "title": "",
+                "document_classification": "None",
+                "document_tags": metadata.get('tags', []),
+                "chunk_sequence": page_number,
+                "upload_date": current_time,
+                "version": version,
+                "group_id": group_id,
+                "shared_group_ids": shared_group_ids
+            }
+        else:
+            shared_user_ids = metadata.get('shared_user_ids', []) if metadata else []
+            chunk_document = {
+                "id": chunk_id,
+                "document_id": document_id,
+                "chunk_id": str(page_number),
+                "chunk_text": enhanced_chunk_text,
+                "embedding": embedding,
+                "file_name": file_name,
+                "chunk_keywords": [],
+                "chunk_summary": "",
+                "page_number": page_number,
+                "author": [],
+                "title": "",
+                "document_classification": "None",
+                "document_tags": metadata.get('tags', []),
+                "chunk_sequence": page_number,
+                "upload_date": current_time,
+                "version": version,
+                "user_id": user_id,
+                "shared_user_ids": shared_user_ids
+            }
+
+        chunk_documents.append(chunk_document)
+
+    # Batch upload to AI Search
+    try:
+        if is_public_workspace:
+            search_client = CLIENTS["search_client_public"]
+        elif is_group:
+            search_client = CLIENTS["search_client_group"]
+        else:
+            search_client = CLIENTS["search_client_user"]
+
+        # Upload in sub-batches of 32 to avoid request size limits
+        upload_batch_size = 32
+        for i in range(0, len(chunk_documents), upload_batch_size):
+            sub_batch = chunk_documents[i:i + upload_batch_size]
+            search_client.upload_documents(documents=sub_batch)
+
+    except Exception as e:
+        log_event(f"[save_chunks_batch] Error uploading batch to AI Search for document {document_id}: {e}", level=logging.ERROR)
+        raise
+
+    return total_token_usage
+
 def get_document_metadata_for_citations(document_id, user_id=None, group_id=None, public_workspace_id=None):
     """
     Retrieve keywords and abstract from a document for creating metadata citations.
@@ -4669,37 +4854,30 @@ def process_single_tabular_sheet(df, document_id, user_id, file_name, update_cal
     # Consider accumulating page count in the caller if needed.
     update_callback(number_of_pages=num_chunks_final)
 
-    # Save chunks, prepending the header to each
+    # Save chunks, prepending the header to each — use batch processing for speed
+    all_chunks = []
     for idx, chunk_rows_content in enumerate(final_chunks_content, start=1):
-        # Prepend header - header length does not count towards chunk size limit
         chunk_with_header = header_string + chunk_rows_content
-
-        update_callback(
-            current_file_chunk=idx,
-            status=f"Saving chunk {idx}/{num_chunks_final} from {file_name}..."
-        )
-
-        args = {
+        all_chunks.append({
             "page_text_content": chunk_with_header,
             "page_number": idx,
-            "file_name": file_name,
-            "user_id": user_id,
-            "document_id": document_id
-        }
+            "file_name": file_name
+        })
 
-        if is_public_workspace:
-            args["public_workspace_id"] = public_workspace_id
-        elif is_group:
-            args["group_id"] = group_id
+    if all_chunks:
+        update_callback(
+            current_file_chunk=1,
+            status=f"Batch processing {num_chunks_final} chunks from {file_name}..."
+        )
 
-        token_usage = save_chunks(**args)
-        total_chunks_saved += 1
-        
-        # Accumulate embedding tokens
-        if token_usage:
-            total_embedding_tokens += token_usage.get('total_tokens', 0)
-            if not embedding_model_name:
-                embedding_model_name = token_usage.get('model_deployment_name')
+        batch_token_usage = save_chunks_batch(
+            all_chunks, user_id, document_id,
+            group_id=group_id, public_workspace_id=public_workspace_id
+        )
+        total_chunks_saved = len(all_chunks)
+        if batch_token_usage:
+            total_embedding_tokens = batch_token_usage.get('total_tokens', 0)
+            embedding_model_name = batch_token_usage.get('model_deployment_name')
 
     return total_chunks_saved, total_embedding_tokens, embedding_model_name
 
@@ -4729,63 +4907,93 @@ def process_tabular(document_id, user_id, temp_file_path, original_filename, fil
             args["group_id"] = group_id
 
         upload_to_blob(**args)
+        update_callback(enhanced_citations=True, status=f"Enhanced citations enabled for {file_ext}")
 
-    try:
-        if file_ext == '.csv':
-            # Process CSV
-             # Read CSV, attempt to infer header, keep data as string initially
-            df = pandas.read_csv(
-                temp_file_path, 
-                keep_default_na=False, 
-                dtype=str
-            )
-            args = {
-                "df": df,
-                "document_id": document_id,
-                "user_id": user_id,
+    # When enhanced citations is on, index a single schema summary chunk
+    # instead of row-by-row chunking. The tabular processing plugin handles analysis.
+    if enable_enhanced_citations:
+        try:
+            if file_ext == '.csv':
+                df_preview = pandas.read_csv(temp_file_path, keep_default_na=False, dtype=str, nrows=5)
+                full_df = pandas.read_csv(temp_file_path, keep_default_na=False, dtype=str)
+                row_count = len(full_df)
+                columns = [str(column) for column in df_preview.columns]
+                preview_rows = df_preview.head(5).to_string(index=False)
+
+                schema_summary = (
+                    f"Tabular data file: {original_filename}\n"
+                    f"Columns ({len(columns)}): {', '.join(columns)}\n"
+                    f"Total rows: {row_count}\n"
+                    f"Preview (first 5 rows):\n{preview_rows}\n\n"
+                    f"This file is available for detailed analysis via the Tabular Processing plugin."
+                )
+            elif file_ext in ('.xlsx', '.xls', '.xlsm'):
+                engine = 'openpyxl' if file_ext in ('.xlsx', '.xlsm') else 'xlrd'
+                excel_file = pandas.ExcelFile(temp_file_path, engine=engine)
+                workbook_sections = []
+
+                for sheet_name in excel_file.sheet_names:
+                    df_preview = excel_file.parse(sheet_name, keep_default_na=False, dtype=str, nrows=3)
+                    full_df = excel_file.parse(sheet_name, keep_default_na=False, dtype=str)
+                    columns = [str(column) for column in df_preview.columns]
+                    preview_rows = df_preview.head(3).to_string(index=False)
+                    workbook_sections.append(
+                        f"Sheet: {sheet_name}\n"
+                        f"Columns ({len(columns)}): {', '.join(columns)}\n"
+                        f"Total rows: {len(full_df)}\n"
+                        f"Preview (first 3 rows):\n{preview_rows}"
+                    )
+
+                schema_summary = (
+                    f"Tabular workbook: {original_filename}\n"
+                    f"Sheets ({len(excel_file.sheet_names)}): {', '.join(excel_file.sheet_names)}\n\n"
+                    + "\n\n".join(workbook_sections)
+                    + "\n\nThis workbook is available for detailed analysis via the Tabular Processing plugin."
+                )
+            else:
+                raise ValueError(f"Unsupported tabular file type: {file_ext}")
+
+            update_callback(number_of_pages=1, status=f"Indexing schema summary for {original_filename}...")
+
+            save_args = {
+                "page_text_content": schema_summary,
+                "page_number": 1,
                 "file_name": original_filename,
-                "update_callback": update_callback
+                "user_id": user_id,
+                "document_id": document_id
             }
-
             if is_public_workspace:
-                args["public_workspace_id"] = public_workspace_id
+                save_args["public_workspace_id"] = public_workspace_id
             elif is_group:
-                args["group_id"] = group_id
+                save_args["group_id"] = group_id
 
-            result = process_single_tabular_sheet(**args)
-            if isinstance(result, tuple) and len(result) == 3:
-                chunks, tokens, model = result
-                total_chunks_saved = chunks
-                total_embedding_tokens += tokens
-                if not embedding_model_name:
-                    embedding_model_name = model
-            else:
-                total_chunks_saved = result
-
-        elif file_ext in ('.xlsx', '.xls', '.xlsm'):
-            # Process Excel (potentially multiple sheets)
-            excel_file = pandas.ExcelFile(
-                temp_file_path, 
-                engine='openpyxl' if file_ext in ('.xlsx', '.xlsm') else 'xlrd'
-            )
-            sheet_names = excel_file.sheet_names
-            base_name, ext = os.path.splitext(original_filename)
-
-            accumulated_total_chunks = 0
-            for sheet_name in sheet_names:
-                update_callback(status=f"Processing sheet '{sheet_name}'...")
-                # Read specific sheet, get values (not formulas), keep data as string
-                # Note: pandas typically reads values, not formulas by default.
-                df = excel_file.parse(sheet_name, keep_default_na=False, dtype=str)
+            token_usage = save_chunks(**save_args)
+            total_chunks_saved = 1
+            if token_usage:
+                total_embedding_tokens = token_usage.get('total_tokens', 0)
+                embedding_model_name = token_usage.get('model_deployment_name')
 
-                # Create effective filename for this sheet
-                effective_filename = f"{base_name}-{sheet_name}{ext}" if len(sheet_names) > 1 else original_filename
+            # Don't return here — fall through to metadata extraction below
+        except Exception as e:
+            log_event(f"[process_tabular] Error creating schema summary, falling back to row-by-row: {e}", level=logging.WARNING)
+            # Fall through to existing row-by-row processing
 
+    # Only do row-by-row chunking if schema-only didn't produce chunks
+    if total_chunks_saved == 0:
+        try:
+            if file_ext == '.csv':
+                # Process CSV
+                # Read CSV, attempt to infer header, keep data as string initially
+                df = pandas.read_csv(
+                    temp_file_path,
+                    keep_default_na=False,
+                    dtype=str
+                )
                 args = {
                     "df": df,
                     "document_id": document_id,
                     "user_id": user_id,
-                    "file_name": effective_filename,
+                    "file_name": original_filename,
                     "update_callback": update_callback
                 }
 
@@ -4797,21 +5005,62 @@ def process_tabular(document_id, user_id, temp_file_path, original_filename, fil
                 result = process_single_tabular_sheet(**args)
                 if isinstance(result, tuple) and len(result) == 3:
                     chunks, tokens, model = result
-                    accumulated_total_chunks += chunks
+                    total_chunks_saved = chunks
                     total_embedding_tokens += tokens
                     if not embedding_model_name:
                         embedding_model_name = model
                 else:
-                    accumulated_total_chunks += result
+                    total_chunks_saved = result
 
-            total_chunks_saved = accumulated_total_chunks # Total across all sheets
+            elif file_ext in ('.xlsx', '.xls', '.xlsm'):
+                # Process Excel (potentially multiple sheets)
+                excel_file = pandas.ExcelFile(
+                    temp_file_path,
+                    engine='openpyxl' if file_ext in ('.xlsx', '.xlsm') else 'xlrd'
+                )
+                sheet_names = excel_file.sheet_names
+                base_name, ext = os.path.splitext(original_filename)
 
+                accumulated_total_chunks = 0
+                for sheet_name in sheet_names:
+                    update_callback(status=f"Processing sheet '{sheet_name}'...")
+                    # Read specific sheet, get values (not formulas), keep data as string
+                    # Note: pandas typically reads values, not formulas by default.
+                    df = excel_file.parse(sheet_name, keep_default_na=False, dtype=str)
 
-    except pandas.errors.EmptyDataError:
-        print(f"Warning: Tabular file or sheet is empty: {original_filename}")
-        update_callback(status=f"Warning: File/sheet is empty - {original_filename}", number_of_pages=0)
-    except Exception as e:
-        raise Exception(f"Failed processing Tabular file {original_filename}: {e}")
+                    # Create effective filename for this sheet
+                    effective_filename = f"{base_name}-{sheet_name}{ext}" if len(sheet_names) > 1 else original_filename
+
+                    args = {
+                        "df": df,
+                        "document_id": document_id,
+                        "user_id": user_id,
+                        "file_name": effective_filename,
+                        "update_callback": update_callback
+                    }
+
+                    if is_public_workspace:
+                        args["public_workspace_id"] = public_workspace_id
+                    elif is_group:
+                        args["group_id"] = group_id
+
+                    result = process_single_tabular_sheet(**args)
+                    if isinstance(result, tuple) and len(result) == 3:
+                        chunks, tokens, model = result
+                        accumulated_total_chunks += chunks
+                        total_embedding_tokens += tokens
+                        if not embedding_model_name:
+                            embedding_model_name = model
+                    else:
+                        accumulated_total_chunks += result
+
+                total_chunks_saved = accumulated_total_chunks # Total across all sheets
+
+        except pandas.errors.EmptyDataError:
+            log_event(f"[process_tabular] Warning: Tabular file or sheet is empty: {original_filename}", level=logging.WARNING)
+            update_callback(status=f"Warning: File/sheet is empty - {original_filename}", number_of_pages=0)
+        except Exception as e:
+            raise Exception(f"Failed processing Tabular file {original_filename}: {e}")
 
     # Extract metadata if enabled and chunks were processed
     settings = get_settings()
diff --git a/application/single_app/functions_global_actions.py b/application/single_app/functions_global_actions.py
index 91f0d9f9..6adea426 100644
--- a/application/single_app/functions_global_actions.py
+++ b/application/single_app/functions_global_actions.py
@@ -11,6 +11,7 @@
 import traceback
 from datetime import datetime
 from config import cosmos_global_actions_container
+from functions_authentication import get_current_user_id
 from functions_keyvault import keyvault_plugin_save_helper, keyvault_plugin_get_helper, keyvault_plugin_delete_helper, SecretReturnType
 
 def get_global_actions(return_type=SecretReturnType.TRIGGER):
@@ -60,24 +61,49 @@ def get_global_action(action_id, return_type=SecretReturnType.TRIGGER):
         return None
 
 
-def save_global_action(action_data):
+def save_global_action(action_data, user_id=None):
     """
     Save or update a global action.
     
     Args:
         action_data (dict): Action data to save
+        user_id (str, optional): The user ID of the person performing the action
         
     Returns:
         dict: Saved action data or None if failed
     """
     try:
+        if user_id is None:
+            user_id = get_current_user_id()
+        if not user_id:
+            user_id = "system"
+
         # Ensure required fields
         if 'id' not in action_data:
             action_data['id'] = str(uuid.uuid4())
         # Add metadata
         action_data['is_global'] = True
-        action_data['created_at'] = datetime.utcnow().isoformat()
-        action_data['updated_at'] = datetime.utcnow().isoformat()
+        now = datetime.utcnow().isoformat()
+
+        # Check if this is a new action or an update to preserve created_by/created_at
+        existing_action = None
+        try:
+            existing_action = cosmos_global_actions_container.read_item(
+                item=action_data['id'],
+                partition_key=action_data['id']
+            )
+        except Exception:
+            pass
+
+        if existing_action:
+            action_data['created_by'] = existing_action.get('created_by') or user_id
+            action_data['created_at'] = existing_action.get('created_at') or now
+        else:
+            action_data['created_by'] = user_id
+            action_data['created_at'] = now
+        action_data['modified_by'] = user_id
+        action_data['modified_at'] = now
+        action_data['updated_at'] = now
         print(f"💾 Saving global action: {action_data.get('name', 'Unknown')}")
         # Store secrets in Key Vault before upsert
         action_data = keyvault_plugin_save_helper(action_data, scope_value=action_data.get('id'), scope="global")
diff --git a/application/single_app/functions_global_agents.py b/application/single_app/functions_global_agents.py
index 5cf6a3d4..87976510 100644
--- a/application/single_app/functions_global_agents.py
+++ b/application/single_app/functions_global_agents.py
@@ -163,25 +163,46 @@ def get_global_agent(agent_id):
         return None
 
 
-def save_global_agent(agent_data):
+def save_global_agent(agent_data, user_id=None):
     """
     Save or update a global agent.
     
     Args:
         agent_data (dict): Agent data to save
+        user_id (str, optional): The user ID of the person performing the action
         
     Returns:
         dict: Saved agent data or None if failed
     """
     try:
-        user_id = get_current_user_id()
+        if user_id is None:
+            user_id = get_current_user_id()
         cleaned_agent = sanitize_agent_payload(agent_data)
         if 'id' not in cleaned_agent:
             cleaned_agent['id'] = str(uuid.uuid4())
         cleaned_agent['is_global'] = True
         cleaned_agent['is_group'] = False
-        cleaned_agent['created_at'] = datetime.utcnow().isoformat()
-        cleaned_agent['updated_at'] = datetime.utcnow().isoformat()
+        now = datetime.utcnow().isoformat()
+
+        # Check if this is a new agent or an update to preserve created_by/created_at
+        existing_agent = None
+        try:
+            existing_agent = cosmos_global_agents_container.read_item(
+                item=cleaned_agent['id'],
+                partition_key=cleaned_agent['id']
+            )
+        except Exception:
+            pass
+
+        if existing_agent:
+            cleaned_agent['created_by'] = existing_agent.get('created_by', user_id)
+            cleaned_agent['created_at'] = existing_agent.get('created_at', now)
+        else:
+            cleaned_agent['created_by'] = user_id
+            cleaned_agent['created_at'] = now
+        cleaned_agent['modified_by'] = user_id
+        cleaned_agent['modified_at'] = now
+        cleaned_agent['updated_at'] = now
         log_event(
             "Saving global agent.",
             extra={"agent_name": cleaned_agent.get('name', 'Unknown')},
diff --git a/application/single_app/functions_group_actions.py b/application/single_app/functions_group_actions.py
index bc6aa4ea..c0d264b1 100644
--- a/application/single_app/functions_group_actions.py
+++ b/application/single_app/functions_group_actions.py
@@ -82,14 +82,36 @@ def get_group_action(
     return _clean_action(action, group_id, return_type)
 
 
-def save_group_action(group_id: str, action_data: Dict[str, Any]) -> Dict[str, Any]:
+def save_group_action(group_id: str, action_data: Dict[str, Any], user_id: Optional[str] = None) -> Dict[str, Any]:
     """Create or update a group action entry."""
     payload = dict(action_data)
     action_id = payload.get("id") or str(uuid.uuid4())
 
     payload["id"] = action_id
     payload["group_id"] = group_id
-    payload["last_updated"] = datetime.utcnow().isoformat()
+    now = datetime.utcnow().isoformat()
+    payload["last_updated"] = now
+
+    # Track who created/modified this action
+    existing_action = None
+    try:
+        existing_action = cosmos_group_actions_container.read_item(
+            item=action_id,
+            partition_key=group_id,
+        )
+    except exceptions.CosmosResourceNotFoundError:
+        pass
+    except Exception:
+        pass
+
+    if existing_action:
+        payload["created_by"] = existing_action.get("created_by", user_id)
+        payload["created_at"] = existing_action.get("created_at", now)
+    else:
+        payload["created_by"] = user_id
+        payload["created_at"] = now
+    payload["modified_by"] = user_id
+    payload["modified_at"] = now
 
     payload.setdefault("name", "")
     payload.setdefault("displayName", payload.get("name", ""))
diff --git a/application/single_app/functions_group_agents.py b/application/single_app/functions_group_agents.py
index 8bf6f87c..7cbb8324 100644
--- a/application/single_app/functions_group_agents.py
+++ b/application/single_app/functions_group_agents.py
@@ -63,16 +63,38 @@ def get_group_agent(group_id: str, agent_id: str) -> Optional[Dict[str, Any]]:
         return None
 
 
-def save_group_agent(group_id: str, agent_data: Dict[str, Any]) -> Dict[str, Any]:
+def save_group_agent(group_id: str, agent_data: Dict[str, Any], user_id: Optional[str] = None) -> Dict[str, Any]:
     """Create or update a group agent entry."""
     payload = sanitize_agent_payload(agent_data)
     agent_id = payload.get("id") or str(uuid.uuid4())
     payload["id"] = agent_id
     payload["group_id"] = group_id
-    payload["last_updated"] = datetime.utcnow().isoformat()
+    now = datetime.utcnow().isoformat()
+    payload["last_updated"] = now
     payload["is_global"] = False
     payload["is_group"] = True
 
+    # Track who created/modified this agent
+    existing_agent = None
+    try:
+        existing_agent = cosmos_group_agents_container.read_item(
+            item=agent_id,
+            partition_key=group_id,
+        )
+    except exceptions.CosmosResourceNotFoundError:
+        pass
+    except Exception:
+        pass
+
+    if existing_agent:
+        payload["created_by"] = existing_agent.get("created_by", user_id)
+        payload["created_at"] = existing_agent.get("created_at", now)
+    else:
+        payload["created_by"] = user_id
+        payload["created_at"] = now
+    payload["modified_by"] = user_id
+    payload["modified_at"] = now
+
     # Required/defaulted fields
     payload.setdefault("name", "")
     payload.setdefault("display_name", payload.get("name", ""))
diff --git a/application/single_app/functions_personal_actions.py b/application/single_app/functions_personal_actions.py
index 6345438e..91d849f3 100644
--- a/application/single_app/functions_personal_actions.py
+++ b/application/single_app/functions_personal_actions.py
@@ -113,15 +113,26 @@ def save_personal_action(user_id, action_data):
             existing_action = get_personal_action(user_id, action_data['name'])
         
         # Preserve existing ID if updating, or generate new ID if creating
+        now = datetime.utcnow().isoformat()
         if existing_action:
-            # Update existing action - preserve the original ID
+            # Update existing action - preserve the original ID and creation tracking
             action_data['id'] = existing_action['id']
+            action_data['created_by'] = existing_action.get('created_by', user_id)
+            action_data['created_at'] = existing_action.get('created_at', now)
         elif 'id' not in action_data or not action_data['id']:
             # New action - generate UUID for ID
             action_data['id'] = str(uuid.uuid4())
-            
+            action_data['created_by'] = user_id
+            action_data['created_at'] = now
+        else:
+            # Has an ID but no existing action found - treat as new
+            action_data['created_by'] = user_id
+            action_data['created_at'] = now
+        action_data['modified_by'] = user_id
+        action_data['modified_at'] = now
+
         action_data['user_id'] = user_id
-        action_data['last_updated'] = datetime.utcnow().isoformat()
+        action_data['last_updated'] = now
         
         # Validate required fields
         required_fields = ['name', 'displayName', 'type', 'description']
diff --git a/application/single_app/functions_personal_agents.py b/application/single_app/functions_personal_agents.py
index a4a5e47d..3c6c275e 100644
--- a/application/single_app/functions_personal_agents.py
+++ b/application/single_app/functions_personal_agents.py
@@ -128,9 +128,33 @@ def save_personal_agent(user_id, agent_data):
             cleaned_agent.setdefault(field, '')
         if 'id' not in cleaned_agent:
             cleaned_agent['id'] = str(f"{user_id}_{cleaned_agent.get('name', 'default')}")
-            
+
+        # Check if this is a new agent or an update to preserve created_by/created_at
+        existing_agent = None
+        try:
+            existing_agent = cosmos_personal_agents_container.read_item(
+                item=cleaned_agent['id'],
+                partition_key=user_id
+            )
+        except exceptions.CosmosResourceNotFoundError:
+            pass
+        except Exception:
+            pass
+
+        now = datetime.utcnow().isoformat()
+        if existing_agent:
+            # Preserve original creation tracking
+            cleaned_agent['created_by'] = existing_agent.get('created_by', user_id)
+            cleaned_agent['created_at'] = existing_agent.get('created_at', now)
+        else:
+            # New agent
+            cleaned_agent['created_by'] = user_id
+            cleaned_agent['created_at'] = now
+        cleaned_agent['modified_by'] = user_id
+        cleaned_agent['modified_at'] = now
+
         cleaned_agent['user_id'] = user_id
-        cleaned_agent['last_updated'] = datetime.utcnow().isoformat()
+        cleaned_agent['last_updated'] = now
         cleaned_agent['is_global'] = False
         cleaned_agent['is_group'] = False
         
diff --git a/application/single_app/functions_settings.py b/application/single_app/functions_settings.py
index 8176939d..8b80774e 100644
--- a/application/single_app/functions_settings.py
+++ b/application/single_app/functions_settings.py
@@ -25,6 +25,7 @@ def get_settings(use_cosmos=False):
         'enable_text_plugin': True,
         'enable_default_embedding_model_plugin': False,
         'enable_fact_memory_plugin': True,
+        'enable_tabular_processing_plugin': False,
         'enable_multi_agent_orchestration': False,
         'max_rounds_per_agent': 1,
         'enable_semantic_kernel': False,
@@ -205,6 +206,9 @@ def get_settings(use_cosmos=False):
         'require_member_of_feedback_admin': False,
         'enable_conversation_archiving': False,
 
+        # Processing Thoughts
+        'enable_thoughts': False,
+
         # Search and Extract
         'azure_ai_search_endpoint': '',
         'azure_ai_search_key': '',
@@ -258,6 +262,7 @@ def get_settings(use_cosmos=False):
 
         # Other
         'max_file_size_mb': 150,
+        'tabular_preview_max_blob_size_mb': 200,
         'conversation_history_limit': 10,
         'default_system_prompt': '',
         'enable_file_processing_logs': True,
@@ -391,6 +396,9 @@ def update_settings(new_settings):
         # always fetch the latest settings doc, which includes your merges
         settings_item = get_settings()
         settings_item.update(new_settings)
+        # Dependency enforcement: tabular processing requires enhanced citations
+        if not settings_item.get('enable_enhanced_citations', False):
+            settings_item['enable_tabular_processing_plugin'] = False
         cosmos_settings_container.upsert_item(settings_item)
         cache_updater = getattr(app_settings_cache, "update_settings_cache", None)
         if callable(cache_updater):
diff --git a/application/single_app/functions_thoughts.py b/application/single_app/functions_thoughts.py
new file mode 100644
index 00000000..c6ffe9dd
--- /dev/null
+++ b/application/single_app/functions_thoughts.py
@@ -0,0 +1,256 @@
+# functions_thoughts.py
+
+import uuid
+import time
+from datetime import datetime, timezone
+from config import cosmos_thoughts_container, cosmos_archived_thoughts_container, cosmos_messages_container
+from functions_appinsights import log_event
+from functions_settings import get_settings
+
+
+class ThoughtTracker:
+    """Stateful per-request tracker that writes processing step records to Cosmos DB.
+
+    Each add_thought() call immediately upserts a document so that polling
+    clients can see partial progress before the final response is sent.
+
+    All Cosmos writes are wrapped in try/except so thought errors never
+    interrupt the chat processing flow.
+    """
+
+    def __init__(self, conversation_id, message_id, thread_id, user_id):
+        self.conversation_id = conversation_id
+        self.message_id = message_id
+        self.thread_id = thread_id
+        self.user_id = user_id
+        self.current_index = 0
+        settings = get_settings()
+        self.enabled = settings.get('enable_thoughts', False)
+
+    def add_thought(self, step_type, content, detail=None):
+        """Write a thought step to Cosmos immediately.
+
+        Args:
+            step_type: One of search, tabular_analysis, web_search,
+                       agent_tool_call, generation, content_safety.
+            content: Short human-readable description of the step.
+            detail: Optional technical detail (function names, params, etc.).
+
+        Returns:
+            The thought document id, or None if disabled/failed.
+        """
+        if not self.enabled:
+            return None
+
+        thought_id = str(uuid.uuid4())
+        thought_doc = {
+            'id': thought_id,
+            'conversation_id': self.conversation_id,
+            'message_id': self.message_id,
+            'thread_id': self.thread_id,
+            'user_id': self.user_id,
+            'step_index': self.current_index,
+            'step_type': step_type,
+            'content': content,
+            'detail': detail,
+            'duration_ms': None,
+            'timestamp': datetime.now(timezone.utc).isoformat()
+        }
+        self.current_index += 1
+
+        try:
+            cosmos_thoughts_container.upsert_item(thought_doc)
+        except Exception as e:
+            log_event(f"ThoughtTracker.add_thought failed: {e}", level="WARNING")
+            return None
+
+        return thought_id
+
+    def complete_thought(self, thought_id, duration_ms):
+        """Patch an existing thought with its duration after the step finishes."""
+        if not self.enabled or not thought_id:
+            return
+
+        try:
+            thought_doc = cosmos_thoughts_container.read_item(
+                item=thought_id,
+                partition_key=self.user_id
+            )
+            thought_doc['duration_ms'] = duration_ms
+            cosmos_thoughts_container.upsert_item(thought_doc)
+        except Exception as e:
+            log_event(f"ThoughtTracker.complete_thought failed: {e}", level="WARNING")
+
+    def timed_thought(self, step_type, content, detail=None):
+        """Convenience: add a thought and return a timer helper.
+
+        Usage:
+            timer = tracker.timed_thought('search', 'Searching documents...')
+            # ... do work ...
+            timer.stop()
+        """
+        start = time.time()
+        thought_id = self.add_thought(step_type, content, detail)
+        return _ThoughtTimer(self, thought_id, start)
+
+
+class _ThoughtTimer:
+    """Helper returned by ThoughtTracker.timed_thought() for auto-duration capture."""
+
+    def __init__(self, tracker, thought_id, start_time):
+        self._tracker = tracker
+        self._thought_id = thought_id
+        self._start = start_time
+
+    def stop(self):
+        elapsed_ms = int((time.time() - self._start) * 1000)
+        self._tracker.complete_thought(self._thought_id, elapsed_ms)
+        return elapsed_ms
+
+
+# ---------------------------------------------------------------------------
+# CRUD helpers
+# ---------------------------------------------------------------------------
+
+def get_thoughts_for_message(conversation_id, message_id, user_id):
+    """Return all thoughts for a specific assistant message, ordered by step_index."""
+    try:
+        query = (
+            "SELECT * FROM c "
+            "WHERE c.conversation_id = @conv_id "
+            "AND c.message_id = @msg_id "
+            "ORDER BY c.step_index ASC"
+        )
+        params = [
+            {"name": "@conv_id", "value": conversation_id},
+            {"name": "@msg_id", "value": message_id},
+        ]
+        results = list(cosmos_thoughts_container.query_items(
+            query=query,
+            parameters=params,
+            partition_key=user_id
+        ))
+        return results
+    except Exception as e:
+        log_event(f"get_thoughts_for_message failed: {e}", level="WARNING")
+        return []
+
+
+def get_pending_thoughts(conversation_id, user_id):
+    """Return the latest thoughts for a conversation that are still in-progress.
+
+    Used by the polling endpoint.  Retrieves thoughts created within the last
+    5 minutes for the conversation, grouped by the most recent message_id.
+    """
+    try:
+        five_minutes_ago = datetime.now(timezone.utc)
+        from datetime import timedelta
+        five_minutes_ago = (five_minutes_ago - timedelta(minutes=5)).isoformat()
+
+        query = (
+            "SELECT * FROM c "
+            "WHERE c.conversation_id = @conv_id "
+            "AND c.timestamp >= @since "
+            "ORDER BY c.timestamp DESC"
+        )
+        params = [
+            {"name": "@conv_id", "value": conversation_id},
+            {"name": "@since", "value": five_minutes_ago},
+        ]
+        results = list(cosmos_thoughts_container.query_items(
+            query=query,
+            parameters=params,
+            partition_key=user_id
+        ))
+
+        if not results:
+            return []
+
+        # Group by the most recent message_id
+        latest_message_id = results[0].get('message_id')
+        latest_thoughts = [
+            t for t in results if t.get('message_id') == latest_message_id
+        ]
+        # Return in ascending step_index order
+        latest_thoughts.sort(key=lambda t: t.get('step_index', 0))
+        return latest_thoughts
+    except Exception as e:
+        log_event(f"get_pending_thoughts failed: {e}", level="WARNING")
+        return []
+
+
+def get_thoughts_for_conversation(conversation_id, user_id):
+    """Return all thoughts for a conversation."""
+    try:
+        query = (
+            "SELECT * FROM c "
+            "WHERE c.conversation_id = @conv_id "
+            "ORDER BY c.timestamp ASC"
+        )
+        params = [
+            {"name": "@conv_id", "value": conversation_id},
+        ]
+        results = list(cosmos_thoughts_container.query_items(
+            query=query,
+            parameters=params,
+            partition_key=user_id
+        ))
+        return results
+    except Exception as e:
+        log_event(f"get_thoughts_for_conversation failed: {e}", level="WARNING")
+        return []
+
+
+def archive_thoughts_for_conversation(conversation_id, user_id):
+    """Copy all thoughts for a conversation to the archive container, then delete originals."""
+    try:
+        thoughts = get_thoughts_for_conversation(conversation_id, user_id)
+        for thought in thoughts:
+            archived = dict(thought)
+            archived['archived_at'] = datetime.now(timezone.utc).isoformat()
+            cosmos_archived_thoughts_container.upsert_item(archived)
+
+        for thought in thoughts:
+            cosmos_thoughts_container.delete_item(
+                item=thought['id'],
+                partition_key=user_id
+            )
+    except Exception as e:
+        log_event(f"archive_thoughts_for_conversation failed: {e}", level="WARNING")
+
+
+def delete_thoughts_for_conversation(conversation_id, user_id):
+    """Delete all thoughts for a conversation."""
+    try:
+        thoughts = get_thoughts_for_conversation(conversation_id, user_id)
+        for thought in thoughts:
+            cosmos_thoughts_container.delete_item(
+                item=thought['id'],
+                partition_key=user_id
+            )
+    except Exception as e:
+        log_event(f"delete_thoughts_for_conversation failed: {e}", level="WARNING")
+
+
+def delete_thoughts_for_message(message_id, user_id):
+    """Delete all thoughts associated with a specific assistant message."""
+    try:
+        query = (
+            "SELECT * FROM c "
+            "WHERE c.message_id = @msg_id"
+        )
+        params = [
+            {"name": "@msg_id", "value": message_id},
+        ]
+        results = list(cosmos_thoughts_container.query_items(
+            query=query,
+            parameters=params,
+            partition_key=user_id
+        ))
+        for thought in results:
+            cosmos_thoughts_container.delete_item(
+                item=thought['id'],
+                partition_key=user_id
+            )
+    except Exception as e:
+        log_event(f"delete_thoughts_for_message failed: {e}", level="WARNING")
diff --git a/application/single_app/route_backend_agents.py b/application/single_app/route_backend_agents.py
index 57097ee5..2f631af7 100644
--- a/application/single_app/route_backend_agents.py
+++ b/application/single_app/route_backend_agents.py
@@ -23,6 +23,11 @@
 from functions_appinsights import log_event
 from json_schema_validation import validate_agent
 from swagger_wrapper import swagger_route, get_auth_security
+from functions_activity_logging import (
+    log_agent_creation,
+    log_agent_update,
+    log_agent_deletion,
+)
 
 bpa = Blueprint('admin_agents', __name__)
 
@@ -147,6 +152,18 @@ def set_user_agents():
     for agent_name in agents_to_delete:
         delete_personal_agent(user_id, agent_name)
     
+    # Log individual agent activities
+    for agent in filtered_agents:
+        a_name = agent.get('name', '')
+        a_id = agent.get('id', '')
+        a_display = agent.get('display_name', a_name)
+        if a_name in current_agent_names:
+            log_agent_update(user_id=user_id, agent_id=a_id, agent_name=a_name, agent_display_name=a_display, scope='personal')
+        else:
+            log_agent_creation(user_id=user_id, agent_id=a_id, agent_name=a_name, agent_display_name=a_display, scope='personal')
+    for agent_name in agents_to_delete:
+        log_agent_deletion(user_id=user_id, agent_id=agent_name, agent_name=agent_name, scope='personal')
+
     log_event("User agents updated", extra={"user_id": user_id, "agents_count": len(filtered_agents)})
     return jsonify({'success': True})
 
@@ -175,6 +192,9 @@ def delete_user_agent(agent_name):
     # Delete from personal_agents container
     delete_personal_agent(user_id, agent_name)
     
+    # Log agent deletion activity
+    log_agent_deletion(user_id=user_id, agent_id=agent_to_delete.get('id', agent_name), agent_name=agent_name, scope='personal')
+
     # Check if there are any agents left and if they match global_selected_agent
     remaining_agents = get_personal_agents(user_id)
     if len(remaining_agents) > 0:
@@ -270,11 +290,12 @@ def create_group_agent_route():
         cleaned_payload.pop(key, None)
 
     try:
-        saved = save_group_agent(active_group, cleaned_payload)
+        saved = save_group_agent(active_group, cleaned_payload, user_id=user_id)
     except Exception as exc:
         debug_print('Failed to save group agent: %s', exc)
         return jsonify({'error': 'Unable to save agent'}), 500
 
+    log_agent_creation(user_id=user_id, agent_id=saved.get('id', ''), agent_name=saved.get('name', ''), agent_display_name=saved.get('display_name', ''), scope='group', group_id=active_group)
     return jsonify(saved), 201
 
 
@@ -325,11 +346,12 @@ def update_group_agent_route(agent_id):
         return jsonify({'error': str(exc)}), 400
 
     try:
-        saved = save_group_agent(active_group, cleaned_payload)
+        saved = save_group_agent(active_group, cleaned_payload, user_id=user_id)
     except Exception as exc:
         debug_print('Failed to update group agent %s: %s', agent_id, exc)
         return jsonify({'error': 'Unable to update agent'}), 500
 
+    log_agent_update(user_id=user_id, agent_id=agent_id, agent_name=saved.get('name', ''), agent_display_name=saved.get('display_name', ''), scope='group', group_id=active_group)
     return jsonify(saved), 200
 
 
@@ -360,6 +382,7 @@ def delete_group_agent_route(agent_id):
 
     if not removed:
         return jsonify({'error': 'Agent not found'}), 404
+    log_agent_deletion(user_id=user_id, agent_id=agent_id, agent_name=agent_id, scope='group', group_id=active_group)
     return jsonify({'message': 'Agent deleted'}), 200
 
 # User endpoint to set selected agent (new model, not legacy default_agent)
@@ -504,10 +527,11 @@ def add_agent():
                 cleaned_agent['id'] = '15b0c92a-741d-42ff-ba0b-367c7ee0c848'
         
         # Save to global agents container
-        result = save_global_agent(cleaned_agent)
+        result = save_global_agent(cleaned_agent, user_id=str(get_current_user_id()))
         if not result:
             return jsonify({'error': 'Failed to save agent.'}), 500
 
+        log_agent_creation(user_id=str(get_current_user_id()), agent_id=cleaned_agent.get('id', ''), agent_name=cleaned_agent.get('name', ''), agent_display_name=cleaned_agent.get('display_name', ''), scope='global')
         log_event("Agent added", extra={"action": "add", "agent": {k: v for k, v in cleaned_agent.items() if k != 'id'}, "user": str(get_current_user_id())})
         # --- HOT RELOAD TRIGGER ---
         setattr(builtins, "kernel_reload_needed", True)
@@ -615,10 +639,11 @@ def edit_agent(agent_name):
             return jsonify({'error': 'Agent not found.'}), 404
         
         # Save the updated agent
-        result = save_global_agent(cleaned_agent)
+        result = save_global_agent(cleaned_agent, user_id=str(get_current_user_id()))
         if not result:
             return jsonify({'error': 'Failed to save agent.'}), 500
 
+        log_agent_update(user_id=str(get_current_user_id()), agent_id=cleaned_agent.get('id', ''), agent_name=agent_name, agent_display_name=cleaned_agent.get('display_name', ''), scope='global')
         log_event(
             f"Agent {agent_name} edited",
             extra={
@@ -660,6 +685,7 @@ def delete_agent(agent_name):
         if not success:
             return jsonify({'error': 'Failed to delete agent.'}), 500
         
+        log_agent_deletion(user_id=str(get_current_user_id()), agent_id=agent_to_delete.get('id', ''), agent_name=agent_name, scope='global')
         log_event("Agent deleted", extra={"action": "delete", "agent_name": agent_name, "user": str(get_current_user_id())})
         # --- HOT RELOAD TRIGGER ---
         setattr(builtins, "kernel_reload_needed", True)
diff --git a/application/single_app/route_backend_chats.py b/application/single_app/route_backend_chats.py
index e452fed4..bf532385 100644
--- a/application/single_app/route_backend_chats.py
+++ b/application/single_app/route_backend_chats.py
@@ -13,6 +13,7 @@
 import asyncio, types
 import ast
 import json
+import os
 import re
 from typing import Any, Dict, List, Mapping, Optional
 from config import *
@@ -28,17 +29,803 @@
 from functions_activity_logging import log_chat_activity, log_conversation_creation, log_token_usage
 from flask import current_app
 from swagger_wrapper import swagger_route, get_auth_security
+from functions_thoughts import ThoughtTracker
+
+
+TABULAR_DISCOVERY_FUNCTION_NAMES = {
+    'list_tabular_files',
+    'describe_tabular_file',
+}
+TABULAR_ANALYSIS_FUNCTION_NAMES = {
+    'lookup_value',
+    'aggregate_column',
+    'filter_rows',
+    'query_tabular_data',
+    'group_by_aggregate',
+    'group_by_datetime_component',
+}
+TABULAR_THOUGHT_EXCLUDED_PARAMETER_NAMES = {
+    'user_id',
+    'conversation_id',
+    'group_id',
+    'public_workspace_id',
+}
 
 
 def get_kernel():
     return getattr(g, 'kernel', None) or getattr(builtins, 'kernel', None)
 
+
 def get_kernel_agents():
     g_agents = getattr(g, 'kernel_agents', None)
     builtins_agents = getattr(builtins, 'kernel_agents', None)
     log_event(f"[SKChat] get_kernel_agents - g.kernel_agents: {type(g_agents)} ({len(g_agents) if g_agents else 0} agents), builtins.kernel_agents: {type(builtins_agents)} ({len(builtins_agents) if builtins_agents else 0} agents)", level=logging.INFO)
     return g_agents or builtins_agents
 
+
+def get_new_plugin_invocations(invocations, baseline_count):
+    """Return only the plugin invocations created after the baseline count."""
+    if not invocations:
+        return []
+
+    if baseline_count <= 0:
+        return list(invocations)
+
+    if baseline_count >= len(invocations):
+        return []
+
+    return list(invocations[baseline_count:])
+
+
+def split_tabular_plugin_invocations(invocations):
+    """Split tabular plugin invocations into discovery and analytical categories."""
+    discovery_invocations = []
+    analytical_invocations = []
+    other_invocations = []
+
+    for invocation in invocations or []:
+        function_name = getattr(invocation, 'function_name', '')
+
+        if function_name in TABULAR_DISCOVERY_FUNCTION_NAMES:
+            discovery_invocations.append(invocation)
+        elif function_name in TABULAR_ANALYSIS_FUNCTION_NAMES:
+            analytical_invocations.append(invocation)
+        else:
+            other_invocations.append(invocation)
+
+    return discovery_invocations, analytical_invocations, other_invocations
+
+
+def get_tabular_invocation_result_payload(invocation):
+    """Parse a tabular invocation result payload when it is JSON-like."""
+    result = getattr(invocation, 'result', None)
+    if isinstance(result, dict):
+        return result
+    if not isinstance(result, str):
+        return None
+
+    try:
+        payload = json.loads(result)
+    except Exception:
+        return None
+
+    return payload if isinstance(payload, dict) else None
+
+
+def get_tabular_invocation_error_message(invocation):
+    """Return an error message for a tabular invocation, including JSON error payloads."""
+    explicit_error_message = getattr(invocation, 'error_message', None)
+    if explicit_error_message:
+        return str(explicit_error_message)
+
+    result_payload = get_tabular_invocation_result_payload(invocation)
+    if result_payload and result_payload.get('error'):
+        return str(result_payload['error'])
+
+    return None
+
+
+def split_tabular_analysis_invocations(invocations):
+    """Split analytical tabular invocations into successful and failed calls."""
+    successful_invocations = []
+    failed_invocations = []
+
+    for invocation in invocations or []:
+        function_name = getattr(invocation, 'function_name', '')
+        if function_name not in TABULAR_ANALYSIS_FUNCTION_NAMES:
+            continue
+
+        if get_tabular_invocation_error_message(invocation):
+            failed_invocations.append(invocation)
+        else:
+            successful_invocations.append(invocation)
+
+    return successful_invocations, failed_invocations
+
+
+def summarize_tabular_invocation_errors(invocations):
+    """Return a stable list of unique tabular tool error messages."""
+    unique_errors = []
+    seen_errors = set()
+
+    for invocation in invocations or []:
+        error_message = get_tabular_invocation_error_message(invocation)
+        if not error_message:
+            continue
+
+        normalized_error_message = error_message.strip()
+        if not normalized_error_message or normalized_error_message in seen_errors:
+            continue
+
+        seen_errors.add(normalized_error_message)
+        unique_errors.append(normalized_error_message)
+
+    return unique_errors
+
+
+def filter_tabular_citation_invocations(invocations):
+    """Hide discovery-only citation noise when analytical tabular calls exist."""
+    if not invocations:
+        return []
+
+    successful_analytical_invocations, _ = split_tabular_analysis_invocations(invocations)
+    if successful_analytical_invocations:
+        return successful_analytical_invocations
+
+    return []
+
+
+def format_tabular_thought_parameter_value(value):
+    """Render a concise parameter value for tabular thought details."""
+    if value is None:
+        return None
+
+    if isinstance(value, (dict, list, tuple)):
+        rendered_value = json.dumps(value, default=str)
+    else:
+        rendered_value = str(value)
+
+    if not rendered_value:
+        return None
+
+    if len(rendered_value) > 120:
+        rendered_value = rendered_value[:117] + '...'
+
+    return rendered_value
+
+
+def get_tabular_tool_thought_payloads(invocations):
+    """Convert tabular plugin invocations into user-visible thought payloads."""
+    thought_payloads = []
+
+    for invocation in invocations or []:
+        function_name = getattr(invocation, 'function_name', 'unknown_tool')
+        duration_ms = getattr(invocation, 'duration_ms', None)
+        error_message = get_tabular_invocation_error_message(invocation)
+        success = getattr(invocation, 'success', True) and not error_message
+        parameters = getattr(invocation, 'parameters', {}) or {}
+
+        filename = parameters.get('filename')
+        sheet_name = parameters.get('sheet_name')
+        duration_suffix = f" ({int(duration_ms)}ms)" if duration_ms else ""
+        content = f"Tabular tool {function_name}{duration_suffix}"
+        if filename:
+            content = f"Tabular tool {function_name} on {filename}{duration_suffix}"
+        if filename and sheet_name:
+            content = f"Tabular tool {function_name} on {filename} [{sheet_name}]{duration_suffix}"
+        if not success:
+            content = f"{content} failed"
+
+        detail_parts = []
+        for parameter_name, parameter_value in parameters.items():
+            if parameter_name in TABULAR_THOUGHT_EXCLUDED_PARAMETER_NAMES:
+                continue
+
+            rendered_value = format_tabular_thought_parameter_value(parameter_value)
+            if rendered_value is None:
+                continue
+
+            detail_parts.append(f"{parameter_name}={rendered_value}")
+
+        rendered_error_message = format_tabular_thought_parameter_value(error_message)
+        if rendered_error_message:
+            detail_parts.append(f"error={rendered_error_message}")
+
+        detail_parts.append(f"success={success}")
+        detail = "; ".join(detail_parts) if detail_parts else None
+        thought_payloads.append((content, detail))
+
+    return thought_payloads
+
+
+def get_tabular_status_thought_payloads(invocations, analysis_succeeded):
+    """Return additional tabular status thoughts for retries and fallbacks."""
+    successful_analytical_invocations, failed_analytical_invocations = split_tabular_analysis_invocations(invocations)
+    if not failed_analytical_invocations:
+        return []
+
+    error_messages = summarize_tabular_invocation_errors(failed_analytical_invocations)
+    detail = "; ".join(error_messages) if error_messages else None
+
+    if analysis_succeeded and successful_analytical_invocations:
+        return [(
+            "Tabular analysis recovered after retrying tool errors",
+            detail,
+        )]
+
+    if analysis_succeeded:
+        return [(
+            "Tabular analysis recovered via internal fallback after tool errors",
+            detail,
+        )]
+
+    return [(
+        "Tabular analysis encountered tool errors before fallback",
+        detail,
+    )]
+
+
+def _normalize_tabular_sheet_token(token):
+    """Normalize question and sheet-name tokens for lightweight matching."""
+    normalized = re.sub(r'[^a-z0-9]+', '', str(token or '').lower())
+    if len(normalized) > 4 and normalized.endswith('ies'):
+        return normalized[:-3] + 'y'
+    if len(normalized) > 3 and normalized.endswith('s') and not normalized.endswith('ss'):
+        return normalized[:-1]
+    return normalized
+
+
+def _tokenize_tabular_sheet_text(text):
+    """Tokenize free text into normalized sheet-matching tokens."""
+    tokens = []
+    for raw_token in re.split(r'[^a-z0-9]+', str(text or '').lower()):
+        normalized_token = _normalize_tabular_sheet_token(raw_token)
+        if normalized_token and len(normalized_token) > 1:
+            tokens.append(normalized_token)
+    return tokens
+
+
+def _select_likely_workbook_sheet(sheet_names, question_text):
+    """Return a likely sheet name when the user question strongly matches one sheet."""
+    question_tokens = set(_tokenize_tabular_sheet_text(question_text))
+    question_phrase = ' '.join(_tokenize_tabular_sheet_text(question_text))
+    best_sheet = None
+    best_score = 0
+    runner_up_score = 0
+
+    for sheet_name in sheet_names or []:
+        sheet_tokens = _tokenize_tabular_sheet_text(sheet_name)
+        if not sheet_tokens:
+            continue
+
+        sheet_phrase = ' '.join(sheet_tokens)
+        score = 0
+
+        if sheet_phrase and sheet_phrase in question_phrase:
+            score += 8
+
+        token_matches = sum(1 for token in sheet_tokens if token in question_tokens)
+        score += token_matches * 3
+
+        if len(sheet_tokens) == 1 and sheet_tokens[0] in question_tokens:
+            score += 4
+
+        if score > best_score:
+            runner_up_score = best_score
+            best_score = score
+            best_sheet = sheet_name
+        elif score > runner_up_score:
+            runner_up_score = score
+
+    if best_score <= 0 or best_score == runner_up_score:
+        return None
+
+    return best_sheet
+
+
+async def run_tabular_sk_analysis(user_question, tabular_filenames, user_id,
+                                   conversation_id, gpt_model, settings,
+                                   source_hint="workspace", group_id=None,
+                                   public_workspace_id=None):
+    """Run lightweight SK with TabularProcessingPlugin to analyze tabular data.
+
+    Creates a temporary Kernel with only the TabularProcessingPlugin, uses the
+    same chat model as the user's session, and returns computed analysis results.
+    Returns None on failure for graceful degradation.
+    """
+    from semantic_kernel import Kernel as SKKernel
+    from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+    from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+    from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import AzureChatPromptExecutionSettings
+    from semantic_kernel.contents.chat_history import ChatHistory as SKChatHistory
+    from semantic_kernel_plugins.tabular_processing_plugin import TabularProcessingPlugin
+
+    try:
+        plugin_logger = get_plugin_logger()
+        log_event(f"[Tabular SK Analysis] Starting analysis for files: {tabular_filenames}", level=logging.INFO)
+
+        # 1. Create lightweight kernel with only tabular plugin
+        kernel = SKKernel()
+        tabular_plugin = TabularProcessingPlugin()
+        kernel.add_plugin(tabular_plugin, plugin_name="tabular_processing")
+
+        # 2. Create chat service using same config as main chat
+        enable_gpt_apim = settings.get('enable_gpt_apim', False)
+        if enable_gpt_apim:
+            chat_service = AzureChatCompletion(
+                service_id="tabular-analysis",
+                deployment_name=gpt_model,
+                endpoint=settings.get('azure_apim_gpt_endpoint'),
+                api_key=settings.get('azure_apim_gpt_subscription_key'),
+                api_version=settings.get('azure_apim_gpt_api_version'),
+            )
+        else:
+            auth_type = settings.get('azure_openai_gpt_authentication_type')
+            if auth_type == 'managed_identity':
+                token_provider = get_bearer_token_provider(DefaultAzureCredential(), cognitive_services_scope)
+                chat_service = AzureChatCompletion(
+                    service_id="tabular-analysis",
+                    deployment_name=gpt_model,
+                    endpoint=settings.get('azure_openai_gpt_endpoint'),
+                    api_version=settings.get('azure_openai_gpt_api_version'),
+                    ad_token_provider=token_provider,
+                )
+            else:
+                chat_service = AzureChatCompletion(
+                    service_id="tabular-analysis",
+                    deployment_name=gpt_model,
+                    endpoint=settings.get('azure_openai_gpt_endpoint'),
+                    api_key=settings.get('azure_openai_gpt_key'),
+                    api_version=settings.get('azure_openai_gpt_api_version'),
+                )
+        kernel.add_service(chat_service)
+
+        # 3. Pre-dispatch: load file schemas to eliminate discovery LLM rounds
+        source_context = f"source='{source_hint}'"
+        if group_id:
+            source_context += f", group_id='{group_id}'"
+        if public_workspace_id:
+            source_context += f", public_workspace_id='{public_workspace_id}'"
+
+        schema_parts = []
+        workbook_sheet_hints = {}
+        analysis_function_filters = {
+            'included_functions': [
+                f"tabular_processing-{function_name}"
+                for function_name in sorted(TABULAR_ANALYSIS_FUNCTION_NAMES)
+            ]
+        }
+        for fname in tabular_filenames:
+            try:
+                container, blob_path = tabular_plugin._resolve_blob_location_with_fallback(
+                    user_id, conversation_id, fname, source_hint,
+                    group_id=group_id, public_workspace_id=public_workspace_id
+                )
+                schema_info = tabular_plugin._build_workbook_schema_summary(
+                    container,
+                    blob_path,
+                    fname,
+                    preview_rows=2,
+                )
+
+                if schema_info.get('is_workbook') and schema_info.get('sheet_count', 0) > 1:
+                    # Build a compact sheet directory so the model can pick the
+                    # relevant sheet itself instead of us guessing.
+                    per_sheet = schema_info.get('per_sheet_schemas', {})
+                    likely_sheet = _select_likely_workbook_sheet(
+                        schema_info.get('sheet_names', []),
+                        user_question,
+                    )
+                    if likely_sheet:
+                        workbook_sheet_hints[fname] = likely_sheet
+                        tabular_plugin.set_default_sheet(container, blob_path, likely_sheet)
+
+                    sheet_directory = []
+                    for sname in schema_info.get('sheet_names', []):
+                        sheet_info = per_sheet.get(sname, {})
+                        sheet_directory.append({
+                            'sheet_name': sname,
+                            'row_count': sheet_info.get('row_count', 0),
+                            'columns': sheet_info.get('columns', []),
+                        })
+                    directory_schema = {
+                        'filename': fname,
+                        'is_workbook': True,
+                        'sheet_count': schema_info.get('sheet_count', 0),
+                        'likely_sheet': likely_sheet,
+                        'sheet_directory': sheet_directory,
+                    }
+                    schema_parts.append(json.dumps(directory_schema, indent=2, default=str))
+                    log_event(
+                        f"[Tabular SK Analysis] Pre-loaded workbook {fname} directory "
+                        f"({schema_info.get('sheet_count', 0)} sheets available)"
+                        + (f"; likely sheet '{likely_sheet}'" if likely_sheet else ''),
+                        level=logging.DEBUG,
+                    )
+                else:
+                    schema_parts.append(json.dumps(schema_info, indent=2, default=str))
+                    if schema_info.get('is_workbook'):
+                        # Single-sheet workbook — set default so the model needs no sheet arg
+                        single_sheet = (schema_info.get('sheet_names') or [None])[0]
+                        if single_sheet:
+                            tabular_plugin.set_default_sheet(container, blob_path, single_sheet)
+                    df = tabular_plugin._read_tabular_blob_to_dataframe(container, blob_path)
+                    log_event(f"[Tabular SK Analysis] Pre-loaded schema for {fname} ({len(df)} rows)", level=logging.DEBUG)
+            except Exception as e:
+                log_event(f"[Tabular SK Analysis] Failed to pre-load schema for {fname}: {e}", level=logging.WARNING)
+                schema_parts.append(json.dumps({"filename": fname, "error": f"Could not pre-load: {str(e)}"}))
+
+        schema_context = "\n".join(schema_parts)
+
+        def build_system_prompt(force_tool_use=False, tool_error_messages=None):
+            retry_prefix = ""
+            if force_tool_use:
+                retry_prefix = (
+                    "RETRY MODE: Your previous attempt did not execute a usable analytical tool call. "
+                    "You MUST call one or more analytical tabular_processing plugin functions before writing any answer text. "
+                    "Do not say the analysis still needs to be run — run it now.\n\n"
+                )
+
+            tool_error_feedback = ""
+            if tool_error_messages:
+                rendered_errors = "\n".join(
+                    f"- {error_message}" for error_message in tool_error_messages
+                )
+                tool_error_feedback = (
+                    "PREVIOUS TOOL ERRORS:\n"
+                    f"{rendered_errors}\n"
+                    "Correct the function arguments and try again. If the operation is not 'count', provide an aggregate_column.\n\n"
+                )
+
+            missing_sheet_feedback = ""
+            if tool_error_messages and any(
+                'Specify sheet_name or sheet_index on analytical calls.' in error_message
+                for error_message in tool_error_messages
+            ):
+                guidance_lines = [
+                    "MULTI-SHEET RETRY: Your previous analytical call omitted sheet_name on a multi-sheet workbook.",
+                    "Retry immediately with sheet_name set to the most relevant worksheet from sheet_directory.",
+                    "For account/category lookup questions by month, use filter_rows or query_tabular_data on the label column first, then read the requested month column.",
+                    "Do not aggregate an entire month column unless the user explicitly asked for a total, sum, average, min, max, or count.",
+                ]
+                for workbook_name, hinted_sheet in workbook_sheet_hints.items():
+                    guidance_lines.append(
+                        f"Likely worksheet for {workbook_name} based on the question text: {hinted_sheet}."
+                    )
+                missing_sheet_feedback = "\n".join(guidance_lines) + "\n\n"
+
+            sheet_hint_feedback = ""
+            if workbook_sheet_hints:
+                rendered_hints = "\n".join(
+                    f"- {workbook_name}: likely worksheet '{hinted_sheet}'"
+                    for workbook_name, hinted_sheet in workbook_sheet_hints.items()
+                )
+                sheet_hint_feedback = (
+                    "LIKELY WORKSHEET HINTS:\n"
+                    f"{rendered_hints}\n"
+                    "Use the likely worksheet unless the question clearly refers to a different sheet.\n\n"
+                )
+
+            return (
+                "You are a data analyst. The full dataset is available through the "
+                "tabular_processing plugin functions. You MUST use one or more "
+                "tabular_processing plugin functions before answering. Never answer from "
+                "the schema preview alone. Never say that you would need to run the "
+                "analysis later — run it now.\n\n"
+                f"{retry_prefix}"
+                f"{tool_error_feedback}"
+                f"{sheet_hint_feedback}"
+                f"{missing_sheet_feedback}"
+                f"FILE SCHEMAS:\n"
+                f"{schema_context}\n\n"
+                "AVAILABLE FUNCTIONS: lookup_value, aggregate_column, filter_rows, query_tabular_data, "
+                "group_by_aggregate, and group_by_datetime_component for year/quarter/month/week/day/hour trend analysis.\n\n"
+                "Discovery functions are not available in this analysis run because schema context is already pre-loaded.\n\n"
+                "IMPORTANT:\n"
+                "1. Use the pre-loaded schema to pick the correct columns, then call the plugin functions.\n"
+                "2. For multi-sheet workbooks, review the sheet_directory to find the most relevant sheet for the question. Pass sheet_name='<name>' in every analytical tool call unless the likely_sheet already matches the question.\n"
+                "3. For account/category lookup questions at a specific period or metric, use lookup_value first. Provide lookup_column, lookup_value, and target_column.\n"
+                "4. If lookup_value is not sufficient, use filter_rows or query_tabular_data on the label column, then read the requested period column.\n"
+                "5. Only use aggregate_column when the user explicitly asks for a sum, average, min, max, or count across rows.\n"
+                "6. For time-based questions on datetime columns, use group_by_datetime_component.\n"
+                "7. For threshold, ranking, comparison, or correlation-like questions, first filter/query the relevant rows, then compute grouped metrics.\n"
+                "8. Batch multiple independent function calls in a SINGLE response whenever possible.\n"
+                "9. If you need more than 100 filtered rows, explicitly set max_rows higher.\n"
+                "10. For analytical questions, prefer lookup/filter/query plus aggregate/grouped computations over raw row or preview output.\n"
+                "11. For peak, busiest, highest, or lowest questions, use grouped functions and inspect the highest_group, highest_value, lowest_group, and lowest_value summary fields.\n"
+                "12. Return only computed findings and name the strongest drivers clearly.\n"
+                "13. Do not mention hypothetical follow-up analyses, parser errors, or failed attempts unless the user explicitly asked about failures and you have actual tool error output to report."
+            )
+
+        baseline_invocations = plugin_logger.get_invocations_for_conversation(
+            user_id,
+            conversation_id,
+            limit=1000
+        )
+        baseline_invocation_count = len(baseline_invocations)
+        previous_tool_error_messages = []
+
+        for attempt_number in range(1, 4):
+            force_tool_use = attempt_number > 1
+            # 4. Build chat history with pre-loaded schemas
+            chat_history = SKChatHistory()
+            chat_history.add_system_message(build_system_prompt(
+                force_tool_use=force_tool_use,
+                tool_error_messages=previous_tool_error_messages,
+            ))
+
+            chat_history.add_user_message(
+                f"Analyze the tabular data to answer: {user_question}\n"
+                f"Use user_id='{user_id}', conversation_id='{conversation_id}', {source_context}."
+            )
+
+            # 5. Execute with auto function calling
+            execution_settings = AzureChatPromptExecutionSettings(
+                service_id="tabular-analysis",
+                function_choice_behavior=(
+                    FunctionChoiceBehavior.Required(
+                        maximum_auto_invoke_attempts=8,
+                        filters=analysis_function_filters,
+                    )
+                    if force_tool_use else
+                    FunctionChoiceBehavior.Auto(
+                        maximum_auto_invoke_attempts=7,
+                        filters=analysis_function_filters,
+                    )
+                ),
+            )
+
+            result = await chat_service.get_chat_message_contents(
+                chat_history, execution_settings, kernel=kernel
+            )
+
+            invocations_after = plugin_logger.get_invocations_for_conversation(
+                user_id,
+                conversation_id,
+                limit=1000
+            )
+            new_invocations = get_new_plugin_invocations(invocations_after, baseline_invocation_count)
+            new_invocation_count = len(new_invocations)
+            discovery_invocations, analytical_invocations, _ = split_tabular_plugin_invocations(new_invocations)
+            successful_analytical_invocations, failed_analytical_invocations = split_tabular_analysis_invocations(new_invocations)
+
+            if result and result[0].content:
+                analysis = result[0].content.strip()
+                if len(analysis) > 20000:
+                    analysis = analysis[:20000] + "\n[Analysis truncated]"
+
+                if successful_analytical_invocations:
+                    log_event(
+                        f"[Tabular SK Analysis] Analysis complete via {len(successful_analytical_invocations)} analytical tool call(s) on attempt {attempt_number}",
+                        level=logging.INFO
+                    )
+                    return analysis
+
+                if failed_analytical_invocations:
+                    previous_tool_error_messages = summarize_tabular_invocation_errors(failed_analytical_invocations)
+                    log_event(
+                        f"[Tabular SK Analysis] Attempt {attempt_number} used analytical tool(s) but all returned errors; retrying",
+                        extra={
+                            'tool_errors': previous_tool_error_messages,
+                            'failed_tool_count': len(failed_analytical_invocations),
+                        },
+                        level=logging.WARNING
+                    )
+                elif analytical_invocations:
+                    log_event(
+                        f"[Tabular SK Analysis] Attempt {attempt_number} used analytical tool(s) without usable computed results; retrying",
+                        level=logging.WARNING
+                    )
+                elif discovery_invocations:
+                    discovery_function_names = sorted({
+                        invocation.function_name for invocation in discovery_invocations
+                    })
+                    log_event(
+                        f"[Tabular SK Analysis] Attempt {attempt_number} used only discovery tool(s) {discovery_function_names}; retrying",
+                        level=logging.WARNING
+                    )
+                elif new_invocation_count > 0:
+                    log_event(
+                        f"[Tabular SK Analysis] Attempt {attempt_number} used unsupported tool(s) without computed analysis; retrying",
+                        level=logging.WARNING
+                    )
+                else:
+                    log_event(
+                        f"[Tabular SK Analysis] Attempt {attempt_number} returned narrative without tool use; retrying",
+                        level=logging.WARNING
+                    )
+
+            else:
+                if failed_analytical_invocations:
+                    previous_tool_error_messages = summarize_tabular_invocation_errors(failed_analytical_invocations)
+                    log_event(
+                        f"[Tabular SK Analysis] Attempt {attempt_number} returned no content after tool errors; retrying",
+                        extra={
+                            'tool_errors': previous_tool_error_messages,
+                            'failed_tool_count': len(failed_analytical_invocations),
+                        },
+                        level=logging.WARNING
+                    )
+                else:
+                    log_event(
+                        f"[Tabular SK Analysis] Attempt {attempt_number} returned no content",
+                        level=logging.WARNING
+                    )
+
+            baseline_invocation_count = len(invocations_after)
+
+        log_event("[Tabular SK Analysis] Unable to obtain computed tool-backed results", level=logging.WARNING)
+        return None
+
+    except Exception as e:
+        log_event(f"[Tabular SK Analysis] Error: {e}", level=logging.WARNING, exceptionTraceback=True)
+        return None
+
+def collect_tabular_sk_citations(user_id, conversation_id):
+    """Collect plugin invocations from the tabular SK analysis and convert to citation format."""
+    from semantic_kernel_plugins.plugin_invocation_logger import get_plugin_logger
+
+    plugin_logger = get_plugin_logger()
+    plugin_invocations = plugin_logger.get_invocations_for_conversation(user_id, conversation_id)
+    plugin_invocations = filter_tabular_citation_invocations(plugin_invocations)
+
+    if not plugin_invocations:
+        return []
+
+    def make_json_serializable(obj):
+        if obj is None:
+            return None
+        elif isinstance(obj, (str, int, float, bool)):
+            return obj
+        elif isinstance(obj, dict):
+            return {str(k): make_json_serializable(v) for k, v in obj.items()}
+        elif isinstance(obj, (list, tuple)):
+            return [make_json_serializable(item) for item in obj]
+        else:
+            return str(obj)
+
+    citations = []
+    for inv in plugin_invocations:
+        timestamp_str = None
+        if inv.timestamp:
+            if hasattr(inv.timestamp, 'isoformat'):
+                timestamp_str = inv.timestamp.isoformat()
+            else:
+                timestamp_str = str(inv.timestamp)
+
+        parameters = getattr(inv, 'parameters', {}) or {}
+        sheet_name = parameters.get('sheet_name')
+        sheet_index = parameters.get('sheet_index')
+        tool_name = f"{inv.plugin_name}.{inv.function_name}"
+        if sheet_name:
+            tool_name = f"{tool_name} [{sheet_name}]"
+        elif sheet_index not in (None, ''):
+            tool_name = f"{tool_name} [sheet #{sheet_index}]"
+
+        citation = {
+            'tool_name': tool_name,
+            'function_name': inv.function_name,
+            'plugin_name': inv.plugin_name,
+            'function_arguments': make_json_serializable(parameters),
+            'function_result': make_json_serializable(inv.result),
+            'duration_ms': inv.duration_ms,
+            'timestamp': timestamp_str,
+            'success': inv.success,
+            'error_message': make_json_serializable(inv.error_message),
+            'user_id': inv.user_id,
+            'sheet_name': sheet_name,
+            'sheet_index': sheet_index,
+        }
+        citations.append(citation)
+
+    log_event(f"[Tabular SK Citations] Collected {len(citations)} tool execution citations", level=logging.INFO)
+    return citations
+
+
+def is_tabular_filename(filename):
+    """Return True when the filename has a supported tabular extension."""
+    if not filename or not isinstance(filename, str):
+        return False
+
+    _, extension = os.path.splitext(filename.strip().lower())
+    return extension.lstrip('.') in TABULAR_EXTENSIONS
+
+
+def get_citation_location(file_name, page_number=None, chunk_text=None, sheet_name=None):
+    """Return a display label/value pair for a citation location."""
+    if sheet_name:
+        return 'Sheet', str(sheet_name)
+
+    normalized_chunk_text = (chunk_text or '').strip()
+    if is_tabular_filename(file_name) and (
+        normalized_chunk_text.startswith('Tabular workbook:')
+        or normalized_chunk_text.startswith('Tabular data file:')
+    ):
+        return 'Location', 'Workbook Schema'
+
+    return 'Page', str(page_number or 1)
+
+
+def get_document_container_for_scope(document_scope):
+    """Return the Cosmos documents container that matches the workspace scope."""
+    if document_scope == 'group':
+        return cosmos_group_documents_container
+    if document_scope == 'public':
+        return cosmos_public_documents_container
+    return cosmos_user_documents_container
+
+
+def get_selected_workspace_tabular_filenames(selected_document_ids=None, selected_document_id=None, document_scope='personal'):
+    """Resolve explicitly selected workspace documents and return tabular filenames."""
+    selected_ids = list(selected_document_ids or [])
+    if not selected_ids and selected_document_id and selected_document_id != 'all':
+        selected_ids = [selected_document_id]
+
+    if not selected_ids:
+        return set()
+
+    cosmos_container = get_document_container_for_scope(document_scope)
+    tabular_filenames = set()
+
+    for doc_id in selected_ids:
+        if not doc_id or doc_id == 'all':
+            continue
+
+        try:
+            doc_query = (
+                "SELECT TOP 1 c.file_name, c.title "
+                "FROM c WHERE c.id = @doc_id "
+                "ORDER BY c.version DESC"
+            )
+            doc_params = [{"name": "@doc_id", "value": doc_id}]
+            doc_results = list(cosmos_container.query_items(
+                query=doc_query,
+                parameters=doc_params,
+                enable_cross_partition_query=True
+            ))
+
+            if not doc_results:
+                continue
+
+            file_name = doc_results[0].get('file_name') or doc_results[0].get('title')
+            if is_tabular_filename(file_name):
+                tabular_filenames.add(file_name)
+        except Exception as e:
+            log_event(
+                f"[Tabular SK Analysis] Failed to resolve selected document '{doc_id}': {e}",
+                level=logging.WARNING
+            )
+
+    return tabular_filenames
+
+
+def collect_workspace_tabular_filenames(combined_documents=None, selected_document_ids=None,
+                                        selected_document_id=None, document_scope='personal'):
+    """Collect tabular filenames from search results and explicit workspace selection."""
+    tabular_filenames = set()
+
+    for source_doc in combined_documents or []:
+        file_name = source_doc.get('file_name', '')
+        if is_tabular_filename(file_name):
+            tabular_filenames.add(file_name)
+
+    tabular_filenames.update(get_selected_workspace_tabular_filenames(
+        selected_document_ids=selected_document_ids,
+        selected_document_id=selected_document_id,
+        document_scope=document_scope,
+    ))
+
+    return tabular_filenames
+
+
+def determine_tabular_source_hint(document_scope, active_group_id=None, active_public_workspace_id=None):
+    """Map workspace scope metadata to the tabular plugin source hint."""
+    if document_scope == 'group' and active_group_id:
+        return 'group'
+    if document_scope == 'public' and active_public_workspace_id:
+        return 'public'
+    return 'workspace'
+
 def register_route_backend_chats(app):
     @app.route('/api/chat', methods=['POST'])
     @swagger_route(security=get_auth_security())
@@ -46,6 +833,7 @@ def register_route_backend_chats(app):
     @user_required
     def chat_api():
         try:
+            request_start_time = time.time()
             settings = get_settings()
             data = request.get_json()
             user_id = get_current_user_id()
@@ -668,6 +1456,18 @@ def result_requires_message_reload(result: Any) -> bool:
 
                 conversation_item['last_updated'] = datetime.utcnow().isoformat()
                 cosmos_conversations_container.upsert_item(conversation_item) # Update timestamp and potentially title
+
+                # Generate assistant_message_id early for thought tracking
+                assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}"
+
+                # Initialize thought tracker
+                thought_tracker = ThoughtTracker(
+                    conversation_id=conversation_id,
+                    message_id=assistant_message_id,
+                    thread_id=current_user_thread_id,
+                    user_id=user_id
+                )
+
         # region 3 - Content Safety
             # ---------------------------------------------------------------------
             # 3) Check Content Safety (but DO NOT return 403).
@@ -679,6 +1479,7 @@ def result_requires_message_reload(result: Any) -> bool:
             blocklist_matches = []
 
             if settings.get('enable_content_safety') and "content_safety_client" in CLIENTS:
+                thought_tracker.add_thought('content_safety', 'Checking content safety...')
                 try:
                     content_safety_client = CLIENTS["content_safety_client"]
                     request_obj = AnalyzeTextOptions(text=user_message)
@@ -836,6 +1637,7 @@ def result_requires_message_reload(result: Any) -> bool:
 
 
                 # Perform the search
+                thought_tracker.add_thought('search', f"Searching {document_scope or 'personal'} workspace documents for '{(search_query or user_message)[:50]}'")
                 try:
                     # Prepare search arguments
                     # Set default and maximum values for top_n
@@ -898,9 +1700,11 @@ def result_requires_message_reload(result: Any) -> bool:
                         'error': 'There was an issue with the embedding process. Please check with an admin on embedding configuration.'
                     }), 500
 
+                combined_documents = []
                 if search_results:
+                    unique_doc_names = set(doc.get('file_name', 'Unknown') for doc in search_results)
+                    thought_tracker.add_thought('search', f"Found {len(search_results)} results from {len(unique_doc_names)} documents")
                     retrieved_texts = []
-                    combined_documents = []
                     classifications_found = set(conversation_item.get('classification', [])) # Load existing
 
                     for doc in search_results:
@@ -915,13 +1719,23 @@ def result_requires_message_reload(result: Any) -> bool:
                         chunk_id = doc.get('chunk_id', str(uuid.uuid4())) # Ensure ID exists
                         score = doc.get('score', 0.0) # Add default score
                         group_id = doc.get('group_id', None) # Add default group ID
+                        sheet_name = doc.get('sheet_name')
+                        location_label, location_value = get_citation_location(
+                            file_name,
+                            page_number=page_number,
+                            chunk_text=chunk_text,
+                            sheet_name=sheet_name,
+                        )
 
-                        citation = f"(Source: {file_name}, Page: {page_number}) [#{citation_id}]"
+                        citation = f"(Source: {file_name}, {location_label}: {location_value}) [#{citation_id}]"
                         retrieved_texts.append(f"{chunk_text}\n{citation}")
                         combined_documents.append({
                             "file_name": file_name, 
                             "citation_id": citation_id, 
                             "page_number": page_number,
+                            "sheet_name": sheet_name,
+                            "location_label": location_label,
+                            "location_value": location_value,
                             "version": version, 
                             "classification": classification, 
                             "chunk_text": chunk_text,
@@ -1138,8 +1952,8 @@ def result_requires_message_reload(result: Any) -> bool:
                                 """
                             # Update the system message with enhanced content and updated documents array
                             if system_messages_for_augmentation:
-                                system_messages_for_augmentation[-1]['content'] = system_prompt_search
-                                system_messages_for_augmentation[-1]['documents'] = combined_documents
+                                system_messages_for_augmentation[0]['content'] = system_prompt_search
+                                system_messages_for_augmentation[0]['documents'] = combined_documents
                     # --- END NEW METADATA CITATIONS ---
 
                     # Update conversation classifications if new ones were found
@@ -1488,7 +2302,90 @@ def result_requires_message_reload(result: Any) -> bool:
                         'error': user_friendly_message
                     }), status_code
 
+            workspace_tabular_files = set()
+            if hybrid_search_enabled and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+                workspace_tabular_files = collect_workspace_tabular_filenames(
+                    combined_documents=combined_documents,
+                    selected_document_ids=selected_document_ids,
+                    selected_document_id=selected_document_id,
+                    document_scope=document_scope,
+                )
+
+            if hybrid_search_enabled and workspace_tabular_files and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+                tabular_source_hint = determine_tabular_source_hint(
+                    document_scope,
+                    active_group_id=active_group_id,
+                    active_public_workspace_id=active_public_workspace_id,
+                )
+                tabular_filenames_str = ", ".join(sorted(workspace_tabular_files))
+                plugin_logger = get_plugin_logger()
+                baseline_tabular_invocation_count = len(
+                    plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000)
+                )
+
+                tabular_analysis = asyncio.run(run_tabular_sk_analysis(
+                    user_question=user_message,
+                    tabular_filenames=workspace_tabular_files,
+                    user_id=user_id,
+                    conversation_id=conversation_id,
+                    gpt_model=gpt_model,
+                    settings=settings,
+                    source_hint=tabular_source_hint,
+                    group_id=active_group_id if tabular_source_hint == 'group' else None,
+                    public_workspace_id=active_public_workspace_id if tabular_source_hint == 'public' else None,
+                ))
+                tabular_invocations = get_new_plugin_invocations(
+                    plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000),
+                    baseline_tabular_invocation_count
+                )
+                tabular_thought_payloads = get_tabular_tool_thought_payloads(tabular_invocations)
+                for thought_content, thought_detail in tabular_thought_payloads:
+                    thought_tracker.add_thought('tabular_analysis', thought_content, thought_detail)
+                tabular_status_thought_payloads = get_tabular_status_thought_payloads(
+                    tabular_invocations,
+                    analysis_succeeded=bool(tabular_analysis),
+                )
+                for thought_content, thought_detail in tabular_status_thought_payloads:
+                    thought_tracker.add_thought('tabular_analysis', thought_content, thought_detail)
+
+                if tabular_analysis:
+                    tabular_system_msg = (
+                        f"The following analysis was computed from the tabular file(s) "
+                        f"{tabular_filenames_str} using data analysis functions:\n\n"
+                        f"{tabular_analysis}\n\n"
+                        f"Use these computed results to answer the user's question accurately."
+                    )
+                else:
+                    tabular_system_msg = (
+                        f"IMPORTANT: The selected workspace tabular file(s) are {tabular_filenames_str}. "
+                        f"The search results contain only a schema summary (column names and a few sample rows), NOT the full data. "
+                        f"You MUST use the tabular_processing plugin functions to answer ANY question about these files. "
+                        f"Do NOT attempt to answer using the schema summary alone — it is incomplete. "
+                        f"Available functions: describe_tabular_file, aggregate_column, filter_rows, query_tabular_data, group_by_aggregate, group_by_datetime_component. "
+                        f"Use source='{tabular_source_hint}'"
+                        + (f" and group_id='{active_group_id}'" if tabular_source_hint == 'group' else "")
+                        + (f" and public_workspace_id='{active_public_workspace_id}'" if tabular_source_hint == 'public' else "")
+                        + "."
+                    )
+
+                system_messages_for_augmentation.append({
+                    'role': 'system',
+                    'content': tabular_system_msg
+                })
+
+                if tabular_analysis:
+                    tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id)
+                    if tabular_sk_citations:
+                        agent_citations_list.extend(tabular_sk_citations)
+                else:
+                    thought_tracker.add_thought(
+                        'tabular_analysis',
+                        "Tabular analysis could not compute results; using schema context instead",
+                        detail=f"files={tabular_filenames_str}"
+                    )
+
             if web_search_enabled:
+                thought_tracker.add_thought('web_search', f"Searching the web for '{(search_query or user_message)[:50]}'")
                 perform_web_search(
                     settings=settings,
                     conversation_id=conversation_id,
@@ -1504,7 +2401,9 @@ def result_requires_message_reload(result: Any) -> bool:
                     agent_citations_list=agent_citations_list,
                     web_search_citations_list=web_search_citations_list,
                 )
-            
+                if web_search_citations_list:
+                    thought_tracker.add_thought('web_search', f"Got {len(web_search_citations_list)} web search results")
+
         # region 5 - FINAL conversation history preparation
             # ---------------------------------------------------------------------
             # 5) Prepare FINAL conversation history for GPT (including summarization)
@@ -1650,6 +2549,7 @@ def result_requires_message_reload(result: Any) -> bool:
                 allowed_roles_in_history = ['user', 'assistant'] # Add 'system' if you PERSIST general system messages not related to augmentation
                 max_file_content_length_in_history = 50000 # Increased limit for all file content in history
                 max_tabular_content_length_in_history = 50000 # Same limit for tabular data consistency
+                chat_tabular_files = set()  # Track tabular files uploaded directly to chat
 
                 for message in recent_messages:
                     role = message.get('role')
@@ -1685,25 +2585,38 @@ def result_requires_message_reload(result: Any) -> bool:
                         filename = message.get('filename', 'uploaded_file')
                         file_content = message.get('file_content', '') # Assuming file content is stored
                         is_table = message.get('is_table', False)
-                        
-                        # Use higher limit for tabular data that needs complete analysis
-                        content_limit = max_tabular_content_length_in_history if is_table else max_file_content_length_in_history
-                        
-                        display_content = file_content[:content_limit]
-                        if len(file_content) > content_limit:
-                            display_content += "..."
-                        
-                        # Enhanced message for tabular data
-                        if is_table:
+                        file_content_source = message.get('file_content_source', '')
+
+                        # Tabular files stored in blob (enhanced citations enabled) - reference plugin
+                        if is_table and file_content_source == 'blob':
+                            chat_tabular_files.add(filename)  # Track for mini SK analysis
                             conversation_history_for_api.append({
-                                'role': 'system', # Represent file as system info
-                                'content': f"[User uploaded a tabular data file named '{filename}'. This is CSV format data for analysis:\n{display_content}]\nThis is complete tabular data in CSV format. You can perform calculations, analysis, and data operations on this dataset."
+                                'role': 'system',
+                                'content': f"[User uploaded a tabular data file named '{filename}'. "
+                                    f"The file is stored in blob storage and available for analysis. "
+                                    f"Use the tabular_processing plugin functions (list_tabular_files, describe_tabular_file, "
+                                    f"aggregate_column, filter_rows, query_tabular_data, group_by_aggregate, group_by_datetime_component) to analyze this data. "
+                                    f"The file source is 'chat'.]"
                             })
                         else:
-                            conversation_history_for_api.append({
-                                'role': 'system', # Represent file as system info
-                                'content': f"[User uploaded a file named '{filename}'. Content preview:\n{display_content}]\nUse this file context if relevant."
-                            })
+                            # Use higher limit for tabular data that needs complete analysis
+                            content_limit = max_tabular_content_length_in_history if is_table else max_file_content_length_in_history
+
+                            display_content = file_content[:content_limit]
+                            if len(file_content) > content_limit:
+                                display_content += "..."
+
+                            # Enhanced message for tabular data
+                            if is_table:
+                                conversation_history_for_api.append({
+                                    'role': 'system', # Represent file as system info
+                                    'content': f"[User uploaded a tabular data file named '{filename}'. This is CSV format data for analysis:\n{display_content}]\nThis is complete tabular data in CSV format. You can perform calculations, analysis, and data operations on this dataset."
+                                })
+                            else:
+                                conversation_history_for_api.append({
+                                    'role': 'system', # Represent file as system info
+                                    'content': f"[User uploaded a file named '{filename}'. Content preview:\n{display_content}]\nUse this file context if relevant."
+                                })
                     elif role == 'image': # Handle image uploads with extracted text and vision analysis
                         filename = message.get('filename', 'uploaded_image')
                         is_user_upload = message.get('metadata', {}).get('is_user_upload', False)
@@ -1767,6 +2680,67 @@ def result_requires_message_reload(result: Any) -> bool:
 
                     # Ignored roles: 'safety', 'blocked', 'system' (if they are only for augmentation/summary)
 
+                # --- Mini SK analysis for tabular files uploaded directly to chat ---
+                if chat_tabular_files and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+                    chat_tabular_filenames_str = ", ".join(chat_tabular_files)
+                    log_event(
+                        f"[Chat Tabular SK] Detected {len(chat_tabular_files)} tabular file(s) uploaded to chat: {chat_tabular_filenames_str}",
+                        level=logging.INFO
+                    )
+                    plugin_logger = get_plugin_logger()
+                    baseline_tabular_invocation_count = len(
+                        plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000)
+                    )
+
+                    chat_tabular_analysis = asyncio.run(run_tabular_sk_analysis(
+                        user_question=user_message,
+                        tabular_filenames=chat_tabular_files,
+                        user_id=user_id,
+                        conversation_id=conversation_id,
+                        gpt_model=gpt_model,
+                        settings=settings,
+                        source_hint="chat",
+                    ))
+                    chat_tabular_invocations = get_new_plugin_invocations(
+                        plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000),
+                        baseline_tabular_invocation_count
+                    )
+                    chat_tabular_thought_payloads = get_tabular_tool_thought_payloads(chat_tabular_invocations)
+                    for thought_content, thought_detail in chat_tabular_thought_payloads:
+                        thought_tracker.add_thought('tabular_analysis', thought_content, thought_detail)
+                    chat_tabular_status_thought_payloads = get_tabular_status_thought_payloads(
+                        chat_tabular_invocations,
+                        analysis_succeeded=bool(chat_tabular_analysis),
+                    )
+                    for thought_content, thought_detail in chat_tabular_status_thought_payloads:
+                        thought_tracker.add_thought('tabular_analysis', thought_content, thought_detail)
+
+                    if chat_tabular_analysis:
+                        # Inject pre-computed analysis results as context
+                        conversation_history_for_api.append({
+                            'role': 'system',
+                            'content': (
+                                f"The following analysis was computed from the chat-uploaded tabular file(s) "
+                                f"{chat_tabular_filenames_str} using data analysis functions:\n\n"
+                                f"{chat_tabular_analysis}\n\n"
+                                f"Use these computed results to answer the user's question accurately."
+                            )
+                        })
+
+                        # Collect tool execution citations from SK tabular analysis
+                        chat_tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id)
+                        if chat_tabular_sk_citations:
+                            agent_citations_list.extend(chat_tabular_sk_citations)
+
+                        debug_print(f"[Chat Tabular SK] Analysis injected, {len(chat_tabular_analysis)} chars")
+                    else:
+                        thought_tracker.add_thought(
+                            'tabular_analysis',
+                            "Tabular analysis could not compute results; using existing chat file context",
+                            detail=f"files={chat_tabular_filenames_str}"
+                        )
+                        debug_print("[Chat Tabular SK] Analysis returned None, relying on existing file context messages")
+
                 # Ensure the very last message is the current user's message (it should be if fetched correctly)
                 if not conversation_history_for_api or conversation_history_for_api[-1]['role'] != 'user':
                     debug_print("Warning: Last message in history is not the user's current message. Appending.")
@@ -2110,6 +3084,27 @@ def orchestrator_error(e):
                     })
 
                 if selected_agent:
+                    agent_deployment_name = getattr(selected_agent, 'deployment_name', None) or gpt_model
+                    thought_tracker.add_thought('agent_tool_call', f"Sending to agent '{getattr(selected_agent, 'display_name', getattr(selected_agent, 'name', 'unknown'))}'")
+                    thought_tracker.add_thought('generation', f"Sending to '{agent_deployment_name}'")
+
+                    # Register callback to write plugin thoughts to Cosmos in real-time
+                    callback_key = f"{user_id}:{conversation_id}"
+                    plugin_logger = get_plugin_logger()
+
+                    def on_plugin_invocation(inv):
+                        duration_str = f" ({int(inv.duration_ms)}ms)" if inv.duration_ms else ""
+                        tool_name = f"{inv.plugin_name}.{inv.function_name}"
+                        thought_tracker.add_thought(
+                            'agent_tool_call',
+                            f"Agent called {tool_name}{duration_str}",
+                            detail=f"success={inv.success}"
+                        )
+
+                    plugin_logger.register_callback(callback_key, on_plugin_invocation)
+
+                    agent_invoke_start_time = time.time()
+
                     def invoke_selected_agent():
                         return asyncio.run(run_sk_call(
                             selected_agent.invoke,
@@ -2120,16 +3115,22 @@ def agent_success(result):
                         msg = str(result)
                         notice = None
                         agent_used = getattr(selected_agent, 'name', 'All Plugins')
-                        
+
+                        # Emit responded thought with total duration from user message
+                        agent_total_duration_s = round(time.time() - request_start_time, 1)
+                        thought_tracker.add_thought('generation', f"'{agent_deployment_name}' responded ({agent_total_duration_s}s from initial message)")
+
+                        # Deregister real-time thought callback
+                        plugin_logger.deregister_callbacks(callback_key)
+
                         # Get the actual model deployment used by the agent
                         actual_model_deployment = getattr(selected_agent, 'deployment_name', None) or agent_used
                         debug_print(f"Agent '{agent_used}' using deployment: {actual_model_deployment}")
-                        
+
                         # Extract detailed plugin invocations for enhanced agent citations
-                        plugin_logger = get_plugin_logger()
-                        # CRITICAL FIX: Filter by user_id and conversation_id to prevent cross-conversation contamination
+                        # (Thoughts already written to Cosmos in real-time by callback)
                         plugin_invocations = plugin_logger.get_invocations_for_conversation(user_id, conversation_id)
-                        
+
                         # Convert plugin invocations to citation format with detailed information
                         detailed_citations = []
                         for inv in plugin_invocations:
@@ -2204,6 +3205,7 @@ def make_json_serializable(obj):
                             )
                         return (msg, actual_model_deployment, "agent", notice)
                     def agent_error(e):
+                        plugin_logger.deregister_callbacks(callback_key)
                         debug_print(f"Error during Semantic Kernel Agent invocation: {str(e)}")
                         log_event(
                             f"Error during Semantic Kernel Agent invocation: {str(e)}",
@@ -2244,8 +3246,21 @@ def foundry_agent_success(result):
                                 or agent_used
                             )
 
+                            # Emit responded thought with total duration from user message
+                            foundry_total_duration_s = round(time.time() - request_start_time, 1)
+                            thought_tracker.add_thought('generation', f"'{actual_model_deployment}' responded ({foundry_total_duration_s}s from initial message)")
+
+                            # Deregister real-time thought callback
+                            plugin_logger.deregister_callbacks(callback_key)
+
                             foundry_citations = getattr(selected_agent, 'last_run_citations', []) or []
                             if foundry_citations:
+                                # Emit thoughts for Foundry agent citations/tool calls
+                                for citation in foundry_citations:
+                                    thought_tracker.add_thought(
+                                        'agent_tool_call',
+                                        f"Agent retrieved citation from Azure AI Foundry"
+                                    )
                                 for citation in foundry_citations:
                                     try:
                                         serializable = json.loads(json.dumps(citation, default=str))
@@ -2282,6 +3297,7 @@ def foundry_agent_success(result):
                             return (msg, actual_model_deployment, 'agent', notice)
 
                         def foundry_agent_error(e):
+                            plugin_logger.deregister_callbacks(callback_key)
                             log_event(
                                 f"Error during Azure AI Foundry agent invocation: {str(e)}",
                                 extra={
@@ -2360,6 +3376,7 @@ def kernel_error(e):
                         'on_error': kernel_error
                     })
 
+            thought_tracker.add_thought('generation', f"Sending to '{gpt_model}'")
             def invoke_gpt_fallback():
                 if not conversation_history_for_api:
                     raise Exception('Cannot generate response: No conversation history available.')
@@ -2443,12 +3460,18 @@ def gpt_error(e):
             })
 
             fallback_result = try_fallback_chain(fallback_steps)
+
             # Unpack result - handle both 4-tuple (SK) and 5-tuple (GPT with tokens)
             if len(fallback_result) == 5:
                 ai_message, final_model_used, chat_mode, kernel_fallback_notice, token_usage_data = fallback_result
             else:
                 ai_message, final_model_used, chat_mode, kernel_fallback_notice = fallback_result
                 token_usage_data = None
+
+            # Emit responded thought for non-agent paths (agent paths emit their own inside callbacks)
+            if not selected_agent:
+                gpt_total_duration_s = round(time.time() - request_start_time, 1)
+                thought_tracker.add_thought('generation', f"'{final_model_used}' responded ({gpt_total_duration_s}s from initial message)")
             
             # Collect token usage from Semantic Kernel services if available
             if kernel and not token_usage_data:
@@ -2510,8 +3533,8 @@ def gpt_error(e):
                 if hasattr(selected_agent, 'name'):
                     agent_name = selected_agent.name
             
-            assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}"
-            
+            # assistant_message_id was generated earlier for thought tracking
+
             # Get user_info and thread_id from the user message for ownership tracking and threading
             user_info_for_assistant = None
             user_thread_id = None
@@ -2672,7 +3695,8 @@ def gpt_error(e):
                 'web_search_citations': web_search_citations_list,
                 'agent_citations': agent_citations_list,
                 'reload_messages': reload_messages_required,
-                'kernel_fallback_notice': kernel_fallback_notice
+                'kernel_fallback_notice': kernel_fallback_notice,
+                'thoughts_enabled': thought_tracker.enabled
             }), 200
         
         except Exception as e:
@@ -2713,6 +3737,7 @@ def chat_stream_api():
             data = request.get_json()
             user_id = get_current_user_id()
             settings = get_settings()
+            request_start_time = time.time()
         except Exception as e:
             return jsonify({'error': f'Failed to parse request: {str(e)}'}), 400
         
@@ -3111,10 +4136,122 @@ def generate():
                 
                 conversation_item['last_updated'] = datetime.utcnow().isoformat()
                 cosmos_conversations_container.upsert_item(conversation_item)
-                
+
+                # Generate assistant_message_id early for thought tracking
+                assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}"
+
+                # Initialize thought tracker for streaming path
+                thought_tracker = ThoughtTracker(
+                    conversation_id=conversation_id,
+                    message_id=assistant_message_id,
+                    thread_id=current_user_thread_id,
+                    user_id=user_id
+                )
+
+                def emit_thought(step_type, content, detail=None):
+                    """Add a thought to Cosmos and return an SSE event string."""
+                    thought_tracker.add_thought(step_type, content, detail)
+                    return f"data: {json.dumps({'type': 'thought', 'step_index': thought_tracker.current_index - 1, 'step_type': step_type, 'content': content})}\n\n"
+
+                # Content Safety check (matching non-streaming path)
+                blocked = False
+                if settings.get('enable_content_safety') and "content_safety_client" in CLIENTS:
+                    yield emit_thought('content_safety', 'Checking content safety...')
+                    try:
+                        content_safety_client = CLIENTS["content_safety_client"]
+                        request_obj = AnalyzeTextOptions(text=user_message)
+                        cs_response = content_safety_client.analyze_text(request_obj)
+
+                        max_severity = 0
+                        triggered_categories = []
+                        blocklist_matches = []
+                        block_reasons = []
+
+                        for cat_result in cs_response.categories_analysis:
+                            triggered_categories.append({
+                                "category": cat_result.category,
+                                "severity": cat_result.severity
+                            })
+                            if cat_result.severity > max_severity:
+                                max_severity = cat_result.severity
+
+                        if cs_response.blocklists_match:
+                            for match in cs_response.blocklists_match:
+                                blocklist_matches.append({
+                                    "blocklistName": match.blocklist_name,
+                                    "blocklistItemId": match.blocklist_item_id,
+                                    "blocklistItemText": match.blocklist_item_text
+                                })
+
+                        if max_severity >= 4:
+                            blocked = True
+                            block_reasons.append("Max severity >= 4")
+                        if len(blocklist_matches) > 0:
+                            blocked = True
+                            block_reasons.append("Blocklist match")
+
+                        if blocked:
+                            # Upsert to safety container
+                            safety_item = {
+                                'id': str(uuid.uuid4()),
+                                'user_id': user_id,
+                                'conversation_id': conversation_id,
+                                'message': user_message,
+                                'triggered_categories': triggered_categories,
+                                'blocklist_matches': blocklist_matches,
+                                'timestamp': datetime.utcnow().isoformat(),
+                                'reason': "; ".join(block_reasons),
+                                'metadata': {}
+                            }
+                            cosmos_safety_container.upsert_item(safety_item)
+
+                            # Build blocked message
+                            blocked_msg_content = (
+                                "Your message was blocked by Content Safety.\n\n"
+                                f"**Reason**: {', '.join(block_reasons)}\n"
+                                "Triggered categories:\n"
+                            )
+                            for cat in triggered_categories:
+                                blocked_msg_content += (
+                                    f" - {cat['category']} (severity={cat['severity']})\n"
+                                )
+                            if blocklist_matches:
+                                blocked_msg_content += (
+                                    "\nBlocklist Matches:\n" +
+                                    "\n".join([f" - {m['blocklistItemText']} (in {m['blocklistName']})"
+                                            for m in blocklist_matches])
+                                )
+
+                            # Insert safety message
+                            safety_message_id = f"{conversation_id}_safety_{int(time.time())}_{random.randint(1000,9999)}"
+                            safety_doc = {
+                                'id': safety_message_id,
+                                'conversation_id': conversation_id,
+                                'role': 'safety',
+                                'content': blocked_msg_content.strip(),
+                                'timestamp': datetime.utcnow().isoformat(),
+                                'model_deployment_name': None,
+                                'metadata': {},
+                            }
+                            cosmos_messages_container.upsert_item(safety_doc)
+
+                            conversation_item['last_updated'] = datetime.utcnow().isoformat()
+                            cosmos_conversations_container.upsert_item(conversation_item)
+
+                            # Stream the blocked response and stop
+                            yield f"data: {json.dumps({'content': blocked_msg_content.strip(), 'blocked': True})}\n\n"
+                            yield "data: [DONE]\n\n"
+                            return
+
+                    except HttpResponseError as e:
+                        debug_print(f"[Content Safety Error - Streaming] {e}")
+                    except Exception as ex:
+                        debug_print(f"[Content Safety - Streaming] Unexpected error: {ex}")
+
                 # Hybrid search (if enabled)
                 combined_documents = []
                 if hybrid_search_enabled:
+                    yield emit_thought('search', f"Searching {document_scope or 'personal'} workspace documents for '{(search_query or user_message)[:50]}'")
                     try:
                         search_args = {
                             "query": search_query,
@@ -3144,8 +4281,10 @@ def generate():
                         search_results = hybrid_search(**search_args)
                     except Exception as e:
                         debug_print(f"Error during hybrid search: {e}")
-                    
+
                     if search_results:
+                        unique_doc_names_stream = set(doc.get('file_name', 'Unknown') for doc in search_results)
+                        yield emit_thought('search', f"Found {len(search_results)} results from {len(unique_doc_names_stream)} documents")
                         retrieved_texts = []
                         
                         for doc in search_results:
@@ -3159,14 +4298,24 @@ def generate():
                             chunk_id = doc.get('chunk_id', str(uuid.uuid4()))
                             score = doc.get('score', 0.0)
                             group_id = doc.get('group_id', None)
+                            sheet_name = doc.get('sheet_name')
+                            location_label, location_value = get_citation_location(
+                                file_name,
+                                page_number=page_number,
+                                chunk_text=chunk_text,
+                                sheet_name=sheet_name,
+                            )
                             
-                            citation = f"(Source: {file_name}, Page: {page_number}) [#{citation_id}]"
+                            citation = f"(Source: {file_name}, {location_label}: {location_value}) [#{citation_id}]"
                             retrieved_texts.append(f"{chunk_text}\n{citation}")
                             
                             combined_documents.append({
                                 "file_name": file_name,
                                 "citation_id": citation_id,
                                 "page_number": page_number,
+                                "sheet_name": sheet_name,
+                                "location_label": location_label,
+                                "location_value": location_value,
                                 "version": version,
                                 "classification": classification,
                                 "chunk_text": chunk_text,
@@ -3319,11 +4468,94 @@ def generate():
                             'content': system_prompt_search,
                             'documents': combined_documents
                         })
-                        
+
                         # Reorder hybrid citations list in descending order based on page_number
                         hybrid_citations_list.sort(key=lambda x: x.get('page_number', 0), reverse=True)
                 
+                workspace_tabular_files = set()
+                if hybrid_search_enabled and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+                    workspace_tabular_files = collect_workspace_tabular_filenames(
+                        combined_documents=combined_documents,
+                        selected_document_ids=selected_document_ids,
+                        selected_document_id=selected_document_id,
+                        document_scope=document_scope,
+                    )
+
+                if hybrid_search_enabled and workspace_tabular_files and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+                    tabular_source_hint = determine_tabular_source_hint(
+                        document_scope,
+                        active_group_id=active_group_id,
+                        active_public_workspace_id=active_public_workspace_id,
+                    )
+                    tabular_filenames_str = ", ".join(sorted(workspace_tabular_files))
+                    plugin_logger = get_plugin_logger()
+                    baseline_tabular_invocation_count = len(
+                        plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000)
+                    )
+
+                    tabular_analysis = asyncio.run(run_tabular_sk_analysis(
+                        user_question=user_message,
+                        tabular_filenames=workspace_tabular_files,
+                        user_id=user_id,
+                        conversation_id=conversation_id,
+                        gpt_model=gpt_model,
+                        settings=settings,
+                        source_hint=tabular_source_hint,
+                        group_id=active_group_id if tabular_source_hint == 'group' else None,
+                        public_workspace_id=active_public_workspace_id if tabular_source_hint == 'public' else None,
+                    ))
+                    tabular_invocations = get_new_plugin_invocations(
+                        plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000),
+                        baseline_tabular_invocation_count
+                    )
+                    tabular_thought_payloads = get_tabular_tool_thought_payloads(tabular_invocations)
+                    for thought_content, thought_detail in tabular_thought_payloads:
+                        yield emit_thought('tabular_analysis', thought_content, thought_detail)
+                    tabular_status_thought_payloads = get_tabular_status_thought_payloads(
+                        tabular_invocations,
+                        analysis_succeeded=bool(tabular_analysis),
+                    )
+                    for thought_content, thought_detail in tabular_status_thought_payloads:
+                        yield emit_thought('tabular_analysis', thought_content, thought_detail)
+
+                    if tabular_analysis:
+                        system_messages_for_augmentation.append({
+                            'role': 'system',
+                            'content': (
+                                f"The following analysis was computed from the tabular file(s) "
+                                f"{tabular_filenames_str} using data analysis functions:\n\n"
+                                f"{tabular_analysis}\n\n"
+                                f"Use these computed results to answer the user's question accurately."
+                            )
+                        })
+
+                        tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id)
+                        if tabular_sk_citations:
+                            agent_citations_list.extend(tabular_sk_citations)
+                    else:
+                        system_messages_for_augmentation.append({
+                            'role': 'system',
+                            'content': (
+                                f"IMPORTANT: The selected workspace tabular file(s) are {tabular_filenames_str}. "
+                                f"The search results contain only a schema summary (column names and a few sample rows), NOT the full data. "
+                                f"You MUST use the tabular_processing plugin functions to answer ANY question about these files. "
+                                f"Do NOT attempt to answer using the schema summary alone — it is incomplete. "
+                                f"Available functions: describe_tabular_file, aggregate_column, filter_rows, query_tabular_data, group_by_aggregate, group_by_datetime_component. "
+                                f"Use source='{tabular_source_hint}'"
+                                + (f" and group_id='{active_group_id}'" if tabular_source_hint == 'group' else "")
+                                + (f" and public_workspace_id='{active_public_workspace_id}'" if tabular_source_hint == 'public' else "")
+                                + "."
+                            )
+                        })
+
+                        yield emit_thought(
+                            'tabular_analysis',
+                            "Tabular analysis could not compute results; using schema context instead",
+                            detail=f"files={tabular_filenames_str}"
+                        )
+
                 if web_search_enabled:
+                    yield emit_thought('web_search', f"Searching the web for '{(search_query or user_message)[:50]}'")
                     perform_web_search(
                         settings=settings,
                         conversation_id=conversation_id,
@@ -3339,6 +4571,8 @@ def generate():
                         agent_citations_list=agent_citations_list,
                         web_search_citations_list=web_search_citations_list,
                     )
+                    if web_search_citations_list:
+                        yield emit_thought('web_search', f"Got {len(web_search_citations_list)} web search results")
 
                 # Update message chat type
                 message_chat_type = None
@@ -3381,15 +4615,130 @@ def generate():
                             'content': aug_msg['content']
                         })
                     
-                    # Add recent messages
+                    # Add recent messages (with file role handling)
                     allowed_roles_in_history = ['user', 'assistant']
+                    max_file_content_length_in_history = 50000
+                    max_tabular_content_length_in_history = 50000
+                    chat_tabular_files = set()  # Track tabular files uploaded directly to chat
+
                     for message in recent_messages:
-                        if message.get('role') in allowed_roles_in_history:
+                        role = message.get('role')
+                        content = message.get('content', '')
+
+                        if role in allowed_roles_in_history:
                             conversation_history_for_api.append({
-                                'role': message['role'],
-                                'content': message.get('content', '')
+                                'role': role,
+                                'content': content
                             })
-                    
+                        elif role == 'file':
+                            filename = message.get('filename', 'uploaded_file')
+                            file_content = message.get('file_content', '')
+                            is_table = message.get('is_table', False)
+                            file_content_source = message.get('file_content_source', '')
+
+                            # Tabular files stored in blob - track for mini SK analysis
+                            if is_table and file_content_source == 'blob':
+                                chat_tabular_files.add(filename)
+                                conversation_history_for_api.append({
+                                    'role': 'system',
+                                    'content': (
+                                        f"[User uploaded a tabular data file named '{filename}'. "
+                                        f"The file is stored in blob storage and available for analysis. "
+                                        f"Use the tabular_processing plugin functions (list_tabular_files, "
+                                        f"describe_tabular_file, aggregate_column, filter_rows, "
+                                        f"query_tabular_data, group_by_aggregate, group_by_datetime_component) to analyze this data. "
+                                        f"The file source is 'chat'.]"
+                                    )
+                                })
+                            else:
+                                content_limit = (
+                                    max_tabular_content_length_in_history if is_table
+                                    else max_file_content_length_in_history
+                                )
+                                display_content = file_content[:content_limit]
+                                if len(file_content) > content_limit:
+                                    display_content += "..."
+
+                                if is_table:
+                                    conversation_history_for_api.append({
+                                        'role': 'system',
+                                        'content': (
+                                            f"[User uploaded a tabular data file named '{filename}'. "
+                                            f"This is CSV format data for analysis:\n{display_content}]\n"
+                                            f"This is complete tabular data in CSV format. You can perform "
+                                            f"calculations, analysis, and data operations on this dataset."
+                                        )
+                                    })
+                                else:
+                                    conversation_history_for_api.append({
+                                        'role': 'system',
+                                        'content': (
+                                            f"[User uploaded a file named '{filename}'. "
+                                            f"Content preview:\n{display_content}]\n"
+                                            f"Use this file context if relevant."
+                                        )
+                                    })
+
+                    # --- Mini SK analysis for tabular files uploaded directly to chat ---
+                    if chat_tabular_files and settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+                        chat_tabular_filenames_str = ", ".join(chat_tabular_files)
+                        log_event(
+                            f"[Chat Tabular SK] Streaming: Detected {len(chat_tabular_files)} tabular file(s) uploaded to chat: {chat_tabular_filenames_str}",
+                            level=logging.INFO
+                        )
+                        plugin_logger = get_plugin_logger()
+                        baseline_tabular_invocation_count = len(
+                            plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000)
+                        )
+
+                        chat_tabular_analysis = asyncio.run(run_tabular_sk_analysis(
+                            user_question=user_message,
+                            tabular_filenames=chat_tabular_files,
+                            user_id=user_id,
+                            conversation_id=conversation_id,
+                            gpt_model=gpt_model,
+                            settings=settings,
+                            source_hint="chat",
+                        ))
+                        chat_tabular_invocations = get_new_plugin_invocations(
+                            plugin_logger.get_invocations_for_conversation(user_id, conversation_id, limit=1000),
+                            baseline_tabular_invocation_count
+                        )
+                        chat_tabular_thought_payloads = get_tabular_tool_thought_payloads(chat_tabular_invocations)
+                        for thought_content, thought_detail in chat_tabular_thought_payloads:
+                            yield emit_thought('tabular_analysis', thought_content, thought_detail)
+                        chat_tabular_status_thought_payloads = get_tabular_status_thought_payloads(
+                            chat_tabular_invocations,
+                            analysis_succeeded=bool(chat_tabular_analysis),
+                        )
+                        for thought_content, thought_detail in chat_tabular_status_thought_payloads:
+                            yield emit_thought('tabular_analysis', thought_content, thought_detail)
+
+                        if chat_tabular_analysis:
+                            conversation_history_for_api.append({
+                                'role': 'system',
+                                'content': (
+                                    f"The following analysis was computed from the chat-uploaded tabular file(s) "
+                                    f"{chat_tabular_filenames_str} using data analysis functions:\n\n"
+                                    f"{chat_tabular_analysis}\n\n"
+                                    f"Use these computed results to answer the user's question accurately."
+                                )
+                            })
+
+                            # Collect tool execution citations
+                            chat_tabular_sk_citations = collect_tabular_sk_citations(user_id, conversation_id)
+                            if chat_tabular_sk_citations:
+                                agent_citations_list.extend(chat_tabular_sk_citations)
+
+                            debug_print(f"[Chat Tabular SK] Streaming: Analysis injected, {len(chat_tabular_analysis)} chars")
+                        else:
+                            yield emit_thought(
+                                'tabular_analysis',
+                                "Tabular analysis could not compute results; using existing chat file context",
+                                detail=f"files={chat_tabular_filenames_str}"
+                            )
+                            debug_print("[Chat Tabular SK] Streaming: Analysis returned None, relying on existing file context")
+
                 except Exception as e:
                     yield f"data: {json.dumps({'error': f'History error: {str(e)}'})}\n\n"
                     return
@@ -3472,7 +4821,7 @@ def generate():
                 # Stream the response
                 accumulated_content = ""
                 token_usage_data = None  # Will be populated from final stream chunk
-                assistant_message_id = f"{conversation_id}_assistant_{int(time.time())}_{random.randint(1000,9999)}"
+                # assistant_message_id was generated earlier for thought tracking
                 final_model_used = gpt_model  # Default to gpt_model, will be overridden if agent is used
                 
                 # DEBUG: Check agent streaming decision
@@ -3482,8 +4831,24 @@ def generate():
                 try:
                     if use_agent_streaming and selected_agent:
                         # Stream from agent using invoke_stream
+                        yield emit_thought('agent_tool_call', f"Sending to agent '{agent_display_name_used or agent_name_used}'")
+                        yield emit_thought('generation', f"Sending to '{actual_model_used}'")
                         debug_print(f"--- Streaming from Agent: {agent_name_used} ---")
-                        
+
+                        # Register callback to persist plugin thoughts to Cosmos in real-time
+                        callback_key = f"{user_id}:{conversation_id}"
+                        plugin_logger_cb = get_plugin_logger()
+
+                        def on_plugin_invocation_streaming(inv):
+                            duration_str = f" ({int(inv.duration_ms)}ms)" if inv.duration_ms else ""
+                            tool_name = f"{inv.plugin_name}.{inv.function_name}"
+                            thought_tracker.add_thought(
+                                'agent_tool_call',
+                                f"Agent called {tool_name}{duration_str}"
+                            )
+
+                        plugin_logger_cb.register_callback(callback_key, on_plugin_invocation_streaming)
+
                         # Import required classes
                         from semantic_kernel.contents.chat_message_content import ChatMessageContent
                         
@@ -3497,6 +4862,8 @@ def generate():
                             for msg in conversation_history_for_api
                         ]
                         
+                        agent_stream_start_time = time.time()
+
                         # Stream agent responses - collect chunks first then yield
                         async def stream_agent_async():
                             """Collect all streaming chunks from agent"""
@@ -3524,7 +4891,6 @@ async def stream_agent_async():
                             return chunks, usage_data
                         
                         # Execute async streaming
-                        import asyncio
                         try:
                             # Try to get existing event loop
                             loop = asyncio.get_event_loop()
@@ -3539,36 +4905,53 @@ async def stream_agent_async():
                         try:
                             # Run streaming and collect chunks and usage
                             chunks, stream_usage = loop.run_until_complete(stream_agent_async())
-                            
-                            # Yield chunks to frontend
-                            for chunk_content in chunks:
-                                accumulated_content += chunk_content
-                                yield f"data: {json.dumps({'content': chunk_content})}\n\n"
-                            
-                            # Try to capture token usage from stream metadata
-                            if stream_usage:
-                                # stream_usage is a CompletionUsage object, not a dict
-                                prompt_tokens = getattr(stream_usage, 'prompt_tokens', 0)
-                                completion_tokens = getattr(stream_usage, 'completion_tokens', 0)
-                                total_tokens = getattr(stream_usage, 'total_tokens', None)
-                                
-                                # Calculate total if not provided
-                                if total_tokens is None or total_tokens == 0:
-                                    total_tokens = prompt_tokens + completion_tokens
-                                
-                                token_usage_data = {
-                                    'prompt_tokens': prompt_tokens,
-                                    'completion_tokens': completion_tokens,
-                                    'total_tokens': total_tokens,
-                                    'captured_at': datetime.utcnow().isoformat()
-                                }
-                                debug_print(f"[Agent Streaming Tokens] From metadata - prompt: {prompt_tokens}, completion: {completion_tokens}, total: {total_tokens}")
                         except Exception as stream_error:
+                            plugin_logger_cb.deregister_callbacks(callback_key)
                             debug_print(f"❌ Agent streaming error: {stream_error}")
                             import traceback
                             traceback.print_exc()
                             yield f"data: {json.dumps({'error': f'Agent streaming failed: {str(stream_error)}'})}\n\n"
                             return
+
+                        # Emit responded thought with total duration from user message
+                        agent_stream_total_duration_s = round(time.time() - request_start_time, 1)
+                        yield emit_thought('generation', f"'{actual_model_used}' responded ({agent_stream_total_duration_s}s from initial message)")
+
+                        # Deregister callback (agent completed successfully)
+                        plugin_logger_cb.deregister_callbacks(callback_key)
+
+                        # Emit SSE-only events for streaming UI (Cosmos writes already done by callback)
+                        agent_plugin_invocations = plugin_logger_cb.get_invocations_for_conversation(user_id, conversation_id)
+                        for inv in agent_plugin_invocations:
+                            duration_str = f" ({int(inv.duration_ms)}ms)" if inv.duration_ms else ""
+                            tool_name = f"{inv.plugin_name}.{inv.function_name}"
+                            content = f"Agent called {tool_name}{duration_str}"
+                            yield f"data: {json.dumps({'type': 'thought', 'step_index': thought_tracker.current_index, 'step_type': 'agent_tool_call', 'content': content})}\n\n"
+                            thought_tracker.current_index += 1
+
+                        # Yield chunks to frontend
+                        for chunk_content in chunks:
+                            accumulated_content += chunk_content
+                            yield f"data: {json.dumps({'content': chunk_content})}\n\n"
+
+                        # Try to capture token usage from stream metadata
+                        if stream_usage:
+                            # stream_usage is a CompletionUsage object, not a dict
+                            prompt_tokens = getattr(stream_usage, 'prompt_tokens', 0)
+                            completion_tokens = getattr(stream_usage, 'completion_tokens', 0)
+                            total_tokens = getattr(stream_usage, 'total_tokens', None)
+
+                            # Calculate total if not provided
+                            if total_tokens is None or total_tokens == 0:
+                                total_tokens = prompt_tokens + completion_tokens
+
+                            token_usage_data = {
+                                'prompt_tokens': prompt_tokens,
+                                'completion_tokens': completion_tokens,
+                                'total_tokens': total_tokens,
+                                'captured_at': datetime.utcnow().isoformat()
+                            }
+                            debug_print(f"[Agent Streaming Tokens] From metadata - prompt: {prompt_tokens}, completion: {completion_tokens}, total: {total_tokens}")
                         
                         # Collect token usage from kernel services if not captured from stream
                         if not token_usage_data:
@@ -3650,6 +5033,7 @@ def make_json_serializable(obj):
                     
                     else:
                         # Stream from regular GPT model (non-agent)
+                        yield emit_thought('generation', f"Sending to '{gpt_model}'")
                         debug_print(f"--- Streaming from GPT ({gpt_model}) ---")
                         
                         # Prepare stream parameters
@@ -3700,6 +5084,10 @@ def make_json_serializable(obj):
                                     'captured_at': datetime.utcnow().isoformat()
                                 }
                                 debug_print(f"[Streaming Tokens] Captured usage - prompt: {chunk.usage.prompt_tokens}, completion: {chunk.usage.completion_tokens}, total: {chunk.usage.total_tokens}")
+
+                        # Emit responded thought for regular LLM streaming
+                        gpt_stream_total_duration_s = round(time.time() - request_start_time, 1)
+                        yield emit_thought('generation', f"'{gpt_model}' responded ({gpt_stream_total_duration_s}s from initial message)")
                     
                     # Stream complete - save message and send final metadata
                     # Get user thread info to maintain thread consistency
@@ -3818,7 +5206,8 @@ def make_json_serializable(obj):
                         'agent_citations': agent_citations_list,
                         'agent_display_name': agent_display_name_used if use_agent_streaming else None,
                         'agent_name': agent_name_used if use_agent_streaming else None,
-                        'full_content': accumulated_content
+                        'full_content': accumulated_content,
+                        'thoughts_enabled': thought_tracker.enabled
                     }
                     yield f"data: {json.dumps(final_data)}\n\n"
                     
diff --git a/application/single_app/route_backend_conversation_export.py b/application/single_app/route_backend_conversation_export.py
index aad750e4..abd6490f 100644
--- a/application/single_app/route_backend_conversation_export.py
+++ b/application/single_app/route_backend_conversation_export.py
@@ -2,17 +2,31 @@
 
 import io
 import json
+import markdown2
+import re
+import tempfile
 import zipfile
+from collections import Counter, defaultdict
 from datetime import datetime
+from html import escape as _escape_html
+from typing import Any, Dict, List, Optional
 
 from config import *
+from flask import jsonify, make_response, request
+from functions_appinsights import log_event
 from functions_authentication import *
-from functions_settings import *
-from flask import Response, jsonify, request, make_response
+from functions_chat import sort_messages_by_thread
+from functions_conversation_metadata import update_conversation_with_metadata
 from functions_debug import debug_print
+from functions_settings import *
+from functions_thoughts import get_thoughts_for_conversation
 from swagger_wrapper import swagger_route, get_auth_security
 
 
+TRANSCRIPT_ROLES = {'user', 'assistant'}
+SUMMARY_SOURCE_CHAR_LIMIT = 60000
+
+
 def register_route_backend_conversation_export(app):
     """Register conversation export API routes."""
 
@@ -29,32 +43,36 @@ def api_export_conversations():
             conversation_ids (list): List of conversation IDs to export.
             format (str): Export format — "json" or "markdown".
             packaging (str): Output packaging — "single" or "zip".
+            include_summary_intro (bool): Whether to generate a per-conversation intro.
+            summary_model_deployment (str): Optional model deployment for summary generation.
         """
         user_id = get_current_user_id()
         if not user_id:
             return jsonify({'error': 'User not authenticated'}), 401
 
-        data = request.get_json()
+        data = request.get_json(silent=True)
         if not data:
             return jsonify({'error': 'Request body is required'}), 400
 
         conversation_ids = data.get('conversation_ids', [])
-        export_format = data.get('format', 'json').lower()
-        packaging = data.get('packaging', 'single').lower()
+        export_format = str(data.get('format', 'json')).lower()
+        packaging = str(data.get('packaging', 'single')).lower()
+        include_summary_intro = bool(data.get('include_summary_intro', False))
+        summary_model_deployment = str(data.get('summary_model_deployment', '') or '').strip()
 
         if not conversation_ids or not isinstance(conversation_ids, list):
             return jsonify({'error': 'At least one conversation_id is required'}), 400
 
-        if export_format not in ('json', 'markdown'):
-            return jsonify({'error': 'Format must be "json" or "markdown"'}), 400
+        if export_format not in ('json', 'markdown', 'pdf'):
+            return jsonify({'error': 'Format must be "json", "markdown", or "pdf"'}), 400
 
         if packaging not in ('single', 'zip'):
             return jsonify({'error': 'Packaging must be "single" or "zip"'}), 400
 
         try:
+            settings = get_settings()
             exported = []
             for conv_id in conversation_ids:
-                # Verify ownership and fetch conversation
                 try:
                     conversation = cosmos_conversations_container.read_item(
                         item=conv_id,
@@ -64,225 +82,1597 @@ def api_export_conversations():
                     debug_print(f"Export: conversation {conv_id} not found or access denied")
                     continue
 
-                # Verify user owns this conversation
                 if conversation.get('user_id') != user_id:
                     debug_print(f"Export: user {user_id} does not own conversation {conv_id}")
                     continue
 
-                # Fetch messages ordered by timestamp
-                message_query = f"""
+                message_query = """
                     SELECT * FROM c
-                    WHERE c.conversation_id = '{conv_id}'
+                    WHERE c.conversation_id = @conversation_id
                     ORDER BY c.timestamp ASC
                 """
                 messages = list(cosmos_messages_container.query_items(
                     query=message_query,
+                    parameters=[{'name': '@conversation_id', 'value': conv_id}],
                     partition_key=conv_id
                 ))
 
-                # Filter for active thread messages only
-                filtered_messages = []
-                for msg in messages:
-                    thread_info = msg.get('metadata', {}).get('thread_info', {})
-                    active = thread_info.get('active_thread')
-                    if active is True or active is None or 'active_thread' not in thread_info:
-                        filtered_messages.append(msg)
-
-                exported.append({
-                    'conversation': _sanitize_conversation(conversation),
-                    'messages': [_sanitize_message(m) for m in filtered_messages]
-                })
+                exported.append(
+                    _build_export_entry(
+                        conversation=conversation,
+                        raw_messages=messages,
+                        user_id=user_id,
+                        settings=settings,
+                        include_summary_intro=include_summary_intro,
+                        summary_model_deployment=summary_model_deployment
+                    )
+                )
 
             if not exported:
                 return jsonify({'error': 'No accessible conversations found'}), 404
 
-            # Generate export content
             timestamp_str = datetime.utcnow().strftime('%Y%m%d_%H%M%S')
 
             if packaging == 'zip':
                 return _build_zip_response(exported, export_format, timestamp_str)
-            else:
-                return _build_single_file_response(exported, export_format, timestamp_str)
-
-        except Exception as e:
-            debug_print(f"Export error: {str(e)}")
-            return jsonify({'error': f'Export failed: {str(e)}'}), 500
-
-    def _sanitize_conversation(conv):
-        """Return only user-facing conversation fields."""
-        return {
-            'id': conv.get('id'),
-            'title': conv.get('title', 'Untitled'),
-            'last_updated': conv.get('last_updated', ''),
-            'chat_type': conv.get('chat_type', 'personal'),
-            'tags': conv.get('tags', []),
-            'is_pinned': conv.get('is_pinned', False),
-            'context': conv.get('context', [])
-        }
-
-    def _sanitize_message(msg):
-        """Return only user-facing message fields."""
-        result = {
-            'role': msg.get('role', ''),
-            'content': msg.get('content', ''),
-            'timestamp': msg.get('timestamp', ''),
-        }
-        # Include citations if present
-        if msg.get('citations'):
-            result['citations'] = msg['citations']
-        # Include context/tool info if present
-        if msg.get('context'):
-            result['context'] = msg['context']
-        return result
-
-    def _build_single_file_response(exported, export_format, timestamp_str):
-        """Build a single-file download response."""
-        if export_format == 'json':
-            content = json.dumps(exported, indent=2, ensure_ascii=False, default=str)
-            filename = f"conversations_export_{timestamp_str}.json"
-            content_type = 'application/json; charset=utf-8'
+
+            return _build_single_file_response(exported, export_format, timestamp_str)
+
+        except Exception as exc:
+            debug_print(f"Export error: {str(exc)}")
+            log_event(f"Conversation export failed: {exc}", level="WARNING")
+            return jsonify({'error': f'Export failed: {str(exc)}'}), 500
+
+
+def _build_export_entry(
+    conversation: Dict[str, Any],
+    raw_messages: List[Dict[str, Any]],
+    user_id: str,
+    settings: Dict[str, Any],
+    include_summary_intro: bool = False,
+    summary_model_deployment: str = ''
+) -> Dict[str, Any]:
+    filtered_messages = _filter_messages_for_export(raw_messages)
+    ordered_messages = sort_messages_by_thread(filtered_messages)
+
+    raw_thoughts = get_thoughts_for_conversation(conversation.get('id'), user_id)
+    thoughts_by_message = defaultdict(list)
+    for thought in raw_thoughts:
+        thoughts_by_message[thought.get('message_id')].append(_sanitize_thought(thought))
+
+    exported_messages = []
+    role_counts = Counter()
+    total_citation_counts = Counter({'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0})
+    transcript_index = 0
+    total_thoughts = 0
+
+    for sequence_index, message in enumerate(ordered_messages, start=1):
+        role = message.get('role', 'unknown')
+        role_counts[role] += 1
+
+        message_transcript_index = None
+        if role in TRANSCRIPT_ROLES:
+            transcript_index += 1
+            message_transcript_index = transcript_index
+
+        thoughts = thoughts_by_message.get(message.get('id'), [])
+        exported_message = _sanitize_message(
+            message,
+            sequence_index=sequence_index,
+            transcript_index=message_transcript_index,
+            thoughts=thoughts
+        )
+        exported_messages.append(exported_message)
+
+        counts = exported_message.get('citation_counts', {})
+        for key in total_citation_counts:
+            total_citation_counts[key] += counts.get(key, 0)
+        total_thoughts += len(thoughts)
+
+    # Compute message time range for summary caching
+    message_time_start = None
+    message_time_end = None
+    if ordered_messages:
+        message_time_start = ordered_messages[0].get('timestamp')
+        message_time_end = ordered_messages[-1].get('timestamp')
+
+    sanitized_conversation = _sanitize_conversation(
+        conversation,
+        messages=exported_messages,
+        role_counts=role_counts,
+        citation_counts=total_citation_counts,
+        thought_count=total_thoughts
+    )
+    summary_intro = _build_summary_intro(
+        messages=exported_messages,
+        conversation=conversation,
+        sanitized_conversation=sanitized_conversation,
+        settings=settings,
+        enabled=include_summary_intro,
+        summary_model_deployment=summary_model_deployment,
+        message_time_start=message_time_start,
+        message_time_end=message_time_end
+    )
+
+    return {
+        'conversation': sanitized_conversation,
+        'summary_intro': summary_intro,
+        'messages': exported_messages
+    }
+
+
+def _filter_messages_for_export(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    filtered_messages = []
+    for message in messages:
+        metadata = message.get('metadata', {}) or {}
+        if metadata.get('is_deleted') is True:
+            continue
+
+        thread_info = metadata.get('thread_info', {}) or {}
+        active = thread_info.get('active_thread')
+        if active is True or active is None or 'active_thread' not in thread_info:
+            filtered_messages.append(message)
+
+    return filtered_messages
+
+
+def _sanitize_conversation(
+    conversation: Dict[str, Any],
+    messages: List[Dict[str, Any]],
+    role_counts: Counter,
+    citation_counts: Counter,
+    thought_count: int
+) -> Dict[str, Any]:
+    transcript_count = sum(1 for message in messages if message.get('is_transcript_message'))
+    return {
+        'id': conversation.get('id'),
+        'title': conversation.get('title', 'Untitled'),
+        'last_updated': conversation.get('last_updated', ''),
+        'chat_type': conversation.get('chat_type', 'personal'),
+        'tags': conversation.get('tags', []),
+        'context': conversation.get('context', []),
+        'classification': conversation.get('classification', []),
+        'strict': conversation.get('strict', False),
+        'is_pinned': conversation.get('is_pinned', False),
+        'scope_locked': conversation.get('scope_locked'),
+        'locked_contexts': conversation.get('locked_contexts', []),
+        'message_count': len(messages),
+        'transcript_message_count': transcript_count,
+        'message_counts_by_role': dict(role_counts),
+        'citation_counts': dict(citation_counts),
+        'thought_count': thought_count
+    }
+
+
+def _sanitize_message(
+    message: Dict[str, Any],
+    sequence_index: int,
+    transcript_index: Optional[int],
+    thoughts: List[Dict[str, Any]]
+) -> Dict[str, Any]:
+    role = message.get('role', '')
+    content = message.get('content', '')
+    raw_citation_buckets = _collect_raw_citation_buckets(message)
+    normalized_citations = _normalize_citations(raw_citation_buckets)
+    citation_counts = _build_citation_counts(normalized_citations)
+    details = _curate_message_details(message, citation_counts, len(thoughts))
+
+    return {
+        'id': message.get('id'),
+        'role': role,
+        'speaker_label': _role_to_label(role),
+        'sequence_index': sequence_index,
+        'transcript_index': transcript_index,
+        'label': f"Turn {transcript_index}" if transcript_index else f"Message {sequence_index}",
+        'is_transcript_message': role in TRANSCRIPT_ROLES,
+        'timestamp': message.get('timestamp', ''),
+        'content': content,
+        'content_text': _normalize_content(content),
+        'details': details,
+        'citations': normalized_citations,
+        'citation_counts': citation_counts,
+        'thoughts': thoughts,
+        'legacy_citations': raw_citation_buckets['legacy'],
+        'hybrid_citations': raw_citation_buckets['hybrid'],
+        'web_search_citations': raw_citation_buckets['web'],
+        'agent_citations': raw_citation_buckets['agent']
+    }
+
+
+def _sanitize_thought(thought: Dict[str, Any]) -> Dict[str, Any]:
+    return {
+        'step_index': thought.get('step_index'),
+        'step_type': thought.get('step_type'),
+        'content': thought.get('content'),
+        'detail': thought.get('detail'),
+        'duration_ms': thought.get('duration_ms'),
+        'timestamp': thought.get('timestamp')
+    }
+
+
+def _collect_raw_citation_buckets(message: Dict[str, Any]) -> Dict[str, List[Any]]:
+    def ensure_list(value: Any) -> List[Any]:
+        if not value:
+            return []
+        return value if isinstance(value, list) else [value]
+
+    return {
+        'legacy': ensure_list(message.get('citations')),
+        'hybrid': ensure_list(message.get('hybrid_citations')),
+        'web': ensure_list(message.get('web_search_citations')),
+        'agent': ensure_list(message.get('agent_citations'))
+    }
+
+
+def _normalize_citations(raw_citation_buckets: Dict[str, List[Any]]) -> List[Dict[str, Any]]:
+    normalized = []
+
+    for citation in raw_citation_buckets.get('hybrid', []):
+        if isinstance(citation, dict):
+            normalized.append({
+                'citation_type': 'document',
+                'label': _build_document_citation_label(citation),
+                'file_name': citation.get('file_name'),
+                'title': citation.get('title') or citation.get('file_name'),
+                'page_number': citation.get('page_number'),
+                'citation_id': citation.get('citation_id'),
+                'chunk_id': citation.get('chunk_id'),
+                'metadata_type': citation.get('metadata_type'),
+                'metadata_content': citation.get('metadata_content'),
+                'score': citation.get('score'),
+                'classification': citation.get('classification'),
+                'url': citation.get('url')
+            })
         else:
-            parts = []
-            for entry in exported:
-                parts.append(_conversation_to_markdown(entry))
-            content = '\n\n---\n\n'.join(parts)
-            filename = f"conversations_export_{timestamp_str}.md"
-            content_type = 'text/markdown; charset=utf-8'
-
-        response = make_response(content)
-        response.headers['Content-Type'] = content_type
-        response.headers['Content-Disposition'] = f'attachment; filename="{filename}"'
-        return response
-
-    def _build_zip_response(exported, export_format, timestamp_str):
-        """Build a ZIP archive containing one file per conversation."""
-        buffer = io.BytesIO()
-        with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as zf:
-            for entry in exported:
-                conv = entry['conversation']
-                safe_title = _safe_filename(conv.get('title', 'Untitled'))
-                conv_id_short = conv.get('id', 'unknown')[:8]
-
-                if export_format == 'json':
-                    file_content = json.dumps(entry, indent=2, ensure_ascii=False, default=str)
-                    ext = 'json'
-                else:
-                    file_content = _conversation_to_markdown(entry)
-                    ext = 'md'
+            normalized.append({
+                'citation_type': 'document',
+                'label': str(citation),
+                'value': str(citation)
+            })
+
+    for citation in raw_citation_buckets.get('web', []):
+        if isinstance(citation, dict):
+            title = citation.get('title') or citation.get('url') or 'Web source'
+            normalized.append({
+                'citation_type': 'web',
+                'label': title,
+                'title': title,
+                'url': citation.get('url')
+            })
+        else:
+            normalized.append({
+                'citation_type': 'web',
+                'label': str(citation),
+                'value': str(citation)
+            })
+
+    for citation in raw_citation_buckets.get('agent', []):
+        if isinstance(citation, dict):
+            tool_name = citation.get('tool_name') or citation.get('function_name') or 'Tool invocation'
+            normalized.append({
+                'citation_type': 'agent_tool',
+                'label': tool_name,
+                'tool_name': citation.get('tool_name'),
+                'function_name': citation.get('function_name'),
+                'plugin_name': citation.get('plugin_name'),
+                'success': citation.get('success'),
+                'timestamp': citation.get('timestamp')
+            })
+        else:
+            normalized.append({
+                'citation_type': 'agent_tool',
+                'label': str(citation),
+                'value': str(citation)
+            })
+
+    for citation in raw_citation_buckets.get('legacy', []):
+        if isinstance(citation, dict):
+            title = citation.get('title') or citation.get('filepath') or citation.get('url') or 'Legacy citation'
+            normalized.append({
+                'citation_type': 'legacy',
+                'label': title,
+                'title': title,
+                'url': citation.get('url'),
+                'filepath': citation.get('filepath')
+            })
+        else:
+            normalized.append({
+                'citation_type': 'legacy',
+                'label': str(citation),
+                'value': str(citation)
+            })
+
+    return normalized
+
+
+def _build_document_citation_label(citation: Dict[str, Any]) -> str:
+    file_name = citation.get('file_name') or citation.get('title') or 'Document source'
+    metadata_type = citation.get('metadata_type')
+    page_number = citation.get('page_number')
+
+    if metadata_type:
+        return f"{file_name} — {metadata_type.replace('_', ' ').title()}"
+    if page_number not in (None, ''):
+        return f"{file_name} — Page {page_number}"
+    return file_name
+
+
+def _build_citation_counts(citations: List[Dict[str, Any]]) -> Dict[str, int]:
+    counts = {
+        'document': 0,
+        'web': 0,
+        'agent_tool': 0,
+        'legacy': 0,
+        'total': len(citations)
+    }
+    for citation in citations:
+        citation_type = citation.get('citation_type')
+        if citation_type in counts:
+            counts[citation_type] += 1
+    return counts
+
+
+def _curate_message_details(
+    message: Dict[str, Any],
+    citation_counts: Dict[str, int],
+    thought_count: int
+) -> Dict[str, Any]:
+    role = message.get('role', '')
+    metadata = message.get('metadata', {}) or {}
+    details: Dict[str, Any] = {}
+
+    if role == 'user':
+        details['interaction_mode'] = _remove_empty_values({
+            'button_states': metadata.get('button_states'),
+            'workspace_search': _curate_workspace_search(metadata.get('workspace_search')),
+            'prompt_selection': _curate_prompt_selection(metadata.get('prompt_selection')),
+            'agent_selection': _curate_agent_selection(metadata.get('agent_selection')),
+            'model_selection': _curate_model_selection(metadata.get('model_selection'))
+        })
+    elif role == 'assistant':
+        details['generation'] = _remove_empty_values({
+            'augmented': message.get('augmented'),
+            'model_deployment': message.get('model_deployment_name'),
+            'agent_name': message.get('agent_name'),
+            'agent_display_name': message.get('agent_display_name'),
+            'reasoning_effort': metadata.get('reasoning_effort'),
+            'hybrid_search_query': message.get('hybridsearch_query'),
+            'token_usage': _curate_token_usage(metadata.get('token_usage')),
+            'citation_counts': citation_counts,
+            'thought_count': thought_count
+        })
+    else:
+        details['message_context'] = _remove_empty_values({
+            'filename': message.get('filename'),
+            'prompt': message.get('prompt'),
+            'is_table': message.get('is_table'),
+            'model_deployment': message.get('model_deployment_name')
+        })
+
+    return _remove_empty_values(details)
+
+
+def _curate_workspace_search(workspace_search: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+    if not isinstance(workspace_search, dict):
+        return {}
+    return _remove_empty_values({
+        'search_enabled': workspace_search.get('search_enabled'),
+        'document_scope': workspace_search.get('document_scope'),
+        'document_name': workspace_search.get('document_name'),
+        'document_filename': workspace_search.get('document_filename'),
+        'group_name': workspace_search.get('group_name'),
+        'classification': workspace_search.get('classification'),
+        'public_workspace_id': workspace_search.get('active_public_workspace_id')
+    })
 
-                file_name = f"{safe_title}_{conv_id_short}.{ext}"
-                zf.writestr(file_name, file_content)
 
-        buffer.seek(0)
-        filename = f"conversations_export_{timestamp_str}.zip"
+def _curate_prompt_selection(prompt_selection: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+    if not isinstance(prompt_selection, dict):
+        return {}
+    return _remove_empty_values({
+        'prompt_name': prompt_selection.get('prompt_name'),
+        'selected_prompt_index': prompt_selection.get('selected_prompt_index'),
+        'selected_prompt_text': prompt_selection.get('selected_prompt_text')
+    })
 
-        response = make_response(buffer.read())
-        response.headers['Content-Type'] = 'application/zip'
-        response.headers['Content-Disposition'] = f'attachment; filename="{filename}"'
-        return response
 
-    def _conversation_to_markdown(entry):
-        """Convert a conversation + messages entry to Markdown format."""
-        conv = entry['conversation']
-        messages = entry['messages']
+def _curate_agent_selection(agent_selection: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+    if not isinstance(agent_selection, dict):
+        return {}
+    return _remove_empty_values({
+        'selected_agent': agent_selection.get('selected_agent'),
+        'agent_display_name': agent_selection.get('agent_display_name'),
+        'is_global': agent_selection.get('is_global'),
+        'is_group': agent_selection.get('is_group'),
+        'group_name': agent_selection.get('group_name')
+    })
+
+
+def _curate_model_selection(model_selection: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+    if not isinstance(model_selection, dict):
+        return {}
+    return _remove_empty_values({
+        'selected_model': model_selection.get('selected_model'),
+        'frontend_requested_model': model_selection.get('frontend_requested_model'),
+        'reasoning_effort': model_selection.get('reasoning_effort'),
+        'streaming': model_selection.get('streaming')
+    })
+
+
+def _curate_token_usage(token_usage: Any) -> Dict[str, Any]:
+    if not isinstance(token_usage, dict):
+        return {}
+    return _remove_empty_values({
+        'prompt_tokens': token_usage.get('prompt_tokens'),
+        'completion_tokens': token_usage.get('completion_tokens'),
+        'total_tokens': token_usage.get('total_tokens')
+    })
+
+
+def _remove_empty_values(value: Any) -> Any:
+    if isinstance(value, dict):
+        cleaned = {}
+        for key, item in value.items():
+            cleaned_item = _remove_empty_values(item)
+            if cleaned_item in (None, '', [], {}):
+                continue
+            cleaned[key] = cleaned_item
+        return cleaned
+
+    if isinstance(value, list):
+        cleaned_list = []
+        for item in value:
+            cleaned_item = _remove_empty_values(item)
+            if cleaned_item in (None, '', [], {}):
+                continue
+            cleaned_list.append(cleaned_item)
+        return cleaned_list
+
+    return value
+
+
+def generate_conversation_summary(
+    messages: List[Dict[str, Any]],
+    conversation_title: str,
+    settings: Dict[str, Any],
+    model_deployment: str,
+    message_time_start: str = None,
+    message_time_end: str = None,
+    conversation_id: str = None
+) -> Dict[str, Any]:
+    """Generate a conversation summary using the LLM and optionally persist it.
+
+    This is the shared helper used by both the export pipeline and the
+    on-demand summary API endpoint.  Returns a summary dict suitable for
+    storage in conversation metadata.
+
+    Raises ValueError when there is no content to summarise and
+    RuntimeError on model errors.
+    """
+    transcript_lines = []
+    for message in messages:
+        content_text = message.get('content_text', '')
+        if not content_text:
+            continue
+        role = message.get('role', 'unknown')
+        speaker = message.get('speaker_label', role).upper()
+        transcript_lines.append(f"{speaker}: {content_text}")
+
+    transcript_text = '\n\n'.join(transcript_lines).strip()
+    if not transcript_text:
+        raise ValueError('No message content was available to summarize.')
+
+    transcript_text = _truncate_for_summary(transcript_text)
+
+    gpt_client, gpt_model = _initialize_gpt_client(settings, model_deployment)
+    summary_prompt = (
+        "You are summarizing a conversation for an export document. "
+        "Read the full conversation below and write a concise summary. "
+        "Use your judgement on length: for short conversations write one brief paragraph, "
+        "for longer or more detailed conversations write two paragraphs. "
+        "If you need refer to the user, use their name, but do not refer to the user too often."
+        "Cover the goals, the key topics discussed, any data or tools referenced, "
+        "and the main outcomes or answers provided. "
+        "Be factual and neutral. Return plain text only — no headings, no bullet points, no markdown formatting."
+    )
+
+    model_lower = gpt_model.lower()
+    is_reasoning_model = (
+        'o1' in model_lower or 'o3' in model_lower or 'gpt-5' in model_lower
+    )
+    instruction_role = 'developer' if is_reasoning_model else 'system'
+
+    debug_print(f"Summary generation: sending {len(transcript_lines)} messages "
+                f"({len(transcript_text)} chars) to {gpt_model} (role={instruction_role})")
+
+    summary_response = gpt_client.chat.completions.create(
+        model=gpt_model,
+        messages=[
+            {
+                'role': instruction_role,
+                'content': summary_prompt
+            },
+            {
+                'role': 'user',
+                'content': (
+                    f"Conversation Title: {conversation_title}\n\n"
+                    f"{transcript_text}"
+                )
+            }
+        ]
+    )
+
+    debug_print(f"Summary generation: response choices="
+                f"{len(summary_response.choices) if summary_response.choices else 0}, "
+                f"finish_reason={summary_response.choices[0].finish_reason if summary_response.choices else 'N/A'}")
+
+    summary_text = (summary_response.choices[0].message.content or '').strip() if summary_response.choices else ''
+    if not summary_text:
+        debug_print('Summary generation: model returned an empty response')
+        log_event('Conversation summary generation returned empty response', level='WARNING')
+        raise RuntimeError('Summary model returned an empty response.')
+
+    summary_data = {
+        'content': summary_text,
+        'model_deployment': gpt_model,
+        'generated_at': datetime.utcnow().isoformat(),
+        'message_time_start': message_time_start,
+        'message_time_end': message_time_end
+    }
+
+    # Persist to Cosmos when a conversation_id is available
+    if conversation_id:
+        try:
+            update_conversation_with_metadata(conversation_id, {'summary': summary_data})
+            debug_print(f"Summary persisted to conversation {conversation_id}")
+        except Exception as persist_exc:
+            debug_print(f"Failed to persist summary to Cosmos: {persist_exc}")
+            log_event(f"Failed to persist conversation summary: {persist_exc}", level="WARNING")
+
+    return summary_data
+
+
+def _build_summary_intro(
+    messages: List[Dict[str, Any]],
+    conversation: Dict[str, Any],
+    sanitized_conversation: Dict[str, Any],
+    settings: Dict[str, Any],
+    enabled: bool,
+    summary_model_deployment: str,
+    message_time_start: str = None,
+    message_time_end: str = None
+) -> Dict[str, Any]:
+    """Build the summary_intro block for the export payload.
+
+    Uses cached summary from conversation metadata when present and
+    still current (no newer messages).  Otherwise generates a fresh
+    summary via ``generate_conversation_summary`` and persists it.
+    """
+    summary_intro = {
+        'enabled': enabled,
+        'generated': False,
+        'model_deployment': summary_model_deployment or None,
+        'generated_at': None,
+        'content': '',
+        'error': None
+    }
+
+    if not enabled:
+        return summary_intro
+
+    # Check for a cached summary stored in the conversation document
+    existing_summary = conversation.get('summary')
+    if existing_summary and isinstance(existing_summary, dict):
+        cached_end = existing_summary.get('message_time_end')
+        if cached_end and message_time_end and cached_end >= message_time_end:
+            debug_print('Export summary: using cached summary from conversation metadata')
+            summary_intro.update({
+                'generated': True,
+                'model_deployment': existing_summary.get('model_deployment'),
+                'generated_at': existing_summary.get('generated_at'),
+                'content': existing_summary.get('content', ''),
+                'error': None
+            })
+            return summary_intro
+        debug_print('Export summary: cached summary is stale, regenerating')
+
+    try:
+        conversation_id = conversation.get('id')
+        conversation_title = sanitized_conversation.get('title', 'Untitled')
+
+        summary_data = generate_conversation_summary(
+            messages=messages,
+            conversation_title=conversation_title,
+            settings=settings,
+            model_deployment=summary_model_deployment,
+            message_time_start=message_time_start,
+            message_time_end=message_time_end,
+            conversation_id=conversation_id
+        )
+
+        summary_intro.update({
+            'generated': True,
+            'model_deployment': summary_data.get('model_deployment'),
+            'generated_at': summary_data.get('generated_at'),
+            'content': summary_data.get('content', ''),
+            'error': None
+        })
+        return summary_intro
+
+    except (ValueError, RuntimeError) as known_exc:
+        debug_print(f"Export summary generation issue: {known_exc}")
+        summary_intro['error'] = str(known_exc)
+        if hasattr(known_exc, 'model_deployment'):
+            summary_intro['model_deployment'] = known_exc.model_deployment
+        return summary_intro
+
+    except Exception as exc:
+        debug_print(f"Export summary generation failed: {exc}")
+        log_event(f"Conversation export summary generation failed: {exc}", level="WARNING")
+        summary_intro['error'] = str(exc)
+        return summary_intro
+
+
+def _truncate_for_summary(transcript_text: str) -> str:
+    if len(transcript_text) <= SUMMARY_SOURCE_CHAR_LIMIT:
+        return transcript_text
+
+    head_chars = SUMMARY_SOURCE_CHAR_LIMIT // 2
+    tail_chars = SUMMARY_SOURCE_CHAR_LIMIT - head_chars
+    return (
+        transcript_text[:head_chars]
+        + "\n\n[... transcript truncated for export summary generation ...]\n\n"
+        + transcript_text[-tail_chars:]
+    )
+
+
+def _initialize_gpt_client(settings: Dict[str, Any], requested_model: str = ''):
+    enable_gpt_apim = settings.get('enable_gpt_apim', False)
+
+    if enable_gpt_apim:
+        raw_models = settings.get('azure_apim_gpt_deployment', '') or ''
+        apim_models = [model.strip() for model in raw_models.split(',') if model.strip()]
+        if not apim_models:
+            raise ValueError('APIM GPT deployment name is not configured.')
+
+        if requested_model and requested_model not in apim_models:
+            raise ValueError(f"Requested summary model '{requested_model}' is not configured for APIM.")
+
+        gpt_model = requested_model or apim_models[0]
+        gpt_client = AzureOpenAI(
+            api_version=settings.get('azure_apim_gpt_api_version'),
+            azure_endpoint=settings.get('azure_apim_gpt_endpoint'),
+            api_key=settings.get('azure_apim_gpt_subscription_key')
+        )
+        return gpt_client, gpt_model
+
+    auth_type = settings.get('azure_openai_gpt_authentication_type')
+    endpoint = settings.get('azure_openai_gpt_endpoint')
+    api_version = settings.get('azure_openai_gpt_api_version')
+    gpt_model_obj = settings.get('gpt_model', {}) or {}
+
+    if requested_model:
+        gpt_model = requested_model
+    elif gpt_model_obj.get('selected'):
+        gpt_model = gpt_model_obj['selected'][0]['deploymentName']
+    else:
+        raise ValueError('No GPT model selected or configured for export summary generation.')
+
+    if auth_type == 'managed_identity':
+        token_provider = get_bearer_token_provider(DefaultAzureCredential(), cognitive_services_scope)
+        gpt_client = AzureOpenAI(
+            api_version=api_version,
+            azure_endpoint=endpoint,
+            azure_ad_token_provider=token_provider
+        )
+    else:
+        api_key = settings.get('azure_openai_gpt_key')
+        if not api_key:
+            raise ValueError('Azure OpenAI API Key not configured.')
+        gpt_client = AzureOpenAI(
+            api_version=api_version,
+            azure_endpoint=endpoint,
+            api_key=api_key
+        )
+
+    return gpt_client, gpt_model
+
+
+def _build_single_file_response(exported: List[Dict[str, Any]], export_format: str, timestamp_str: str):
+    """Build a single-file download response."""
+    if export_format == 'json':
+        content = json.dumps(exported, indent=2, ensure_ascii=False, default=str)
+        filename = f"conversations_export_{timestamp_str}.json"
+        content_type = 'application/json; charset=utf-8'
+    elif export_format == 'pdf':
+        if len(exported) == 1:
+            content = _conversation_to_pdf_bytes(exported[0])
+        else:
+            combined_parts = []
+            for idx, entry in enumerate(exported):
+                if idx > 0:
+                    combined_parts.append(
+                        '<div style="margin-top: 24pt; border-top: 2px solid #999; '
+                        'padding-top: 12pt;"></div>'
+                    )
+                combined_parts.append(_build_pdf_html_body(entry))
+            content = _html_body_to_pdf_bytes('\n'.join(combined_parts))
+        filename = f"conversations_export_{timestamp_str}.pdf"
+        content_type = 'application/pdf'
+    else:
+        parts = []
+        for entry in exported:
+            parts.append(_conversation_to_markdown(entry))
+        content = '\n\n---\n\n'.join(parts)
+        filename = f"conversations_export_{timestamp_str}.md"
+        content_type = 'text/markdown; charset=utf-8'
+
+    response = make_response(content)
+    response.headers['Content-Type'] = content_type
+    response.headers['Content-Disposition'] = f'attachment; filename="{filename}"'
+    return response
+
+
+def _build_zip_response(exported: List[Dict[str, Any]], export_format: str, timestamp_str: str):
+    """Build a ZIP archive containing one file per conversation."""
+    buffer = io.BytesIO()
+    with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as zf:
+        for entry in exported:
+            conversation = entry['conversation']
+            safe_title = _safe_filename(conversation.get('title', 'Untitled'))
+            conversation_id_short = conversation.get('id', 'unknown')[:8]
+
+            if export_format == 'json':
+                file_content = json.dumps(entry, indent=2, ensure_ascii=False, default=str)
+                ext = 'json'
+            elif export_format == 'pdf':
+                file_content = _conversation_to_pdf_bytes(entry)
+                ext = 'pdf'
+            else:
+                file_content = _conversation_to_markdown(entry)
+                ext = 'md'
 
-        lines = []
-        title = conv.get('title', 'Untitled')
-        lines.append(f"# {title}")
+            file_name = f"{safe_title}_{conversation_id_short}.{ext}"
+            zf.writestr(file_name, file_content)
+
+    buffer.seek(0)
+    filename = f"conversations_export_{timestamp_str}.zip"
+
+    response = make_response(buffer.read())
+    response.headers['Content-Type'] = 'application/zip'
+    response.headers['Content-Disposition'] = f'attachment; filename="{filename}"'
+    return response
+
+
+def _conversation_to_markdown(entry: Dict[str, Any]) -> str:
+    """Convert a conversation + messages entry to Markdown format."""
+    conversation = entry['conversation']
+    messages = entry['messages']
+    summary_intro = entry.get('summary_intro', {}) or {}
+
+    transcript_messages = [message for message in messages if message.get('is_transcript_message')]
+    detail_messages = [message for message in messages if message.get('details')]
+    reference_messages = [message for message in messages if message.get('citations')]
+    thought_messages = [message for message in messages if message.get('thoughts')]
+    supplemental_messages = [message for message in messages if not message.get('is_transcript_message')]
+
+    lines: List[str] = []
+    lines.append(f"# {conversation.get('title', 'Untitled')}")
+    lines.append('')
+    lines.append(f"**Last Updated:** {conversation.get('last_updated', '')}  ")
+    lines.append(f"**Chat Type:** {conversation.get('chat_type', 'personal')}  ")
+    lines.append(f"**Messages:** {conversation.get('message_count', len(messages))}  ")
+    if conversation.get('tags'):
+        lines.append(f"**Tags:** {', '.join(_format_tag(tag) for tag in conversation.get('tags', []))}  ")
+    if conversation.get('classification'):
+        lines.append(f"**Classification:** {', '.join(_format_tag(item) for item in conversation.get('classification', []))}  ")
+    lines.append('')
+
+    if summary_intro.get('enabled') and summary_intro.get('generated') and summary_intro.get('content'):
+        lines.append('## Abstract')
         lines.append('')
+        lines.append(summary_intro.get('content', ''))
+        lines.append('')
+        lines.append(f"_Generated with {summary_intro.get('model_deployment') or 'configured model'} on {summary_intro.get('generated_at')}_")
+        lines.append('')
+    elif summary_intro.get('enabled') and summary_intro.get('error'):
+        lines.append('> _A summary intro was requested, but it could not be generated for this export._')
+        lines.append(f"> _Error: {summary_intro.get('error')}_")
+        lines.append('')
+
+    lines.append('## Transcript')
+    lines.append('')
+    if not transcript_messages:
+        lines.append('_No user or assistant transcript messages were available for export._')
+        lines.append('')
+    else:
+        for message in transcript_messages:
+            lines.append(f"### {message.get('label')} — {message.get('speaker_label')}")
+            if message.get('timestamp'):
+                lines.append(f"*{message.get('timestamp')}*")
+            lines.append('')
+            lines.append(message.get('content_text') or '_No content recorded._')
+            lines.append('')
 
-        # Metadata
-        last_updated = conv.get('last_updated', '')
-        chat_type = conv.get('chat_type', 'personal')
-        tags = conv.get('tags', [])
-
-        lines.append(f"**Last Updated:** {last_updated}  ")
-        lines.append(f"**Chat Type:** {chat_type}  ")
-        if tags:
-            tag_strs = [str(t) for t in tags]
-            lines.append(f"**Tags:** {', '.join(tag_strs)}  ")
-        lines.append(f"**Messages:** {len(messages)}  ")
+    lines.append('## Appendix A — Conversation Metadata')
+    lines.append('')
+    metadata_to_render = _remove_empty_values({
+        'context': conversation.get('context'),
+        'classification': conversation.get('classification'),
+        'strict': conversation.get('strict'),
+        'is_pinned': conversation.get('is_pinned'),
+        'scope_locked': conversation.get('scope_locked'),
+        'locked_contexts': conversation.get('locked_contexts'),
+        'message_counts_by_role': conversation.get('message_counts_by_role'),
+        'citation_counts': conversation.get('citation_counts'),
+        'thought_count': conversation.get('thought_count')
+    })
+    _append_markdown_mapping(lines, metadata_to_render)
+    lines.append('')
+
+    if detail_messages:
+        lines.append('## Appendix B — Message Details')
         lines.append('')
-        lines.append('---')
+        for message in detail_messages:
+            lines.append(f"### {message.get('label')} — {message.get('speaker_label')}")
+            if message.get('timestamp'):
+                lines.append(f"*{message.get('timestamp')}*")
+            lines.append('')
+            _append_markdown_mapping(lines, message.get('details', {}))
+            lines.append('')
+
+    if reference_messages:
+        lines.append('## Appendix C — References')
         lines.append('')
+        for message in reference_messages:
+            lines.append(f"### {message.get('label')} — {message.get('speaker_label')}")
+            if message.get('timestamp'):
+                lines.append(f"*{message.get('timestamp')}*")
+            lines.append('')
+            _append_citations_markdown(lines, message)
+            lines.append('')
 
-        # Messages
-        for msg in messages:
-            role = msg.get('role', 'unknown')
-            timestamp = msg.get('timestamp', '')
-            raw_content = msg.get('content', '')
-            content = _normalize_content(raw_content)
-
-            role_label = role.capitalize()
-            if role == 'assistant':
-                role_label = 'Assistant'
-            elif role == 'user':
-                role_label = 'User'
-            elif role == 'system':
-                role_label = 'System'
-            elif role == 'tool':
-                role_label = 'Tool'
-
-            lines.append(f"### {role_label}")
-            if timestamp:
-                lines.append(f"*{timestamp}*")
+    if thought_messages:
+        lines.append('## Appendix D — Processing Thoughts')
+        lines.append('')
+        for message in thought_messages:
+            lines.append(f"### {message.get('label')} — {message.get('speaker_label')}")
+            if message.get('timestamp'):
+                lines.append(f"*{message.get('timestamp')}*")
             lines.append('')
-            lines.append(content)
+            for thought in message.get('thoughts', []):
+                thought_label = thought.get('step_type', 'step').replace('_', ' ').title()
+                lines.append(f"1. **{thought_label}:** {thought.get('content') or 'No content recorded.'}")
+                if thought.get('duration_ms') is not None:
+                    lines.append(f"   - **Duration:** {thought.get('duration_ms')} ms")
+                if thought.get('timestamp'):
+                    lines.append(f"   - **Timestamp:** {thought.get('timestamp')}")
+                if thought.get('detail'):
+                    lines.append('   - **Detail:**')
+                    _append_code_block(lines, thought.get('detail'), indent='     ')
             lines.append('')
 
-            # Citations
-            citations = msg.get('citations')
-            if citations:
-                lines.append('**Citations:**')
-                if isinstance(citations, list):
-                    for cit in citations:
-                        if isinstance(cit, dict):
-                            source = cit.get('title') or cit.get('filepath') or cit.get('url', 'Unknown')
-                            lines.append(f"- {source}")
-                        else:
-                            lines.append(f"- {cit}")
-                lines.append('')
-
-            lines.append('---')
+    if supplemental_messages:
+        lines.append('## Appendix E — Supplemental Messages')
+        lines.append('')
+        for message in supplemental_messages:
+            lines.append(f"### {message.get('label')} — {message.get('speaker_label')}")
+            if message.get('timestamp'):
+                lines.append(f"*{message.get('timestamp')}*")
+            lines.append('')
+            lines.append(message.get('content_text') or '_No content recorded._')
             lines.append('')
 
-        return '\n'.join(lines)
+    return '\n'.join(lines).strip()
 
-    def _normalize_content(content):
-        """Normalize message content to a plain string.
-        
-        Content may be a string, a list of content-part dicts
-        (e.g. [{"type": "text", "text": "..."}, ...]), or a dict.
-        """
-        if isinstance(content, str):
-            return content
-        if isinstance(content, list):
-            parts = []
-            for item in content:
-                if isinstance(item, dict):
-                    if item.get('type') == 'text':
-                        parts.append(item.get('text', ''))
-                    elif item.get('type') == 'image_url':
-                        parts.append('[Image]')
+
+def _append_citations_markdown(lines: List[str], message: Dict[str, Any]):
+    document_citations = [citation for citation in message.get('citations', []) if citation.get('citation_type') == 'document']
+    web_citations = [citation for citation in message.get('citations', []) if citation.get('citation_type') == 'web']
+    agent_citations = message.get('agent_citations', []) or []
+    legacy_citations = [citation for citation in message.get('citations', []) if citation.get('citation_type') == 'legacy']
+
+    if not any([document_citations, web_citations, agent_citations, legacy_citations]):
+        lines.append('_No citations were recorded for this message._')
+        return
+
+    if document_citations:
+        lines.append('#### Document Sources')
+        lines.append('')
+        for index, citation in enumerate(document_citations, start=1):
+            lines.append(f"{index}. **{citation.get('label', 'Document source')}**")
+            detail_mapping = _remove_empty_values({
+                'citation_id': citation.get('citation_id'),
+                'page_number': citation.get('page_number'),
+                'classification': citation.get('classification'),
+                'score': citation.get('score'),
+                'metadata_type': citation.get('metadata_type')
+            })
+            _append_markdown_mapping(lines, detail_mapping, indent=1)
+            if citation.get('metadata_content'):
+                lines.append('   - **Metadata Content:**')
+                _append_code_block(lines, citation.get('metadata_content'), indent='     ')
+        lines.append('')
+
+    if web_citations:
+        lines.append('#### Web Sources')
+        lines.append('')
+        for index, citation in enumerate(web_citations, start=1):
+            title = citation.get('title') or citation.get('label') or 'Web source'
+            url = citation.get('url')
+            if url:
+                lines.append(f"{index}. [{title}]({url})")
+            else:
+                lines.append(f"{index}. {title}")
+        lines.append('')
+
+    if agent_citations:
+        lines.append('#### Tool Invocations')
+        lines.append('')
+        for index, citation in enumerate(agent_citations, start=1):
+            label = citation.get('tool_name') or citation.get('function_name') or f"Tool {index}"
+            lines.append(f"{index}. **{label}**")
+            detail_mapping = _remove_empty_values({
+                'function_name': citation.get('function_name'),
+                'plugin_name': citation.get('plugin_name'),
+                'success': citation.get('success'),
+                'timestamp': citation.get('timestamp')
+            })
+            _append_markdown_mapping(lines, detail_mapping, indent=1)
+            if citation.get('function_arguments') not in (None, '', [], {}):
+                lines.append('   - **Arguments:**')
+                _append_code_block(lines, citation.get('function_arguments'), indent='     ')
+            if citation.get('function_result') not in (None, '', [], {}):
+                lines.append('   - **Result:**')
+                _append_code_block(lines, citation.get('function_result'), indent='     ')
+        lines.append('')
+
+    if legacy_citations:
+        lines.append('#### Legacy Citation Records')
+        lines.append('')
+        for index, citation in enumerate(legacy_citations, start=1):
+            lines.append(f"{index}. {citation.get('label', 'Legacy citation')}")
+        lines.append('')
+
+
+def _append_markdown_mapping(lines: List[str], mapping: Dict[str, Any], indent: int = 0):
+    if not isinstance(mapping, dict) or not mapping:
+        return
+
+    prefix = '  ' * indent
+    for key, value in mapping.items():
+        label = _format_markdown_key(key)
+        if isinstance(value, dict):
+            lines.append(f"{prefix}- **{label}:**")
+            _append_markdown_mapping(lines, value, indent + 1)
+        elif isinstance(value, list):
+            if not value:
+                continue
+            if all(not isinstance(item, (dict, list)) for item in value):
+                lines.append(f"{prefix}- **{label}:** {', '.join(_stringify_markdown_value(item) for item in value)}")
+            else:
+                lines.append(f"{prefix}- **{label}:**")
+                for item in value:
+                    if isinstance(item, dict):
+                        lines.append(f"{prefix}  -")
+                        _append_markdown_mapping(lines, item, indent + 2)
                     else:
-                        parts.append(str(item))
+                        lines.append(f"{prefix}  - {_stringify_markdown_value(item)}")
+        else:
+            lines.append(f"{prefix}- **{label}:** {_stringify_markdown_value(value)}")
+
+
+def _append_code_block(lines: List[str], value: Any, indent: str = ''):
+    if isinstance(value, (dict, list)):
+        code_block = json.dumps(value, indent=2, ensure_ascii=False, default=str)
+        language = 'json'
+    else:
+        code_block = str(value)
+        language = 'text'
+
+    lines.append(f"{indent}```{language}")
+    for line in code_block.splitlines() or ['']:
+        lines.append(f"{indent}{line}")
+    lines.append(f"{indent}```")
+
+
+def _format_markdown_key(key: str) -> str:
+    return str(key).replace('_', ' ').title()
+
+
+def _stringify_markdown_value(value: Any) -> str:
+    if isinstance(value, bool):
+        return 'Yes' if value else 'No'
+    return str(value)
+
+
+def _format_tag(tag: Any) -> str:
+    """Format a tag or classification entry for display.
+
+    Tags in Cosmos are stored as dicts such as
+    ``{'category': 'model', 'value': 'gpt-5'}`` or
+    ``{'category': 'participant', 'name': 'Alice', 'user_id': '...'}``
+    but they can also be plain strings in older data.
+    """
+    if isinstance(tag, dict):
+        category = tag.get('category', '')
+        # Participant tags carry a readable name / email
+        name = tag.get('name') or tag.get('email') or tag.get('display_name')
+        if name:
+            return f"{category}: {name}" if category else str(name)
+        # Document tags carry a title
+        title = tag.get('title') or tag.get('document_id')
+        if title:
+            return f"{category}: {title}" if category else str(title)
+        # Generic category/value tags
+        value = tag.get('value')
+        if value:
+            return f"{category}: {value}" if category else str(value)
+        return category or str(tag)
+    return str(tag)
+
+
+def _role_to_label(role: str) -> str:
+    role_map = {
+        'assistant': 'Assistant',
+        'user': 'User',
+        'system': 'System',
+        'tool': 'Tool',
+        'file': 'File',
+        'image': 'Image',
+        'safety': 'Safety',
+        'blocked': 'Blocked'
+    }
+    return role_map.get(role, str(role).capitalize() or 'Message')
+
+
+def _normalize_content(content: Any) -> str:
+    """Normalize message content to a plain string."""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        parts = []
+        for item in content:
+            if isinstance(item, dict):
+                if item.get('type') == 'text':
+                    parts.append(item.get('text', ''))
+                elif item.get('type') == 'image_url':
+                    parts.append('[Image]')
                 else:
                     parts.append(str(item))
-            return '\n'.join(parts)
-        if isinstance(content, dict):
-            if content.get('type') == 'text':
-                return content.get('text', '')
-            return str(content)
-        return str(content) if content else ''
-
-    def _safe_filename(title):
-        """Create a filesystem-safe filename from a conversation title."""
-        import re
-        # Remove or replace unsafe characters
-        safe = re.sub(r'[<>:"/\\|?*]', '_', title)
-        safe = re.sub(r'\s+', '_', safe)
-        safe = safe.strip('_. ')
-        # Truncate to reasonable length
-        if len(safe) > 50:
-            safe = safe[:50]
-        return safe or 'Untitled'
+            else:
+                parts.append(str(item))
+        return '\n'.join(parts)
+    if isinstance(content, dict):
+        if content.get('type') == 'text':
+            return content.get('text', '')
+        return str(content)
+    return str(content) if content else ''
+
+
+def _safe_filename(title: str) -> str:
+    """Create a filesystem-safe filename from a conversation title."""
+    safe = re.sub(r'[<>:"/\\|?*]', '_', title)
+    safe = re.sub(r'\s+', '_', safe)
+    safe = safe.strip('_. ')
+    if len(safe) > 50:
+        safe = safe[:50]
+    return safe or 'Untitled'
+
+
+# ---------------------------------------------------------------------------
+# PDF Export — HTML generation and PyMuPDF Story rendering
+# ---------------------------------------------------------------------------
+
+_PDF_CSS = """
+body {
+    font-family: sans-serif;
+    font-size: 10pt;
+    color: #222;
+    line-height: 1.4;
+}
+h1 {
+    font-size: 16pt;
+    color: #1a1a2e;
+    margin-bottom: 2pt;
+}
+h2 {
+    font-size: 13pt;
+    color: #16213e;
+    margin-top: 16pt;
+    margin-bottom: 6pt;
+    border-bottom: 1px solid #ccc;
+    padding-bottom: 4pt;
+}
+h3 {
+    font-size: 11pt;
+    color: #0f3460;
+    margin-top: 10pt;
+    margin-bottom: 4pt;
+}
+h4 {
+    font-size: 10pt;
+    color: #333;
+    margin-top: 8pt;
+    margin-bottom: 4pt;
+}
+p {
+    margin-top: 2pt;
+    margin-bottom: 4pt;
+}
+.metadata {
+    font-size: 8pt;
+    color: #666;
+}
+.abstract {
+    background-color: #f8f9fa;
+    padding: 8pt;
+    margin-bottom: 8pt;
+}
+.note {
+    font-size: 9pt;
+    color: #856404;
+    background-color: #fff3cd;
+    padding: 6pt;
+}
+.bubble {
+    padding: 8pt 12pt;
+    margin-bottom: 8pt;
+}
+.bubble-header {
+    font-size: 8pt;
+    color: #444;
+    margin-bottom: 2pt;
+}
+.ts {
+    font-weight: normal;
+    color: #888;
+}
+.user-bubble {
+    background-color: #c8e0fa;
+    margin-left: 60pt;
+}
+.assistant-bubble {
+    background-color: #f1f0f0;
+    margin-right: 60pt;
+}
+.system-bubble {
+    background-color: #fff3cd;
+    margin-left: 30pt;
+    margin-right: 30pt;
+    font-size: 9pt;
+}
+.file-bubble {
+    background-color: #e8f5e9;
+    margin-right: 60pt;
+    font-size: 9pt;
+}
+.other-bubble {
+    background-color: #f5f5f5;
+    margin-left: 30pt;
+    margin-right: 30pt;
+    font-size: 9pt;
+}
+table {
+    border-collapse: collapse;
+    width: 100%;
+    font-size: 9pt;
+    margin-bottom: 8pt;
+}
+th, td {
+    border: 1px solid #ddd;
+    padding: 4pt 6pt;
+    text-align: left;
+}
+th {
+    background-color: #f5f5f5;
+    font-weight: bold;
+}
+pre {
+    background-color: #f5f5f5;
+    padding: 6pt;
+    font-size: 8pt;
+    font-family: monospace;
+}
+code {
+    font-family: monospace;
+    font-size: 9pt;
+    background-color: #f0f0f0;
+    padding: 1pt 3pt;
+}
+ol, ul {
+    margin-top: 4pt;
+    margin-bottom: 8pt;
+}
+li {
+    margin-bottom: 4pt;
+}
+small {
+    font-size: 8pt;
+    color: #666;
+}
+a {
+    color: #0066cc;
+}
+"""
+
+
+def _pdf_bubble_class(role: str) -> str:
+    """Return the CSS class for a chat bubble based on message role."""
+    role_classes = {
+        'user': 'user-bubble',
+        'assistant': 'assistant-bubble',
+        'system': 'system-bubble',
+        'file': 'file-bubble',
+        'image': 'file-bubble'
+    }
+    return role_classes.get(role, 'other-bubble')
+
+
+def _build_pdf_html_body(entry: Dict[str, Any]) -> str:
+    """Build the HTML body content for a single conversation PDF."""
+    conversation = entry['conversation']
+    messages = entry['messages']
+    summary_intro = entry.get('summary_intro', {}) or {}
+
+    transcript_messages = [m for m in messages if m.get('is_transcript_message')]
+    detail_messages = [m for m in messages if m.get('details')]
+    reference_messages = [m for m in messages if m.get('citations')]
+    thought_messages = [m for m in messages if m.get('thoughts')]
+    supplemental_messages = [m for m in messages if not m.get('is_transcript_message')]
+
+    parts: List[str] = []
+
+    # --- Title and metadata ---
+    parts.append(f'<h1>{_escape_html(conversation.get("title", "Untitled"))}</h1>')
+    meta_items = [
+        f'<b>Last Updated:</b> {_escape_html(str(conversation.get("last_updated", "")))}',
+        f'<b>Chat Type:</b> {_escape_html(str(conversation.get("chat_type", "personal")))}',
+        f'<b>Messages:</b> {conversation.get("message_count", len(messages))}'
+    ]
+    tags = conversation.get('tags')
+    if tags:
+        meta_items.append(f'<b>Tags:</b> {_escape_html(", ".join(_format_tag(t) for t in tags))}')
+    classification = conversation.get('classification')
+    if classification:
+        meta_items.append(
+            f'<b>Classification:</b> {_escape_html(", ".join(_format_tag(c) for c in classification))}'
+        )
+    parts.append(f'<p class="metadata">{" &nbsp;|&nbsp; ".join(meta_items)}</p>')
+
+    # --- Abstract ---
+    if summary_intro.get('enabled') and summary_intro.get('generated') and summary_intro.get('content'):
+        parts.append('<h2>Abstract</h2>')
+        abstract_html = markdown2.markdown(
+            summary_intro.get('content', ''),
+            extras=['fenced-code-blocks', 'tables']
+        )
+        parts.append(f'<div class="abstract">{abstract_html}</div>')
+        parts.append(
+            f'<p class="metadata"><i>Generated with '
+            f'{_escape_html(str(summary_intro.get("model_deployment") or "configured model"))} on '
+            f'{_escape_html(str(summary_intro.get("generated_at", "")))}</i></p>'
+        )
+    elif summary_intro.get('enabled') and summary_intro.get('error'):
+        error_text = _escape_html(str(summary_intro.get('error', '')))
+        parts.append(
+            '<p class="note"><i>A summary intro was requested, '
+            'but could not be generated for this export.</i><br/>'
+            f'<small>Error: {error_text}</small></p>'
+        )
+
+    # --- Transcript with chat bubbles ---
+    parts.append('<h2>Transcript</h2>')
+    if not transcript_messages:
+        parts.append(
+            '<p><i>No user or assistant transcript messages were available for export.</i></p>'
+        )
+    else:
+        for message in transcript_messages:
+            role = message.get('role', '')
+            bubble_class = _pdf_bubble_class(role)
+            label = message.get('label', '')
+            speaker = message.get('speaker_label', '')
+            timestamp = message.get('timestamp', '')
+            content = message.get('content_text', '') or 'No content recorded.'
+
+            parts.append(f'<div class="bubble {bubble_class}">')
+            ts_str = (
+                f' &nbsp;|&nbsp; <span class="ts">{_escape_html(str(timestamp))}</span>'
+                if timestamp else ''
+            )
+            parts.append(
+                f'<p class="bubble-header"><b>{_escape_html(label)} — '
+                f'{_escape_html(speaker)}</b>{ts_str}</p>'
+            )
+            content_html = markdown2.markdown(
+                content,
+                extras=['fenced-code-blocks', 'tables', 'break-on-newline']
+            )
+            parts.append(content_html)
+            parts.append('</div>')
+
+    # --- Appendix A: Conversation Metadata ---
+    parts.append('<h2>Appendix A — Conversation Metadata</h2>')
+    metadata_to_render = _remove_empty_values({
+        'context': conversation.get('context'),
+        'classification': conversation.get('classification'),
+        'strict': conversation.get('strict'),
+        'is_pinned': conversation.get('is_pinned'),
+        'scope_locked': conversation.get('scope_locked'),
+        'locked_contexts': conversation.get('locked_contexts'),
+        'message_counts_by_role': conversation.get('message_counts_by_role'),
+        'citation_counts': conversation.get('citation_counts'),
+        'thought_count': conversation.get('thought_count')
+    })
+    _append_html_table(parts, metadata_to_render)
+
+    # --- Appendix B: Message Details ---
+    if detail_messages:
+        parts.append('<h2>Appendix B — Message Details</h2>')
+        for message in detail_messages:
+            parts.append(
+                f'<h3>{_escape_html(message.get("label", ""))} — '
+                f'{_escape_html(message.get("speaker_label", ""))}</h3>'
+            )
+            if message.get('timestamp'):
+                parts.append(
+                    f'<p class="metadata"><i>{_escape_html(str(message.get("timestamp")))}</i></p>'
+                )
+            _append_html_table(parts, message.get('details', {}))
+
+    # --- Appendix C: References ---
+    if reference_messages:
+        parts.append('<h2>Appendix C — References</h2>')
+        for message in reference_messages:
+            parts.append(
+                f'<h3>{_escape_html(message.get("label", ""))} — '
+                f'{_escape_html(message.get("speaker_label", ""))}</h3>'
+            )
+            if message.get('timestamp'):
+                parts.append(
+                    f'<p class="metadata"><i>{_escape_html(str(message.get("timestamp")))}</i></p>'
+                )
+            _append_html_citations(parts, message)
+
+    # --- Appendix D: Processing Thoughts ---
+    if thought_messages:
+        parts.append('<h2>Appendix D — Processing Thoughts</h2>')
+        for message in thought_messages:
+            parts.append(
+                f'<h3>{_escape_html(message.get("label", ""))} — '
+                f'{_escape_html(message.get("speaker_label", ""))}</h3>'
+            )
+            if message.get('timestamp'):
+                parts.append(
+                    f'<p class="metadata"><i>{_escape_html(str(message.get("timestamp")))}</i></p>'
+                )
+            parts.append('<ol>')
+            for thought in message.get('thoughts', []):
+                thought_label = (thought.get('step_type') or 'step').replace('_', ' ').title()
+                parts.append(
+                    f'<li><b>{_escape_html(thought_label)}:</b> '
+                    f'{_escape_html(str(thought.get("content") or "No content recorded."))}'
+                )
+                if thought.get('duration_ms') is not None:
+                    parts.append(
+                        f'<br/><small><b>Duration:</b> {thought.get("duration_ms")} ms</small>'
+                    )
+                if thought.get('timestamp'):
+                    parts.append(
+                        f'<br/><small><b>Timestamp:</b> '
+                        f'{_escape_html(str(thought.get("timestamp")))}</small>'
+                    )
+                if thought.get('detail'):
+                    parts.append('<br/><small><b>Detail:</b></small>')
+                    _append_html_code_block(parts, thought.get('detail'))
+                parts.append('</li>')
+            parts.append('</ol>')
+
+    # --- Appendix E: Supplemental Messages ---
+    if supplemental_messages:
+        parts.append('<h2>Appendix E — Supplemental Messages</h2>')
+        for message in supplemental_messages:
+            parts.append(
+                f'<h3>{_escape_html(message.get("label", ""))} — '
+                f'{_escape_html(message.get("speaker_label", ""))}</h3>'
+            )
+            if message.get('timestamp'):
+                parts.append(
+                    f'<p class="metadata"><i>{_escape_html(str(message.get("timestamp")))}</i></p>'
+                )
+            content = message.get('content_text', '') or 'No content recorded.'
+            content_html = markdown2.markdown(
+                content,
+                extras=['fenced-code-blocks', 'tables', 'break-on-newline']
+            )
+            parts.append(content_html)
+
+    return '\n'.join(parts)
+
+
+def _render_pdf_bytes(body_html: str) -> bytes:
+    """Render HTML body content to PDF bytes using PyMuPDF Story API."""
+    MEDIABOX = fitz.paper_rect("letter")
+    WHERE = MEDIABOX + (36, 36, -36, -36)
+
+    story = fitz.Story(html=body_html, user_css=_PDF_CSS)
+
+    tmp_path = None
+    try:
+        with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as tmp:
+            tmp_path = tmp.name
+
+        writer = fitz.DocumentWriter(tmp_path)
+        more = True
+        while more:
+            device = writer.begin_page(MEDIABOX)
+            more, _ = story.place(WHERE)
+            story.draw(device)
+            writer.end_page()
+        writer.close()
+        del story
+        del writer
+
+        with open(tmp_path, 'rb') as f:
+            return f.read()
+    finally:
+        if tmp_path:
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+
+
+def _conversation_to_pdf_bytes(entry: Dict[str, Any]) -> bytes:
+    """Convert a conversation export entry to PDF bytes."""
+    body_html = _build_pdf_html_body(entry)
+    return _render_pdf_bytes(body_html)
+
+
+def _html_body_to_pdf_bytes(body_html: str) -> bytes:
+    """Convert raw HTML body content to PDF bytes."""
+    return _render_pdf_bytes(body_html)
+
+
+def _append_html_table(parts: List[str], mapping: Dict[str, Any]):
+    """Append a key-value mapping as an HTML table."""
+    if not isinstance(mapping, dict) or not mapping:
+        parts.append('<p><i>No data available.</i></p>')
+        return
+
+    parts.append('<table>')
+    parts.append('<tr><th>Property</th><th>Value</th></tr>')
+    for key, value in mapping.items():
+        label = _format_markdown_key(key)
+        if isinstance(value, dict):
+            formatted = _format_nested_html_value(value)
+        elif isinstance(value, list):
+            formatted = (
+                ', '.join(_escape_html(str(item)) for item in value)
+                if value else '<i>None</i>'
+            )
+        elif isinstance(value, bool):
+            formatted = 'Yes' if value else 'No'
+        else:
+            formatted = _escape_html(str(value))
+        parts.append(f'<tr><td><b>{_escape_html(label)}</b></td><td>{formatted}</td></tr>')
+    parts.append('</table>')
+
+
+def _format_nested_html_value(mapping: Dict[str, Any], depth: int = 0) -> str:
+    """Format a nested dict as an HTML string for table cells."""
+    if not mapping:
+        return '<i>None</i>'
+
+    items = []
+    for key, value in mapping.items():
+        label = _format_markdown_key(key)
+        if isinstance(value, dict):
+            nested = _format_nested_html_value(value, depth + 1)
+            items.append(f'<b>{_escape_html(label)}:</b><br/>{nested}')
+        elif isinstance(value, list):
+            list_str = (
+                ', '.join(_escape_html(str(v)) for v in value)
+                if value else 'None'
+            )
+            items.append(f'<b>{_escape_html(label)}:</b> {list_str}')
+        elif isinstance(value, bool):
+            items.append(f'<b>{_escape_html(label)}:</b> {"Yes" if value else "No"}')
+        else:
+            items.append(f'<b>{_escape_html(label)}:</b> {_escape_html(str(value))}')
+    return '<br/>'.join(items)
+
+
+def _append_html_citations(parts: List[str], message: Dict[str, Any]):
+    """Append citation data as HTML."""
+    citations = message.get('citations', [])
+    if not citations:
+        parts.append('<p><i>No citations were recorded for this message.</i></p>')
+        return
+
+    doc_citations = [c for c in citations if c.get('citation_type') == 'document']
+    web_citations = [c for c in citations if c.get('citation_type') == 'web']
+    agent_citations = [c for c in citations if c.get('citation_type') == 'agent_tool']
+    legacy_citations = [c for c in citations if c.get('citation_type') == 'legacy']
+
+    if doc_citations:
+        parts.append('<h4>Document Sources</h4>')
+        parts.append('<ol>')
+        for citation in doc_citations:
+            parts.append(
+                f'<li><b>{_escape_html(str(citation.get("label", "Document source")))}</b>'
+            )
+            detail_items = _remove_empty_values({
+                'citation_id': citation.get('citation_id'),
+                'page_number': citation.get('page_number'),
+                'classification': citation.get('classification'),
+                'score': citation.get('score'),
+                'metadata_type': citation.get('metadata_type')
+            })
+            if detail_items:
+                detail_str = '; '.join(
+                    f'{_format_markdown_key(k)}: {_escape_html(str(v))}'
+                    for k, v in detail_items.items()
+                )
+                parts.append(f'<br/><small>{detail_str}</small>')
+            if citation.get('metadata_content'):
+                parts.append('<br/><small><b>Metadata Content:</b></small>')
+                _append_html_code_block(parts, citation.get('metadata_content'))
+            parts.append('</li>')
+        parts.append('</ol>')
+
+    if web_citations:
+        parts.append('<h4>Web Sources</h4>')
+        parts.append('<ol>')
+        for citation in web_citations:
+            title = _escape_html(
+                str(citation.get('title') or citation.get('label') or 'Web source')
+            )
+            url = citation.get('url')
+            if url:
+                parts.append(f'<li><a href="{_escape_html(url)}">{title}</a></li>')
+            else:
+                parts.append(f'<li>{title}</li>')
+        parts.append('</ol>')
+
+    if agent_citations:
+        parts.append('<h4>Tool Invocations</h4>')
+        parts.append('<ol>')
+        for citation in agent_citations:
+            label = _escape_html(
+                str(citation.get('tool_name') or citation.get('function_name') or 'Tool')
+            )
+            parts.append(f'<li><b>{label}</b>')
+            detail_items = _remove_empty_values({
+                'function_name': citation.get('function_name'),
+                'plugin_name': citation.get('plugin_name'),
+                'success': citation.get('success'),
+                'timestamp': citation.get('timestamp')
+            })
+            if detail_items:
+                detail_str = '; '.join(
+                    f'{_format_markdown_key(k)}: {_escape_html(str(v))}'
+                    for k, v in detail_items.items()
+                )
+                parts.append(f'<br/><small>{detail_str}</small>')
+            parts.append('</li>')
+        parts.append('</ol>')
+
+    if legacy_citations:
+        parts.append('<h4>Legacy Citation Records</h4>')
+        parts.append('<ol>')
+        for citation in legacy_citations:
+            parts.append(
+                f'<li>{_escape_html(str(citation.get("label", "Legacy citation")))}</li>'
+            )
+        parts.append('</ol>')
+
+
+def _append_html_code_block(parts: List[str], value: Any):
+    """Append a code block in HTML format."""
+    if isinstance(value, (dict, list)):
+        code_text = json.dumps(value, indent=2, ensure_ascii=False, default=str)
+    else:
+        code_text = str(value)
+    parts.append(f'<pre>{_escape_html(code_text)}</pre>')
diff --git a/application/single_app/route_backend_conversations.py b/application/single_app/route_backend_conversations.py
index ed15cb91..d90d7e4a 100644
--- a/application/single_app/route_backend_conversations.py
+++ b/application/single_app/route_backend_conversations.py
@@ -3,11 +3,12 @@
 from config import *
 from functions_authentication import *
 from functions_settings import *
-from functions_conversation_metadata import get_conversation_metadata
+from functions_conversation_metadata import get_conversation_metadata, update_conversation_with_metadata
 from flask import Response, request
 from functions_debug import debug_print
 from swagger_wrapper import swagger_route, get_auth_security
 from functions_activity_logging import log_conversation_creation, log_conversation_deletion, log_conversation_archival
+from functions_thoughts import archive_thoughts_for_conversation, delete_thoughts_for_conversation
 
 def register_route_backend_conversations(app):
 
@@ -430,7 +431,14 @@ def delete_conversation(conversation_id):
                 cosmos_archived_messages_container.upsert_item(archived_doc)
 
             cosmos_messages_container.delete_item(doc['id'], partition_key=conversation_id)
-        
+
+        # Archive/delete thoughts for conversation
+        user_id_for_thoughts = conversation_item.get('user_id')
+        if archiving_enabled:
+            archive_thoughts_for_conversation(conversation_id, user_id_for_thoughts)
+        else:
+            delete_thoughts_for_conversation(conversation_id, user_id_for_thoughts)
+
         # Log conversation deletion before actual deletion
         log_conversation_deletion(
             user_id=conversation_item.get('user_id'),
@@ -530,7 +538,13 @@ def delete_multiple_conversations():
                         cosmos_archived_messages_container.upsert_item(archived_message)
                     
                     cosmos_messages_container.delete_item(message['id'], partition_key=conversation_id)
-                
+
+                # Archive/delete thoughts for conversation
+                if archiving_enabled:
+                    archive_thoughts_for_conversation(conversation_id, user_id)
+                else:
+                    delete_thoughts_for_conversation(conversation_id, user_id)
+
                 # Log conversation deletion before actual deletion
                 log_conversation_deletion(
                     user_id=user_id,
@@ -798,7 +812,8 @@ def get_conversation_metadata_api(conversation_id):
                 "is_hidden": conversation_item.get('is_hidden', False),
                 "scope_locked": conversation_item.get('scope_locked'),
                 "locked_contexts": conversation_item.get('locked_contexts', []),
-                "chat_type": conversation_item.get('chat_type', 'personal') # Default to 'personal' if chat_type is not defined (legacy conversations)
+                "chat_type": conversation_item.get('chat_type'),
+                "summary": conversation_item.get('summary')
             }), 200
             
         except CosmosResourceNotFoundError:
@@ -807,6 +822,95 @@ def get_conversation_metadata_api(conversation_id):
             print(f"Error retrieving conversation metadata: {e}")
             return jsonify({'error': 'Failed to retrieve conversation metadata'}), 500
 
+    @app.route('/api/conversations/<conversation_id>/summary', methods=['POST'])
+    @swagger_route(security=get_auth_security())
+    @login_required
+    @user_required
+    def generate_conversation_summary_api(conversation_id):
+        """
+        Generate (or regenerate) a summary for a conversation and persist it.
+
+        Request body (optional):
+            { "model_deployment": "gpt-4o" }
+
+        Returns the generated summary dict on success.
+        """
+        from route_backend_conversation_export import generate_conversation_summary, _normalize_content
+        from functions_chat import sort_messages_by_thread
+
+        user_id = get_current_user_id()
+        if not user_id:
+            return jsonify({'error': 'User not authenticated'}), 401
+
+        try:
+            conversation_item = cosmos_conversations_container.read_item(
+                item=conversation_id,
+                partition_key=conversation_id
+            )
+            if conversation_item.get('user_id') != user_id:
+                return jsonify({'error': 'Forbidden'}), 403
+        except CosmosResourceNotFoundError:
+            return jsonify({'error': 'Conversation not found'}), 404
+        except Exception as e:
+            debug_print(f"Error reading conversation for summary: {e}")
+            return jsonify({'error': 'Failed to read conversation'}), 500
+
+        body = request.get_json(silent=True) or {}
+        model_deployment = body.get('model_deployment', '')
+
+        # Query messages for this conversation
+        try:
+            query = "SELECT * FROM c WHERE c.conversation_id = @cid ORDER BY c.timestamp ASC"
+            params = [{"name": "@cid", "value": conversation_id}]
+            raw_messages = list(cosmos_messages_container.query_items(
+                query=query,
+                parameters=params,
+                enable_cross_partition_query=True
+            ))
+        except Exception as e:
+            debug_print(f"Error querying messages for summary: {e}")
+            return jsonify({'error': 'Failed to query messages'}), 500
+
+        if not raw_messages:
+            return jsonify({'error': 'No messages in this conversation'}), 400
+
+        # Build lightweight export-style message list for the summary helper
+        ordered_messages = sort_messages_by_thread(raw_messages)
+        export_messages = []
+        for msg in ordered_messages:
+            role = msg.get('role', 'unknown')
+            # Content may be a string OR a list of content parts — normalise it
+            content = _normalize_content(msg.get('content', ''))
+            speaker = 'USER' if role == 'user' else 'ASSISTANT' if role == 'assistant' else role.upper()
+            export_messages.append({
+                'role': role,
+                'content_text': content,
+                'speaker_label': speaker
+            })
+
+        message_time_start = ordered_messages[0].get('timestamp') if ordered_messages else None
+        message_time_end = ordered_messages[-1].get('timestamp') if ordered_messages else None
+
+        settings = get_settings()
+
+        try:
+            summary_data = generate_conversation_summary(
+                messages=export_messages,
+                conversation_title=conversation_item.get('title', 'Untitled'),
+                settings=settings,
+                model_deployment=model_deployment,
+                message_time_start=message_time_start,
+                message_time_end=message_time_end,
+                conversation_id=conversation_id
+            )
+            return jsonify({'success': True, 'summary': summary_data}), 200
+
+        except (ValueError, RuntimeError) as known_exc:
+            return jsonify({'error': str(known_exc)}), 400
+        except Exception as exc:
+            debug_print(f"Summary generation API error: {exc}")
+            return jsonify({'error': 'Summary generation failed'}), 500
+
     @app.route('/api/conversations/<conversation_id>/scope_lock', methods=['PATCH'])
     @swagger_route(security=get_auth_security())
     @login_required
diff --git a/application/single_app/route_backend_documents.py b/application/single_app/route_backend_documents.py
index 28d4ef69..0e9d490b 100644
--- a/application/single_app/route_backend_documents.py
+++ b/application/single_app/route_backend_documents.py
@@ -7,6 +7,7 @@
 from utils_cache import invalidate_personal_search_cache
 from functions_debug import *
 from functions_activity_logging import log_document_upload, log_document_metadata_update_transaction
+import io
 import os
 import requests
 from flask import current_app
@@ -72,7 +73,58 @@ def get_file_content():
 
             filename = items_sorted[0].get('filename', 'Untitled')
             is_table = items_sorted[0].get('is_table', False)
-            debug_print(f"[GET_FILE_CONTENT] Filename: {filename}, is_table: {is_table}")
+            file_content_source = items_sorted[0].get('file_content_source', '')
+            debug_print(f"[GET_FILE_CONTENT] Filename: {filename}, is_table: {is_table}, source: {file_content_source}")
+
+            # Handle blob-stored tabular files (enhanced citations enabled)
+            if file_content_source == 'blob':
+                blob_container = items_sorted[0].get('blob_container', '')
+                blob_path = items_sorted[0].get('blob_path', '')
+                debug_print(f"[GET_FILE_CONTENT] Blob-stored file: container={blob_container}, path={blob_path}")
+
+                if not blob_container or not blob_path:
+                    return jsonify({'error': 'Blob storage reference is incomplete'}), 500
+
+                try:
+                    blob_service_client = CLIENTS.get("storage_account_office_docs_client")
+                    if not blob_service_client:
+                        return jsonify({'error': 'Blob storage client not available'}), 500
+
+                    blob_client = blob_service_client.get_blob_client(
+                        container=blob_container,
+                        blob=blob_path
+                    )
+                    stream = blob_client.download_blob()
+                    blob_data = stream.readall()
+
+                    # Convert to CSV using pandas for display
+                    file_ext = os.path.splitext(filename)[1].lower()
+                    if file_ext == '.csv':
+                        import pandas
+                        df = pandas.read_csv(io.BytesIO(blob_data))
+                        combined_content = df.to_csv(index=False)
+                    elif file_ext in ['.xlsx', '.xlsm']:
+                        import pandas
+                        df = pandas.read_excel(io.BytesIO(blob_data), engine='openpyxl')
+                        combined_content = df.to_csv(index=False)
+                    elif file_ext == '.xls':
+                        import pandas
+                        df = pandas.read_excel(io.BytesIO(blob_data), engine='xlrd')
+                        combined_content = df.to_csv(index=False)
+                    else:
+                        combined_content = blob_data.decode('utf-8', errors='replace')
+
+                    debug_print(f"[GET_FILE_CONTENT] Successfully read blob content, length: {len(combined_content)}")
+                    return jsonify({
+                        'file_content': combined_content,
+                        'filename': filename,
+                        'is_table': is_table,
+                        'file_content_source': 'blob'
+                    }), 200
+
+                except Exception as blob_err:
+                    debug_print(f"[GET_FILE_CONTENT] Error reading from blob: {blob_err}")
+                    return jsonify({'error': f'Error reading file from storage: {str(blob_err)}'}), 500
 
             add_file_task_to_file_processing_log(document_id=file_id, user_id=user_id, content="Combining file content from chunks, filename: " + filename + ", is_table: " + str(is_table))
             combined_parts = []
diff --git a/application/single_app/route_backend_plugins.py b/application/single_app/route_backend_plugins.py
index 77aab866..f4d4dca0 100644
--- a/application/single_app/route_backend_plugins.py
+++ b/application/single_app/route_backend_plugins.py
@@ -32,6 +32,11 @@
 
 from functions_debug import debug_print
 from json_schema_validation import validate_plugin
+from functions_activity_logging import (
+    log_action_creation,
+    log_action_update,
+    log_action_deletion,
+)
 
 def discover_plugin_types():
     # Dynamically discover allowed plugin types from available plugin classes.
@@ -345,6 +350,19 @@ def set_user_plugins():
     except Exception as e:
         debug_print(f"Error saving personal actions for user {user_id}: {e}")
         return jsonify({'error': 'Failed to save plugins'}), 500
+
+    # Log individual action activities
+    for plugin in filtered_plugins:
+        p_name = plugin.get('name', '')
+        p_id = plugin.get('id', '')
+        p_type = plugin.get('type', '')
+        if p_name in current_action_names:
+            log_action_update(user_id=user_id, action_id=p_id, action_name=p_name, action_type=p_type, scope='personal')
+        else:
+            log_action_creation(user_id=user_id, action_id=p_id, action_name=p_name, action_type=p_type, scope='personal')
+    for plugin_name in (current_action_names - new_plugin_names):
+        log_action_deletion(user_id=user_id, action_id=plugin_name, action_name=plugin_name, scope='personal')
+
     log_event("User plugins updated", extra={"user_id": user_id, "plugins_count": len(filtered_plugins)})
     return jsonify({'success': True})
 
@@ -360,6 +378,7 @@ def delete_user_plugin(plugin_name):
     if not deleted:
         return jsonify({'error': 'Plugin not found.'}), 404
     
+    log_action_deletion(user_id=user_id, action_id=plugin_name, action_name=plugin_name, scope='personal')
     log_event("User plugin deleted", extra={"user_id": user_id, "plugin_name": plugin_name})
     return jsonify({'success': True})
 
@@ -460,6 +479,13 @@ def create_group_action_route():
     for key in ('group_id', 'last_updated', 'user_id', 'is_global', 'is_group', 'scope'):
         payload.pop(key, None)
 
+    # Handle endpoint based on plugin type (same logic as personal plugins)
+    plugin_type = payload.get('type', '')
+    if plugin_type in ['sql_schema', 'sql_query']:
+        payload.setdefault('endpoint', f'sql://{plugin_type}')
+    elif plugin_type == 'msgraph':
+        payload.setdefault('endpoint', 'https://graph.microsoft.com')
+
     # Merge with schema to ensure all required fields are present (same as global actions)
     schema_dir = os.path.join(current_app.root_path, 'static', 'json', 'schemas')
     merged = get_merged_plugin_settings(payload.get('type'), payload, schema_dir)
@@ -467,11 +493,12 @@ def create_group_action_route():
     payload['additionalFields'] = merged.get('additionalFields', payload.get('additionalFields', {}))
 
     try:
-        saved = save_group_action(active_group, payload)
+        saved = save_group_action(active_group, payload, user_id=user_id)
     except Exception as exc:
         debug_print('Failed to save group action: %s', exc)
         return jsonify({'error': 'Unable to save action'}), 500
 
+    log_action_creation(user_id=user_id, action_id=saved.get('id', ''), action_name=saved.get('name', ''), action_type=saved.get('type', ''), scope='group', group_id=active_group)
     return jsonify(saved), 201
 
 
@@ -516,6 +543,13 @@ def update_group_action_route(action_id):
     merged['is_group'] = True
     merged['id'] = existing.get('id', action_id)
 
+    # Handle endpoint based on plugin type (same logic as personal plugins)
+    plugin_type = merged.get('type', '')
+    if plugin_type in ['sql_schema', 'sql_query']:
+        merged.setdefault('endpoint', f'sql://{plugin_type}')
+    elif plugin_type == 'msgraph':
+        merged.setdefault('endpoint', 'https://graph.microsoft.com')
+
     try:
         validate_group_action_payload(merged, partial=False)
     except ValueError as exc:
@@ -528,11 +562,12 @@ def update_group_action_route(action_id):
     merged['additionalFields'] = schema_merged.get('additionalFields', merged.get('additionalFields', {}))
 
     try:
-        saved = save_group_action(active_group, merged)
+        saved = save_group_action(active_group, merged, user_id=user_id)
     except Exception as exc:
         debug_print('Failed to update group action %s: %s', action_id, exc)
         return jsonify({'error': 'Unable to update action'}), 500
 
+    log_action_update(user_id=user_id, action_id=action_id, action_name=saved.get('name', ''), action_type=saved.get('type', ''), scope='group', group_id=active_group)
     return jsonify(saved), 200
 
 
@@ -563,6 +598,7 @@ def delete_group_action_route(action_id):
 
     if not removed:
         return jsonify({'error': 'Action not found'}), 404
+    log_action_deletion(user_id=user_id, action_id=action_id, action_name=action_id, scope='group', group_id=active_group)
     return jsonify({'message': 'Action deleted'}), 200
 
 @bpap.route('/api/user/plugins/types', methods=['GET'])
@@ -588,6 +624,8 @@ def get_core_plugin_settings():
         'enable_text_plugin': bool(settings.get('enable_text_plugin', True)),
         'enable_default_embedding_model_plugin': bool(settings.get('enable_default_embedding_model_plugin', True)),
         'enable_fact_memory_plugin': bool(settings.get('enable_fact_memory_plugin', True)),
+        'enable_tabular_processing_plugin': bool(settings.get('enable_tabular_processing_plugin', False)),
+        'enable_enhanced_citations': bool(settings.get('enable_enhanced_citations', False)),
         'enable_semantic_kernel': bool(settings.get('enable_semantic_kernel', False)),
         'allow_user_plugins': bool(settings.get('allow_user_plugins', True)),
         'allow_group_plugins': bool(settings.get('allow_group_plugins', True)),
@@ -610,6 +648,7 @@ def update_core_plugin_settings():
         'enable_text_plugin',
         'enable_default_embedding_model_plugin',
         'enable_fact_memory_plugin',
+        'enable_tabular_processing_plugin',
         'allow_user_plugins',
         'allow_group_plugins'
     ]
@@ -627,6 +666,11 @@ def update_core_plugin_settings():
             return jsonify({'error': f"Field '{key}' must be a boolean."}), 400
         updates[key] = data[key]
     logging.info("Validated plugin settings: %s", updates)
+    # Dependency: tabular processing requires enhanced citations
+    if updates.get('enable_tabular_processing_plugin', False):
+        full_settings = get_settings()
+        if not full_settings.get('enable_enhanced_citations', False):
+            return jsonify({'error': 'Tabular Processing requires Enhanced Citations to be enabled.'}), 400
     # Update settings
     success = update_settings(updates)
     if success:
@@ -692,9 +736,10 @@ def add_plugin():
         new_plugin['id'] = plugin_id
         
         # Save to global actions container
-        save_global_action(new_plugin)
+        save_global_action(new_plugin, user_id=str(get_current_user_id()))
         
-        log_event("Plugin added", extra={"action": "add", "plugin": new_plugin, "user": str(getattr(request, 'user', 'unknown'))})
+        log_action_creation(user_id=str(get_current_user_id()), action_id=plugin_id, action_name=new_plugin.get('name', ''), action_type=new_plugin.get('type', ''), scope='global')
+        log_event("Plugin added", extra={"action": "add", "plugin": new_plugin, "user": str(get_current_user_id())})
         
         # --- HOT RELOAD TRIGGER ---
         setattr(builtins, "kernel_reload_needed", True)
@@ -753,9 +798,10 @@ def edit_plugin(plugin_name):
             # Delete old and save updated
             if 'id' in found_plugin:
                 delete_global_action(found_plugin['id'])
-            save_global_action(updated_plugin)
+            save_global_action(updated_plugin, user_id=str(get_current_user_id()))
             
-            log_event("Plugin edited", extra={"action": "edit", "plugin": updated_plugin, "user": str(getattr(request, 'user', 'unknown'))})
+            log_action_update(user_id=str(get_current_user_id()), action_id=updated_plugin.get('id', ''), action_name=plugin_name, action_type=updated_plugin.get('type', ''), scope='global')
+            log_event("Plugin edited", extra={"action": "edit", "plugin": updated_plugin, "user": str(get_current_user_id())})
             # --- HOT RELOAD TRIGGER ---
             setattr(builtins, "kernel_reload_needed", True)
             return jsonify({'success': True})
@@ -796,7 +842,8 @@ def delete_plugin(plugin_name):
         if 'id' in plugin_to_delete:
             delete_global_action(plugin_to_delete['id'])
         
-        log_event("Plugin deleted", extra={"action": "delete", "plugin_name": plugin_name, "user": str(getattr(request, 'user', 'unknown'))})
+        log_action_deletion(user_id=str(get_current_user_id()), action_id=plugin_to_delete.get('id', ''), action_name=plugin_name, action_type=plugin_to_delete.get('type', ''), scope='global')
+        log_event("Plugin deleted", extra={"action": "delete", "plugin_name": plugin_name, "user": str(get_current_user_id())})
         # --- HOT RELOAD TRIGGER ---
         setattr(builtins, "kernel_reload_needed", True)
         return jsonify({'success': True})
@@ -928,4 +975,116 @@ def _merge_group_and_global_actions(group_actions, global_actions):
     return normalized_actions
 
 
+@bpap.route('/api/plugins/test-sql-connection', methods=['POST'])
+@swagger_route(security=get_auth_security())
+@login_required
+@user_required
+def test_sql_connection():
+    """Test a SQL database connection using provided configuration."""
+    data = request.get_json(silent=True) or {}
+    database_type = (data.get('database_type') or 'sqlserver').lower()
+    connection_method = data.get('connection_method', 'parameters')
+    connection_string = data.get('connection_string', '')
+    server = data.get('server', '')
+    database = data.get('database', '')
+    port = data.get('port', '')
+    driver = data.get('driver', '')
+    username = data.get('username', '')
+    password = data.get('password', '')
+    auth_type = data.get('auth_type', 'username_password')
+    timeout = min(int(data.get('timeout', 10)), 15)  # Cap at 15 seconds for test
+
+    # Map azure_sql to sqlserver
+    if database_type in ('azure_sql', 'azuresql'):
+        database_type = 'sqlserver'
+
+    try:
+        if database_type == 'sqlserver':
+            import pyodbc
+            if connection_method == 'connection_string' and connection_string:
+                conn = pyodbc.connect(connection_string, timeout=timeout)
+            else:
+                if not server or not database:
+                    return jsonify({'success': False, 'error': 'Server and database are required for individual parameters connection.'}), 400
+                drv = driver or 'ODBC Driver 17 for SQL Server'
+                conn_str = f"DRIVER={{{drv}}};SERVER={server};DATABASE={database}"
+                if port:
+                    conn_str += f",{port}"
+                if auth_type == 'username_password' and username and password:
+                    conn_str += f";UID={username};PWD={password}"
+                elif auth_type == 'managed_identity':
+                    conn_str += ";Authentication=ActiveDirectoryMsi"
+                elif auth_type == 'integrated':
+                    conn_str += ";Trusted_Connection=yes"
+                conn = pyodbc.connect(conn_str, timeout=timeout)
+            cursor = conn.cursor()
+            cursor.execute("SELECT 1")
+            cursor.close()
+            conn.close()
+            return jsonify({'success': True, 'message': f'Successfully connected to {data.get("database", "database")} on {data.get("server", "server")}.'})
+
+        elif database_type == 'postgresql':
+            import psycopg2
+            if connection_method == 'connection_string' and connection_string:
+                conn = psycopg2.connect(connection_string, connect_timeout=timeout)
+            else:
+                if not server or not database:
+                    return jsonify({'success': False, 'error': 'Server and database are required.'}), 400
+                conn_params = {'host': server, 'database': database, 'connect_timeout': timeout}
+                if port:
+                    conn_params['port'] = int(port)
+                if username:
+                    conn_params['user'] = username
+                if password:
+                    conn_params['password'] = password
+                conn = psycopg2.connect(**conn_params)
+            cursor = conn.cursor()
+            cursor.execute("SELECT 1")
+            cursor.close()
+            conn.close()
+            return jsonify({'success': True, 'message': f'Successfully connected to PostgreSQL database {data.get("database", "")}.'})
+
+        elif database_type == 'mysql':
+            import pymysql
+            if connection_method == 'connection_string' and connection_string:
+                # pymysql doesn't natively parse connection strings, so use params
+                return jsonify({'success': False, 'error': 'MySQL test connection requires individual parameters, not a connection string.'}), 400
+            if not server or not database:
+                return jsonify({'success': False, 'error': 'Server and database are required.'}), 400
+            conn_params = {'host': server, 'database': database, 'connect_timeout': timeout}
+            if port:
+                conn_params['port'] = int(port)
+            if username:
+                conn_params['user'] = username
+            if password:
+                conn_params['password'] = password
+            conn = pymysql.connect(**conn_params)
+            cursor = conn.cursor()
+            cursor.execute("SELECT 1")
+            cursor.close()
+            conn.close()
+            return jsonify({'success': True, 'message': f'Successfully connected to MySQL database {data.get("database", "")}.'})
+
+        elif database_type == 'sqlite':
+            import sqlite3
+            db_path = connection_string or database
+            if not db_path:
+                return jsonify({'success': False, 'error': 'Database path is required for SQLite.'}), 400
+            conn = sqlite3.connect(db_path, timeout=timeout)
+            cursor = conn.cursor()
+            cursor.execute("SELECT 1")
+            cursor.close()
+            conn.close()
+            return jsonify({'success': True, 'message': f'Successfully connected to SQLite database.'})
+
+        else:
+            return jsonify({'success': False, 'error': f'Unsupported database type: {database_type}'}), 400
 
+    except ImportError as e:
+        return jsonify({'success': False, 'error': f'Database driver not installed: {str(e)}'}), 400
+    except Exception as e:
+        error_msg = str(e)
+        # Sanitize error message to avoid leaking sensitive details
+        if 'password' in error_msg.lower() or 'pwd' in error_msg.lower():
+            error_msg = 'Authentication failed. Please check your credentials.'
+        return jsonify({'success': False, 'error': f'Connection failed: {error_msg}'}), 400
diff --git a/application/single_app/route_backend_thoughts.py b/application/single_app/route_backend_thoughts.py
new file mode 100644
index 00000000..a7624a3f
--- /dev/null
+++ b/application/single_app/route_backend_thoughts.py
@@ -0,0 +1,80 @@
+# route_backend_thoughts.py
+
+from flask import request, jsonify
+from functions_authentication import login_required, user_required, get_current_user_id
+from functions_settings import get_settings
+from functions_thoughts import get_thoughts_for_message, get_pending_thoughts
+from swagger_wrapper import swagger_route, get_auth_security
+from functions_appinsights import log_event
+
+
+def register_route_backend_thoughts(app):
+
+    @app.route('/api/conversations/<conversation_id>/messages/<message_id>/thoughts', methods=['GET'])
+    @swagger_route(security=get_auth_security())
+    @login_required
+    @user_required
+    def api_get_message_thoughts(conversation_id, message_id):
+        """Return persisted thoughts for a specific assistant message."""
+        user_id = get_current_user_id()
+        if not user_id:
+            return jsonify({'error': 'User not authenticated'}), 401
+
+        settings = get_settings()
+        if not settings.get('enable_thoughts', False):
+            return jsonify({'thoughts': [], 'enabled': False}), 200
+
+        try:
+            thoughts = get_thoughts_for_message(conversation_id, message_id, user_id)
+            # Strip internal Cosmos fields before returning
+            sanitized = []
+            for t in thoughts:
+                sanitized.append({
+                    'id': t.get('id'),
+                    'step_index': t.get('step_index'),
+                    'step_type': t.get('step_type'),
+                    'content': t.get('content'),
+                    'detail': t.get('detail'),
+                    'duration_ms': t.get('duration_ms'),
+                    'timestamp': t.get('timestamp')
+                })
+            return jsonify({'thoughts': sanitized, 'enabled': True}), 200
+        except Exception as e:
+            log_event(f"api_get_message_thoughts error: {e}", level="WARNING")
+            return jsonify({'error': 'Failed to retrieve thoughts'}), 500
+
+    @app.route('/api/conversations/<conversation_id>/thoughts/pending', methods=['GET'])
+    @swagger_route(security=get_auth_security())
+    @login_required
+    @user_required
+    def api_get_pending_thoughts(conversation_id):
+        """Return the latest in-progress thoughts for a conversation.
+
+        Used by the non-streaming frontend to poll for thought updates
+        while waiting for the chat response.
+        """
+        user_id = get_current_user_id()
+        if not user_id:
+            return jsonify({'error': 'User not authenticated'}), 401
+
+        settings = get_settings()
+        if not settings.get('enable_thoughts', False):
+            return jsonify({'thoughts': [], 'enabled': False}), 200
+
+        try:
+            thoughts = get_pending_thoughts(conversation_id, user_id)
+            sanitized = []
+            for t in thoughts:
+                sanitized.append({
+                    'id': t.get('id'),
+                    'step_index': t.get('step_index'),
+                    'step_type': t.get('step_type'),
+                    'content': t.get('content'),
+                    'detail': t.get('detail'),
+                    'duration_ms': t.get('duration_ms'),
+                    'timestamp': t.get('timestamp')
+                })
+            return jsonify({'thoughts': sanitized, 'enabled': True}), 200
+        except Exception as e:
+            log_event(f"api_get_pending_thoughts error: {e}", level="WARNING")
+            return jsonify({'error': 'Failed to retrieve pending thoughts'}), 500
diff --git a/application/single_app/route_backend_user_agreement.py b/application/single_app/route_backend_user_agreement.py
index f46559ff..b76213b3 100644
--- a/application/single_app/route_backend_user_agreement.py
+++ b/application/single_app/route_backend_user_agreement.py
@@ -130,7 +130,7 @@ def api_accept_user_agreement():
             return jsonify({"error": "workspace_id and workspace_type are required"}), 400
 
         # Validate workspace type
-        valid_types = ["personal", "group", "public"]
+        valid_types = ["personal", "group", "public", "chat"]
         if workspace_type not in valid_types:
             return jsonify({"error": f"Invalid workspace_type. Must be one of: {', '.join(valid_types)}"}), 400
 
diff --git a/application/single_app/route_enhanced_citations.py b/application/single_app/route_enhanced_citations.py
index c81ef225..44fc4fcb 100644
--- a/application/single_app/route_enhanced_citations.py
+++ b/application/single_app/route_enhanced_citations.py
@@ -8,6 +8,7 @@
 import requests
 import mimetypes
 import io
+import pandas
 
 from functions_authentication import login_required, user_required, get_current_user_id
 from functions_settings import get_settings, enabled_required
@@ -15,7 +16,7 @@
 from functions_group import get_user_groups
 from functions_public_workspaces import get_user_visible_public_workspace_ids_from_settings
 from swagger_wrapper import swagger_route, get_auth_security
-from config import CLIENTS, storage_account_user_documents_container_name, storage_account_group_documents_container_name, storage_account_public_documents_container_name, IMAGE_EXTENSIONS, VIDEO_EXTENSIONS, AUDIO_EXTENSIONS
+from config import CLIENTS, storage_account_user_documents_container_name, storage_account_group_documents_container_name, storage_account_public_documents_container_name, storage_account_personal_chat_container_name, IMAGE_EXTENSIONS, VIDEO_EXTENSIONS, AUDIO_EXTENSIONS, TABULAR_EXTENSIONS, cosmos_messages_container, cosmos_conversations_container
 from functions_debug import debug_print
 
 def register_enhanced_citations_routes(app):
@@ -183,6 +184,274 @@ def get_enhanced_citation_pdf():
         except Exception as e:
             return jsonify({"error": str(e)}), 500
 
+    @app.route("/api/enhanced_citations/tabular", methods=["GET"])
+    @swagger_route(security=get_auth_security())
+    @login_required
+    @user_required
+    @enabled_required("enable_enhanced_citations")
+    def get_enhanced_citation_tabular():
+        """
+        Serve original tabular file (CSV, XLSX, etc.) from blob storage for download.
+        Used for chat-uploaded tabular files stored in blob storage.
+        """
+        conversation_id = request.args.get("conversation_id")
+        file_id = request.args.get("file_id")
+
+        if not conversation_id or not file_id:
+            return jsonify({"error": "conversation_id and file_id are required"}), 400
+
+        user_id = get_current_user_id()
+        if not user_id:
+            return jsonify({"error": "User not authenticated"}), 401
+
+        try:
+            # Verify the current user owns the conversation
+            try:
+                conversation = cosmos_conversations_container.read_item(
+                    item=conversation_id,
+                    partition_key=conversation_id
+                )
+            except Exception:
+                return jsonify({"error": "Conversation not found"}), 404
+
+            if conversation.get('user_id') != user_id:
+                return jsonify({"error": "Forbidden"}), 403
+
+            # Look up the file message in Cosmos to get blob reference
+            query_str = """
+                SELECT * FROM c
+                WHERE c.conversation_id = @conversation_id
+                AND c.id = @file_id
+            """
+            items = list(cosmos_messages_container.query_items(
+                query=query_str,
+                parameters=[
+                    {'name': '@conversation_id', 'value': conversation_id},
+                    {'name': '@file_id', 'value': file_id}
+                ],
+                partition_key=conversation_id
+            ))
+
+            if not items:
+                return jsonify({"error": "File not found"}), 404
+
+            file_msg = items[0]
+            file_content_source = file_msg.get('file_content_source', '')
+
+            if file_content_source != 'blob':
+                return jsonify({"error": "File is not stored in blob storage"}), 400
+
+            blob_container = file_msg.get('blob_container', '')
+            blob_path = file_msg.get('blob_path', '')
+            filename = file_msg.get('filename', 'download')
+
+            if not blob_container or not blob_path:
+                return jsonify({"error": "Blob reference is incomplete"}), 500
+
+            blob_service_client = CLIENTS.get("storage_account_office_docs_client")
+            if not blob_service_client:
+                return jsonify({"error": "Storage not available"}), 500
+
+            blob_client = blob_service_client.get_blob_client(
+                container=blob_container,
+                blob=blob_path
+            )
+            stream = blob_client.download_blob()
+            content = stream.readall()
+
+            # Determine content type
+            content_type, _ = mimetypes.guess_type(filename)
+            if not content_type:
+                content_type = 'application/octet-stream'
+
+            return Response(
+                content,
+                content_type=content_type,
+                headers={
+                    'Content-Length': str(len(content)),
+                    'Content-Disposition': f'attachment; filename="{filename}"',
+                    'Cache-Control': 'private, max-age=300',
+                }
+            )
+
+        except Exception as e:
+            debug_print(f"Error serving tabular citation: {e}")
+            return jsonify({"error": str(e)}), 500
+
+    @app.route("/api/enhanced_citations/tabular_workspace", methods=["GET"])
+    @swagger_route(security=get_auth_security())
+    @login_required
+    @user_required
+    @enabled_required("enable_enhanced_citations")
+    def get_enhanced_citation_tabular_workspace():
+        """
+        Serve tabular file (CSV, XLSX, etc.) from blob storage for workspace documents.
+        Uses doc_id to look up the document across personal, group, and public workspaces.
+        """
+        doc_id = request.args.get("doc_id")
+        if not doc_id:
+            return jsonify({"error": "doc_id is required"}), 400
+
+        user_id = get_current_user_id()
+        if not user_id:
+            return jsonify({"error": "User not authenticated"}), 401
+
+        try:
+            doc_response, status_code = get_document(user_id, doc_id)
+            if status_code != 200:
+                return doc_response, status_code
+
+            raw_doc = doc_response.get_json()
+            file_name = raw_doc.get('file_name', '')
+            ext = file_name.lower().split('.')[-1] if '.' in file_name else ''
+
+            if ext not in ('csv', 'xlsx', 'xls', 'xlsm'):
+                return jsonify({"error": "File is not a tabular file"}), 400
+
+            return serve_enhanced_citation_content(raw_doc, force_download=True)
+
+        except Exception as e:
+            debug_print(f"Error serving tabular workspace citation: {e}")
+            return jsonify({"error": str(e)}), 500
+
+    @app.route("/api/enhanced_citations/tabular_preview", methods=["GET"])
+    @swagger_route(security=get_auth_security())
+    @login_required
+    @user_required
+    @enabled_required("enable_enhanced_citations")
+    def get_enhanced_citation_tabular_preview():
+        """
+        Return JSON preview of a tabular file for rendering as an HTML table.
+        Reads the file into a pandas DataFrame and returns columns + rows as JSON.
+        """
+        doc_id = request.args.get("doc_id")
+        sheet_name = request.args.get("sheet_name")
+        sheet_index = request.args.get("sheet_index")
+        max_rows = min(request.args.get("max_rows", 200, type=int), 500)
+        if not doc_id:
+            return jsonify({"error": "doc_id is required"}), 400
+
+        user_id = get_current_user_id()
+        if not user_id:
+            return jsonify({"error": "User not authenticated"}), 401
+
+        try:
+            doc_response, status_code = get_document(user_id, doc_id)
+            if status_code != 200:
+                return doc_response, status_code
+
+            raw_doc = doc_response.get_json()
+            file_name = raw_doc.get('file_name', '')
+            ext = file_name.lower().rsplit('.', 1)[-1] if '.' in file_name else ''
+            if ext not in ('csv', 'xlsx', 'xls', 'xlsm'):
+                return jsonify({"error": "File is not a tabular file"}), 400
+
+            # Download blob with size cap to protect memory
+            settings = get_settings()
+            max_blob_size = int(settings.get('tabular_preview_max_blob_size_mb', 200)) * 1024 * 1024
+            workspace_type, container_name = determine_workspace_type_and_container(raw_doc)
+            blob_name = get_blob_name(raw_doc, workspace_type)
+            blob_service_client = CLIENTS.get("storage_account_office_docs_client")
+            if not blob_service_client:
+                return jsonify({"error": "Blob storage client not available"}), 500
+            blob_client = blob_service_client.get_blob_client(container=container_name, blob=blob_name)
+            blob_props = blob_client.get_blob_properties()
+            if blob_props.size > max_blob_size:
+                return jsonify({"error": "File is too large to preview"}), 400
+            data = blob_client.download_blob().readall()
+
+            # Read into DataFrame, limiting rows for preview efficiency
+            # Read max_rows + 1 so we can detect truncation without loading the full file
+            nrows_limit = max_rows + 1
+            selected_sheet = None
+            sheet_names = []
+            if ext == 'csv':
+                df = pandas.read_csv(io.BytesIO(data), keep_default_na=False, dtype=str, nrows=nrows_limit)
+            elif ext in ('xlsx', 'xlsm'):
+                excel_file = pandas.ExcelFile(io.BytesIO(data), engine='openpyxl')
+                sheet_names = list(excel_file.sheet_names)
+                if not sheet_names:
+                    return jsonify({"error": "Workbook does not contain any readable sheets"}), 400
+
+                if sheet_name:
+                    requested_sheet_name = sheet_name.strip()
+                    matching_sheet_name = next(
+                        (candidate for candidate in sheet_names if candidate.lower() == requested_sheet_name.lower()),
+                        None,
+                    )
+                    if not matching_sheet_name:
+                        return jsonify({
+                            "error": f"Sheet '{requested_sheet_name}' was not found. Available sheets: {sheet_names}"
+                        }), 400
+                    selected_sheet = matching_sheet_name
+                elif sheet_index not in (None, ''):
+                    try:
+                        resolved_sheet_index = int(sheet_index)
+                    except ValueError:
+                        return jsonify({"error": "sheet_index must be an integer"}), 400
+                    if resolved_sheet_index < 0 or resolved_sheet_index >= len(sheet_names):
+                        return jsonify({
+                            "error": f"sheet_index {resolved_sheet_index} is out of range. Available sheets: {sheet_names}"
+                        }), 400
+                    selected_sheet = sheet_names[resolved_sheet_index]
+                else:
+                    selected_sheet = sheet_names[0]
+
+                df = excel_file.parse(selected_sheet, keep_default_na=False, dtype=str, nrows=nrows_limit)
+            elif ext == 'xls':
+                excel_file = pandas.ExcelFile(io.BytesIO(data), engine='xlrd')
+                sheet_names = list(excel_file.sheet_names)
+                if not sheet_names:
+                    return jsonify({"error": "Workbook does not contain any readable sheets"}), 400
+
+                if sheet_name:
+                    requested_sheet_name = sheet_name.strip()
+                    matching_sheet_name = next(
+                        (candidate for candidate in sheet_names if candidate.lower() == requested_sheet_name.lower()),
+                        None,
+                    )
+                    if not matching_sheet_name:
+                        return jsonify({
+                            "error": f"Sheet '{requested_sheet_name}' was not found. Available sheets: {sheet_names}"
+                        }), 400
+                    selected_sheet = matching_sheet_name
+                elif sheet_index not in (None, ''):
+                    try:
+                        resolved_sheet_index = int(sheet_index)
+                    except ValueError:
+                        return jsonify({"error": "sheet_index must be an integer"}), 400
+                    if resolved_sheet_index < 0 or resolved_sheet_index >= len(sheet_names):
+                        return jsonify({
+                            "error": f"sheet_index {resolved_sheet_index} is out of range. Available sheets: {sheet_names}"
+                        }), 400
+                    selected_sheet = sheet_names[resolved_sheet_index]
+                else:
+                    selected_sheet = sheet_names[0]
+
+                df = excel_file.parse(selected_sheet, keep_default_na=False, dtype=str, nrows=nrows_limit)
+            else:
+                return jsonify({"error": f"Unsupported file type: {ext}"}), 400
+
+            total_rows = len(df)
+            truncated = total_rows > max_rows
+            preview = df.head(max_rows)
+
+            return jsonify({
+                "filename": file_name,
+                "selected_sheet": selected_sheet,
+                "sheet_names": sheet_names,
+                "sheet_count": len(sheet_names),
+                "total_rows": total_rows if not truncated else None,
+                "total_columns": len(df.columns),
+                "columns": list(df.columns),
+                "rows": preview.values.tolist(),
+                "truncated": truncated
+            })
+
+        except Exception as e:
+            debug_print(f"Error generating tabular preview: {e}")
+            return jsonify({"error": str(e)}), 500
+
 def get_document(user_id, doc_id):
     """
     Get document metadata - searches across all enabled workspace types
diff --git a/application/single_app/route_frontend_admin_settings.py b/application/single_app/route_frontend_admin_settings.py
index 578e1545..6e530a38 100644
--- a/application/single_app/route_frontend_admin_settings.py
+++ b/application/single_app/route_frontend_admin_settings.py
@@ -98,6 +98,8 @@ def admin_settings():
             settings['enable_text_plugin'] = False
         if 'enable_fact_memory_plugin' not in settings:
             settings['enable_fact_memory_plugin'] = False
+        if 'enable_tabular_processing_plugin' not in settings:
+            settings['enable_tabular_processing_plugin'] = False
         if 'enable_default_embedding_model_plugin' not in settings:
             settings['enable_default_embedding_model_plugin'] = False
         if 'enable_multi_agent_orchestration' not in settings:
@@ -787,6 +789,7 @@ def is_valid_url(url):
                 'enable_enhanced_citations': enable_enhanced_citations,
                 'enable_enhanced_citations_mount': form_data.get('enable_enhanced_citations_mount') == 'on' and enable_enhanced_citations,
                 'enhanced_citations_mount': form_data.get('enhanced_citations_mount', '/view_documents').strip(),
+                'tabular_preview_max_blob_size_mb': int(form_data.get('tabular_preview_max_blob_size_mb', 200)),
                 'office_docs_storage_account_blob_endpoint': office_docs_storage_account_blob_endpoint,
                 'office_docs_storage_account_url': office_docs_storage_account_url,
                 'office_docs_authentication_type': form_data.get('office_docs_authentication_type', 'key'),
@@ -809,9 +812,10 @@ def is_valid_url(url):
                 'require_member_of_safety_violation_admin': require_member_of_safety_violation_admin, # ADDED
                 'require_member_of_feedback_admin': require_member_of_feedback_admin, # ADDED
 
-                # Feedback & Archiving
+                # Feedback, Archiving & Thoughts
                 'enable_user_feedback': form_data.get('enable_user_feedback') == 'on',
                 'enable_conversation_archiving': form_data.get('enable_conversation_archiving') == 'on',
+                'enable_thoughts': form_data.get('enable_thoughts') == 'on',
 
                 # Search (Web Search via Azure AI Foundry agent)
                 'enable_web_search': enable_web_search,
diff --git a/application/single_app/route_frontend_chats.py b/application/single_app/route_frontend_chats.py
index ca0feb1a..67a41879 100644
--- a/application/single_app/route_frontend_chats.py
+++ b/application/single_app/route_frontend_chats.py
@@ -237,8 +237,33 @@ def upload_file():
                 # Handle XML, YAML, and LOG files as text for inline chat
                 extracted_content  = extract_text_file(temp_file_path)
             elif file_ext_nodot in TABULAR_EXTENSIONS:
-                extracted_content = extract_table_file(temp_file_path, file_ext)
                 is_table = True
+
+                # Upload tabular file to blob storage for tabular processing plugin access
+                if settings.get('enable_enhanced_citations', False):
+                    try:
+                        blob_service_client = CLIENTS.get("storage_account_office_docs_client")
+                        if blob_service_client:
+                            blob_path = f"{user_id}/{conversation_id}/{filename}"
+                            blob_client = blob_service_client.get_blob_client(
+                                container=storage_account_personal_chat_container_name,
+                                blob=blob_path
+                            )
+                            metadata = {
+                                "conversation_id": str(conversation_id),
+                                "user_id": str(user_id)
+                            }
+                            with open(temp_file_path, "rb") as blob_f:
+                                blob_client.upload_blob(blob_f, overwrite=True, metadata=metadata)
+                            log_event(f"Uploaded chat tabular file to blob storage: {blob_path}")
+                    except Exception as blob_err:
+                        log_event(
+                            f"Warning: Failed to upload chat tabular file to blob storage: {blob_err}",
+                            level=logging.WARNING
+                        )
+                else:
+                    # Only extract content for Cosmos storage when enhanced citations is disabled
+                    extracted_content = extract_table_file(temp_file_path, file_ext)
             else:
                 return jsonify({'error': 'Unsupported file type'}), 400
 
@@ -395,25 +420,50 @@ def upload_file():
 
                 current_thread_id = str(uuid.uuid4())
                 
-                file_message = {
-                    'id': file_message_id,
-                    'conversation_id': conversation_id,
-                    'role': 'file',
-                    'filename': filename,
-                    'file_content': extracted_content,
-                    'is_table': is_table,
-                    'timestamp': datetime.utcnow().isoformat(),
-                    'model_deployment_name': None,
-                    'metadata': {
-                        'thread_info': {
-                            'thread_id': current_thread_id,
-                            'previous_thread_id': previous_thread_id,
-                            'active_thread': True,
-                            'thread_attempt': 1
+                # When enhanced citations is enabled and file is tabular, store a lightweight
+                # reference without file_content to avoid Cosmos DB size limits.
+                # The tabular data lives in blob storage and is served from there.
+                if is_table and settings.get('enable_enhanced_citations', False):
+                    file_message = {
+                        'id': file_message_id,
+                        'conversation_id': conversation_id,
+                        'role': 'file',
+                        'filename': filename,
+                        'is_table': is_table,
+                        'file_content_source': 'blob',
+                        'blob_container': storage_account_personal_chat_container_name,
+                        'blob_path': f"{user_id}/{conversation_id}/{filename}",
+                        'timestamp': datetime.utcnow().isoformat(),
+                        'model_deployment_name': None,
+                        'metadata': {
+                            'thread_info': {
+                                'thread_id': current_thread_id,
+                                'previous_thread_id': previous_thread_id,
+                                'active_thread': True,
+                                'thread_attempt': 1
+                            }
                         }
                     }
-                }
-                
+                else:
+                    file_message = {
+                        'id': file_message_id,
+                        'conversation_id': conversation_id,
+                        'role': 'file',
+                        'filename': filename,
+                        'file_content': extracted_content,
+                        'is_table': is_table,
+                        'timestamp': datetime.utcnow().isoformat(),
+                        'model_deployment_name': None,
+                        'metadata': {
+                            'thread_info': {
+                                'thread_id': current_thread_id,
+                                'previous_thread_id': previous_thread_id,
+                                'active_thread': True,
+                                'thread_attempt': 1
+                            }
+                        }
+                    }
+
                 # Add vision analysis if available
                 if vision_analysis:
                     file_message['vision_analysis'] = vision_analysis
diff --git a/application/single_app/semantic_kernel_loader.py b/application/single_app/semantic_kernel_loader.py
index 78f54203..c2a7cc1e 100644
--- a/application/single_app/semantic_kernel_loader.py
+++ b/application/single_app/semantic_kernel_loader.py
@@ -19,6 +19,7 @@
 from semantic_kernel.functions.kernel_plugin import KernelPlugin
 from semantic_kernel_plugins.embedding_model_plugin import EmbeddingModelPlugin
 from semantic_kernel_plugins.fact_memory_plugin import FactMemoryPlugin
+from semantic_kernel_plugins.tabular_processing_plugin import TabularProcessingPlugin
 from functions_settings import get_settings, get_user_settings
 from foundry_agent_runtime import AzureAIFoundryChatCompletionAgent
 from functions_appinsights import log_event, get_appinsights_logger
@@ -408,6 +409,13 @@ def load_embedding_model_plugin(kernel: Kernel, settings):
             description="Provides text embedding functions using the configured embedding model."
         )
 
+def load_tabular_processing_plugin(kernel: Kernel):
+    kernel.add_plugin(
+        TabularProcessingPlugin(),
+        plugin_name="tabular_processing",
+        description="Provides data analysis on tabular files (CSV, XLSX) stored in blob storage. Can list files, describe schemas, aggregate columns, filter rows, run queries, and perform group-by operations."
+    )
+
 def load_core_plugins_only(kernel: Kernel, settings):
     """Load only core plugins for model-only conversations without agents."""
     debug_print(f"[SK Loader] Loading core plugins only for model-only mode...")
@@ -429,6 +437,10 @@ def load_core_plugins_only(kernel: Kernel, settings):
         load_text_plugin(kernel)
         log_event("[SK Loader] Loaded Text plugin.", level=logging.INFO)
 
+    if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+        load_tabular_processing_plugin(kernel)
+        log_event("[SK Loader] Loaded Tabular Processing plugin.", level=logging.INFO)
+
 # =================== Semantic Kernel Initialization ===================
 def initialize_semantic_kernel(user_id: str=None, redis_client=None):
     debug_print(f"[SK Loader] Initializing Semantic Kernel and plugins...")
@@ -721,6 +733,195 @@ def normalize(s):
         print(f"[SK Loader] Error loading agent-specific plugins: {e}")
         log_event(f"[SK Loader] Error loading agent-specific plugins: {e}", level=logging.ERROR, exceptionTraceback=True)
 
+
+def _extract_sql_schema_for_instructions(kernel) -> str:
+    """
+    Check if any SQL Schema plugins are loaded in the kernel and extract their schema
+    information to inject into agent instructions.
+    
+    Returns a formatted schema summary string, or empty string if no SQL schema plugins found.
+    """
+    from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+    
+    schema_parts = []
+    
+    try:
+        # Iterate through all registered plugins in the kernel
+        for plugin_name, plugin in kernel.plugins.items():
+            # Check if the underlying plugin object is a SQLSchemaPlugin
+            # Kernel plugins wrap the original object - we need to check the underlying instance
+            plugin_obj = None
+            
+            # Try to access the underlying plugin instance
+            if isinstance(plugin, SQLSchemaPlugin):
+                plugin_obj = plugin
+            elif hasattr(plugin, '_plugin_instance'):
+                if isinstance(plugin._plugin_instance, SQLSchemaPlugin):
+                    plugin_obj = plugin._plugin_instance
+            else:
+                # Check if any function in this plugin belongs to a SQLSchemaPlugin
+                for func_name, func in plugin.functions.items():
+                    if hasattr(func, 'method') and hasattr(func.method, '__self__'):
+                        if isinstance(func.method.__self__, SQLSchemaPlugin):
+                            plugin_obj = func.method.__self__
+                            break
+            
+            if plugin_obj is not None:
+                print(f"[SK Loader] Found SQL Schema plugin: {plugin_name}, fetching schema...")
+                try:
+                    schema_result = plugin_obj.get_database_schema()
+                    if schema_result and hasattr(schema_result, 'data'):
+                        schema_data = schema_result.data
+                    else:
+                        schema_data = schema_result
+                    
+                    if isinstance(schema_data, dict) and "tables" in schema_data:
+                        db_name = schema_data.get("database_name", "Unknown")
+                        db_type = schema_data.get("database_type", "Unknown")
+                        
+                        schema_text = f"### Database: {db_name} ({db_type})\n\n"
+                        
+                        for table_name, table_info in schema_data["tables"].items():
+                            schema_name = table_info.get("schema_name", "dbo")
+                            qualified_name = f"{schema_name}.{table_name}" if schema_name else table_name
+                            schema_text += f"**Table: {qualified_name}**\n"
+                            
+                            columns = table_info.get("columns", [])
+                            if columns:
+                                schema_text += "| Column | Type | Nullable |\n|--------|------|----------|\n"
+                                for col in columns:
+                                    col_name = col.get("column_name", "?")
+                                    col_type = col.get("data_type", "?")
+                                    nullable = "Yes" if col.get("is_nullable", True) else "No"
+                                    schema_text += f"| {col_name} | {col_type} | {nullable} |\n"
+                            
+                            pks = table_info.get("primary_keys", [])
+                            if pks:
+                                schema_text += f"Primary Key(s): {', '.join(pks)}\n"
+                            
+                            schema_text += "\n"
+                        
+                        # Add relationships
+                        relationships = schema_data.get("relationships", [])
+                        if relationships:
+                            schema_text += "**Relationships (Foreign Keys):**\n"
+                            for rel in relationships:
+                                parent = rel.get("parent_table", "?")
+                                parent_col = rel.get("parent_column", "?")
+                                ref = rel.get("referenced_table", "?")
+                                ref_col = rel.get("referenced_column", "?")
+                                schema_text += f"- {parent}.{parent_col} → {ref}.{ref_col}\n"
+                            schema_text += "\n"
+                        
+                        schema_parts.append(schema_text)
+                        print(f"[SK Loader] Successfully extracted schema for {db_name}: {len(schema_data['tables'])} tables")
+                    else:
+                        print(f"[SK Loader] Schema data for {plugin_name} was empty or had unexpected format")
+                        
+                except Exception as e:
+                    print(f"[SK Loader] Warning: Failed to fetch schema from {plugin_name}: {e}")
+                    log_event(f"[SK Loader] Failed to fetch SQL schema for injection: {e}",
+                             extra={"plugin_name": plugin_name, "error": str(e)},
+                             level=logging.WARNING)
+    except Exception as e:
+        print(f"[SK Loader] Warning: Error iterating kernel plugins for SQL schema: {e}")
+        log_event(f"[SK Loader] Error iterating kernel plugins for SQL schema: {e}",
+                 extra={"error": str(e)}, level=logging.WARNING)
+    
+    # Fallback: If no SQLSchemaPlugin was found, check for SQLQueryPlugin instances
+    # and create a temporary SQLSchemaPlugin from their connection config to extract schema
+    if not schema_parts:
+        from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin as _SQLQueryPlugin
+        
+        try:
+            for plugin_name, plugin in kernel.plugins.items():
+                query_obj = None
+                
+                if isinstance(plugin, _SQLQueryPlugin):
+                    query_obj = plugin
+                elif hasattr(plugin, '_plugin_instance'):
+                    if isinstance(plugin._plugin_instance, _SQLQueryPlugin):
+                        query_obj = plugin._plugin_instance
+                else:
+                    for func_name, func in plugin.functions.items():
+                        if hasattr(func, 'method') and hasattr(func.method, '__self__'):
+                            if isinstance(func.method.__self__, _SQLQueryPlugin):
+                                query_obj = func.method.__self__
+                                break
+                
+                if query_obj is not None:
+                    print(f"[SK Loader] Fallback: Found SQLQueryPlugin '{plugin_name}', creating temporary schema extractor...")
+                    try:
+                        temp_manifest = {
+                            'type': 'sql_schema',
+                            'name': f'{plugin_name}_temp_schema',
+                            'database_type': getattr(query_obj, 'database_type', 'azure_sql'),
+                            'server': getattr(query_obj, 'server', ''),
+                            'database': getattr(query_obj, 'database', ''),
+                            'username': getattr(query_obj, 'username', ''),
+                            'password': getattr(query_obj, 'password', ''),
+                            'driver': getattr(query_obj, 'driver', ''),
+                            'connection_string': getattr(query_obj, 'connection_string', ''),
+                        }
+                        temp_schema = SQLSchemaPlugin(temp_manifest)
+                        schema_result = temp_schema.get_database_schema()
+                        if schema_result and hasattr(schema_result, 'data'):
+                            schema_data = schema_result.data
+                        else:
+                            schema_data = schema_result
+                        
+                        if isinstance(schema_data, dict) and "tables" in schema_data:
+                            db_name = schema_data.get("database_name", "Unknown")
+                            db_type = schema_data.get("database_type", "Unknown")
+                            
+                            schema_text = f"### Database: {db_name} ({db_type})\n\n"
+                            
+                            for table_name, table_info in schema_data["tables"].items():
+                                schema_name = table_info.get("schema_name", "dbo")
+                                qualified_name = f"{schema_name}.{table_name}" if schema_name else table_name
+                                schema_text += f"**Table: {qualified_name}**\n"
+                                
+                                columns = table_info.get("columns", [])
+                                if columns:
+                                    schema_text += "| Column | Type | Nullable |\n|--------|------|----------|\n"
+                                    for col in columns:
+                                        col_name = col.get("column_name", "?")
+                                        col_type = col.get("data_type", "?")
+                                        nullable = "Yes" if col.get("is_nullable", True) else "No"
+                                        schema_text += f"| {col_name} | {col_type} | {nullable} |\n"
+                                
+                                pks = table_info.get("primary_keys", [])
+                                if pks:
+                                    schema_text += f"Primary Key(s): {', '.join(pks)}\n"
+                                
+                                schema_text += "\n"
+                            
+                            relationships = schema_data.get("relationships", [])
+                            if relationships:
+                                schema_text += "**Relationships (Foreign Keys):**\n"
+                                for rel in relationships:
+                                    parent = rel.get("parent_table", "?")
+                                    parent_col = rel.get("parent_column", "?")
+                                    ref = rel.get("referenced_table", "?")
+                                    ref_col = rel.get("referenced_column", "?")
+                                    schema_text += f"- {parent}.{parent_col} → {ref}.{ref_col}\n"
+                                schema_text += "\n"
+                            
+                            schema_parts.append(schema_text)
+                            print(f"[SK Loader] Fallback: Successfully extracted schema from SQLQueryPlugin '{plugin_name}': {len(schema_data['tables'])} tables")
+                    except Exception as e:
+                        print(f"[SK Loader] Fallback: Failed to extract schema from SQLQueryPlugin '{plugin_name}': {e}")
+                        log_event(f"[SK Loader] Fallback schema extraction failed",
+                                 extra={"plugin_name": plugin_name, "error": str(e)},
+                                 level=logging.WARNING)
+        except Exception as e:
+            print(f"[SK Loader] Warning: Error in fallback SQL schema extraction: {e}")
+            log_event(f"[SK Loader] Error in fallback SQL schema extraction: {e}",
+                     extra={"error": str(e)}, level=logging.WARNING)
+    
+    return "\n".join(schema_parts)
+
+
 def load_single_agent_for_kernel(kernel, agent_cfg, settings, context_obj, redis_client=None, mode_label="global"):
     """
     DRY helper to load a single agent (default agent) for the kernel.
@@ -859,6 +1060,27 @@ def load_single_agent_for_kernel(kernel, agent_cfg, settings, context_obj, redis
                 group_id=group_id,
             )
 
+            # Auto-inject SQL database schema into agent instructions if SQL plugins are loaded
+            try:
+                sql_schema_summary = _extract_sql_schema_for_instructions(kernel)
+                if sql_schema_summary:
+                    agent_config["instructions"] = (
+                        agent_config.get("instructions", "") +
+                        "\n\n## Available Database Schema\n"
+                        "The following database tables and columns are available for SQL queries. "
+                        "ALWAYS use these exact table and column names when writing SQL queries.\n\n" +
+                        sql_schema_summary +
+                        "\n\nWhen a user asks a question about data, use the schema above to construct "
+                        "the appropriate SQL query and execute it using the SQL Query plugin functions. "
+                        "Do NOT ask the user for table or column names — use the schema provided above."
+                    )
+                    print(f"[SK Loader] Injected SQL schema into agent instructions for {agent_config['name']}")
+            except Exception as e:
+                print(f"[SK Loader] Warning: Failed to inject SQL schema into instructions: {e}")
+                log_event(f"[SK Loader] Failed to inject SQL schema into agent instructions: {e}",
+                         extra={"agent_name": agent_config["name"], "error": str(e)},
+                         level=logging.WARNING)
+
         try:
             kwargs = {
                 "name": agent_config["name"],
@@ -1013,6 +1235,14 @@ def load_plugins_for_kernel(kernel, plugin_manifests, settings, mode_label="glob
         except Exception as e:
             log_event(f"[SK Loader] Failed to load Fact Memory Plugin: {e}", level=logging.WARNING)
 
+    # Register Tabular Processing Plugin if enabled (requires enhanced citations)
+    if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+        try:
+            load_tabular_processing_plugin(kernel)
+            log_event("[SK Loader] Loaded Tabular Processing plugin.", level=logging.INFO)
+        except Exception as e:
+            log_event(f"[SK Loader] Failed to load Tabular Processing plugin: {e}", level=logging.WARNING)
+
     # Conditionally load static embedding model plugin
     if settings.get('enable_default_embedding_model_plugin', True):
         try:
@@ -1357,7 +1587,11 @@ def load_user_semantic_kernel(kernel: Kernel, settings, user_id: str, redis_clie
         load_embedding_model_plugin(kernel, settings)
         print(f"[SK Loader] Loaded Default Embedding Model plugin.")
         log_event("[SK Loader] Loaded Default Embedding Model plugin.", level=logging.INFO)
-    
+
+    if settings.get('enable_tabular_processing_plugin', False) and settings.get('enable_enhanced_citations', False):
+        load_tabular_processing_plugin(kernel)
+        log_event("[SK Loader] Loaded Tabular Processing plugin.", level=logging.INFO)
+
     # Get selected agent from user settings (this still needs to be in user settings for UI state)
     user_settings = get_user_settings(user_id).get('settings', {})
     selected_agent = user_settings.get('selected_agent')
diff --git a/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py b/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py
index 64443633..f7c9e38c 100644
--- a/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py
+++ b/application/single_app/semantic_kernel_plugins/logged_plugin_loader.py
@@ -80,6 +80,10 @@ def load_plugin_from_manifest(self, manifest: Dict[str, Any],
             # Register the plugin with the kernel
             self._register_plugin_with_kernel(plugin_instance, plugin_name)
             
+            # Auto-create companion SQL Schema plugin when loading a SQL Query plugin
+            if plugin_type == 'sql_query':
+                self._auto_create_companion_schema_plugin(manifest, plugin_name)
+            
             log_event(
                 f"[Logged Plugin Loader] Successfully loaded plugin: {plugin_name}",
                 extra={
@@ -117,8 +121,8 @@ def _create_plugin_instance(self, manifest: Dict[str, Any]):
             return self._create_openapi_plugin(manifest)
         elif plugin_type == 'python':
             return self._create_python_plugin(manifest)
-        #elif plugin_type in ['sql_schema', 'sql_query']:
-        #    return self._create_sql_plugin(manifest)
+        elif plugin_type in ['sql_schema', 'sql_query']:
+            return self._create_sql_plugin(manifest)
         else:
             try:
                 debug_print(f"[Logged Plugin Loader] Attempting to discover plugin type: {plugin_type}")
@@ -221,6 +225,60 @@ def _create_sql_plugin(self, manifest: Dict[str, Any]):
             self.logger.error(f"Failed to import SQL plugin class for {plugin_type}: {e}")
             return None
     
+    def _auto_create_companion_schema_plugin(self, query_manifest: Dict[str, Any], query_plugin_name: str):
+        """
+        Auto-create a companion SQLSchemaPlugin when a SQLQueryPlugin is loaded.
+        This ensures the agent has access to database schema information for constructing queries.
+        The schema plugin uses the same connection details as the query plugin.
+        """
+        try:
+            # Derive schema plugin name from query plugin name
+            if query_plugin_name.endswith('_query'):
+                schema_plugin_name = query_plugin_name[:-6] + '_schema'
+            else:
+                schema_plugin_name = query_plugin_name + '_schema'
+            
+            # Check if schema plugin already exists in kernel
+            if schema_plugin_name in self.kernel.plugins:
+                log_event(
+                    f"[Logged Plugin Loader] Companion schema plugin already exists: {schema_plugin_name}",
+                    level=logging.DEBUG
+                )
+                return
+            
+            # Create schema manifest from query manifest (same connection details)
+            schema_manifest = dict(query_manifest)
+            schema_manifest['type'] = 'sql_schema'
+            schema_manifest['name'] = schema_plugin_name
+            
+            # Create the schema plugin instance
+            schema_instance = SQLSchemaPlugin(schema_manifest)
+            
+            # Enable logging if supported
+            if hasattr(schema_instance, 'enable_invocation_logging'):
+                schema_instance.enable_invocation_logging(True)
+            
+            # Wrap functions if it's a BasePlugin
+            if isinstance(schema_instance, BasePlugin):
+                self._wrap_plugin_functions(schema_instance, schema_plugin_name)
+            
+            # Register with kernel
+            self._register_plugin_with_kernel(schema_instance, schema_plugin_name)
+            
+            log_event(
+                f"[Logged Plugin Loader] Auto-created companion SQL Schema plugin: {schema_plugin_name}",
+                extra={"query_plugin": query_plugin_name, "schema_plugin": schema_plugin_name},
+                level=logging.INFO
+            )
+            
+        except Exception as e:
+            log_event(
+                f"[Logged Plugin Loader] Warning: Failed to auto-create companion schema plugin",
+                extra={"query_plugin": query_plugin_name, "error": str(e)},
+                level=logging.WARNING,
+                exceptionTraceback=True
+            )
+    
     def _wrap_plugin_functions(self, plugin_instance, plugin_name: str):
         """Wrap all kernel functions in a plugin with logging."""
         log_event(f"[Logged Plugin Loader] Checking logging status for plugin", 
diff --git a/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py b/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py
index f982f0a4..bddf9cda 100644
--- a/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py
+++ b/application/single_app/semantic_kernel_plugins/plugin_invocation_logger.py
@@ -11,6 +11,7 @@
 import logging
 import functools
 import inspect
+import threading
 from typing import Any, Dict, List, Optional, Callable
 from datetime import datetime
 from dataclasses import dataclass, asdict
@@ -51,24 +52,29 @@ def __init__(self):
         self.invocations: List[PluginInvocation] = []
         self.max_history = 1000  # Keep last 1000 invocations in memory
         self.logger = get_appinsights_logger() or logging.getLogger(__name__)
+        self._callbacks: Dict[str, List[Callable[[PluginInvocation], None]]] = {}
+        self._callback_lock = threading.Lock()
         
     def log_invocation(self, invocation: PluginInvocation):
         """Log a plugin invocation to Application Insights and local history."""
         # Add to local history
         self.invocations.append(invocation)
-        
+
         # Trim history if needed
         if len(self.invocations) > self.max_history:
             self.invocations = self.invocations[-self.max_history:]
-        
+
         # Enhanced terminal logging
         self._log_to_terminal(invocation)
-        
+
         # Log to Application Insights
         self._log_to_appinsights(invocation)
-        
+
         # Log to standard logging
         self._log_to_standard(invocation)
+
+        # Fire registered thought callbacks
+        self._fire_callbacks(invocation)
     
     def _log_to_terminal(self, invocation: PluginInvocation):
         """Log detailed invocation information to terminal."""
@@ -277,6 +283,34 @@ def clear_history(self):
         """Clear the invocation history."""
         self.invocations.clear()
 
+    def register_callback(self, key, callback):
+        """Register a callback fired on each plugin invocation for the given key.
+
+        Args:
+            key: A string key, typically f"{user_id}:{conversation_id}".
+            callback: Called with the PluginInvocation after it is logged.
+        """
+        with self._callback_lock:
+            if key not in self._callbacks:
+                self._callbacks[key] = []
+            self._callbacks[key].append(callback)
+
+    def deregister_callbacks(self, key):
+        """Remove all callbacks for the given key."""
+        with self._callback_lock:
+            self._callbacks.pop(key, None)
+
+    def _fire_callbacks(self, invocation):
+        """Fire matching callbacks for this invocation's user+conversation."""
+        key = f"{invocation.user_id}:{invocation.conversation_id}"
+        with self._callback_lock:
+            callbacks = list(self._callbacks.get(key, []))
+        for cb in callbacks:
+            try:
+                cb(invocation)
+            except Exception as e:
+                log_event(f"Plugin invocation callback error: {e}", level="WARNING")
+
 
 # Global instance
 _plugin_logger = PluginInvocationLogger()
diff --git a/application/single_app/semantic_kernel_plugins/sql_query_plugin.py b/application/single_app/semantic_kernel_plugins/sql_query_plugin.py
index ccad030f..084c4c9b 100644
--- a/application/single_app/semantic_kernel_plugins/sql_query_plugin.py
+++ b/application/single_app/semantic_kernel_plugins/sql_query_plugin.py
@@ -176,11 +176,12 @@ def metadata(self) -> Dict[str, Any]:
         user_desc = self._metadata.get("description", f"SQL Query plugin for {self.database_type} database")
         api_desc = (
             "This plugin executes SQL queries against databases and returns structured results. "
-            "It supports SQL Server, PostgreSQL, MySQL, and SQLite databases. The plugin includes "
-            "query sanitization, validation, and security features including parameterized queries, "
-            "read-only mode, result limiting, and timeout protection. It automatically cleans queries "
-            "from unnecessary characters and formats results for easy consumption by AI agents. "
-            "The plugin handles database-specific SQL variations and connection management."
+            "It supports SQL Server, PostgreSQL, MySQL, and SQLite databases. "
+            "WORKFLOW: Before executing any query, you MUST first use the SQL Schema plugin to discover "
+            "available tables, column names, data types, and relationships. Then construct valid SQL queries "
+            "using the discovered schema with correct fully-qualified table names (e.g., dbo.TableName). "
+            "The plugin includes query sanitization, validation, and security features including "
+            "parameterized queries, read-only mode, result limiting, and timeout protection."
         )
         full_desc = f"{user_desc}\n\n{api_desc}"
         
@@ -215,14 +216,24 @@ def metadata(self) -> Dict[str, Any]:
                         {"name": "query", "type": "str", "description": "The SQL query to validate", "required": True}
                     ],
                     "returns": {"type": "ResultWithMetadata", "description": "Validation result with any issues found"}
+                },
+                {
+                    "name": "query_database",
+                    "description": "Execute a SQL query to answer a question about the database",
+                    "parameters": [
+                        {"name": "question", "type": "str", "description": "The natural language question being answered", "required": True},
+                        {"name": "query", "type": "str", "description": "The SQL query to execute", "required": True},
+                        {"name": "max_rows", "type": "int", "description": "Maximum number of rows to return (overrides default)", "required": False}
+                    ],
+                    "returns": {"type": "ResultWithMetadata", "description": "Query results with columns, data, and original question context"}
                 }
             ]
         }
 
     def get_functions(self) -> List[str]:
-        return ["execute_query", "execute_scalar", "validate_query"]
+        return ["execute_query", "execute_scalar", "validate_query", "query_database"]
 
-    @kernel_function(description="Execute a SQL query and return results")
+    @kernel_function(description="Execute a SQL query against the database and return results as structured data with columns and rows. If the database schema is provided in your instructions, use those exact table and column names to construct valid SQL queries. If no schema is available in your instructions, call get_database_schema or get_table_list from the SQL Schema plugin to discover tables first. Always use fully qualified table names (e.g., dbo.TableName) when available. Results are limited by max_rows to prevent excessive data transfer.")
     @plugin_function_logger("SQLQueryPlugin")
     def execute_query(
         self, 
@@ -301,7 +312,7 @@ def execute_query(
             }
             return ResultWithMetadata(error_result, self.metadata)
 
-    @kernel_function(description="Execute a query that returns a single value")
+    @kernel_function(description="Execute a query that returns a single scalar value (e.g., COUNT, SUM, MAX, MIN). If the database schema is provided in your instructions, use it directly to construct the query. Otherwise, call get_database_schema from the SQL Schema plugin first to discover table and column names.")
     @plugin_function_logger("SQLQueryPlugin")
     def execute_scalar(
         self, 
@@ -360,7 +371,7 @@ def execute_scalar(
             }
             return ResultWithMetadata(error_result, self.metadata)
 
-    @kernel_function(description="Validate a SQL query without executing it")
+    @kernel_function(description="Validate a SQL query for syntax correctness and safety without executing it. Use this to pre-check complex queries before execution, especially when constructing multi-table JOINs or complex WHERE clauses.")
     @plugin_function_logger("SQLQueryPlugin")
     def validate_query(self, query: str) -> ResultWithMetadata:
         """Validate a SQL query without executing it"""
@@ -380,6 +391,80 @@ def validate_query(self, query: str) -> ResultWithMetadata:
             }
             return ResultWithMetadata(error_result, self.metadata)
 
+    @kernel_function(description="Execute a SQL query to answer a question about the database. This is a convenience function that executes a SQL query and returns results along with the original question for context. If the database schema is provided in your instructions, use those table and column names directly to construct the query. Otherwise, first call get_database_schema from the SQL Schema plugin to discover the schema. Then construct the appropriate SQL query and provide it along with the original question.")
+    @plugin_function_logger("SQLQueryPlugin")
+    def query_database(
+        self,
+        question: str,
+        query: str,
+        max_rows: Optional[int] = None
+    ) -> ResultWithMetadata:
+        """Execute a SQL query to answer a specific question about the database"""
+        try:
+            # Clean and validate the query
+            cleaned_query = self._clean_query(query)
+            validation_result = self._validate_query(cleaned_query)
+            
+            if not validation_result["is_valid"]:
+                raise ValueError(f"Invalid query: {validation_result['issues']}")
+            
+            conn = self._get_connection()
+            cursor = conn.cursor()
+            
+            # Set query timeout
+            if hasattr(cursor, 'settimeout'):
+                cursor.settimeout(self.timeout)
+            
+            cursor.execute(cleaned_query)
+            
+            # Get column names
+            if hasattr(cursor, 'description') and cursor.description:
+                columns = [desc[0] for desc in cursor.description]
+            else:
+                columns = []
+            
+            # Fetch results with row limit
+            effective_max_rows = max_rows or self.max_rows
+            
+            if self.database_type == 'sqlite':
+                rows = cursor.fetchall()
+                if len(rows) > effective_max_rows:
+                    rows = rows[:effective_max_rows]
+                results = [dict(row) for row in rows]
+            else:
+                rows = cursor.fetchmany(effective_max_rows)
+                results = []
+                for row in rows:
+                    if isinstance(row, (list, tuple)):
+                        results.append(dict(zip(columns, row)))
+                    else:
+                        results.append(row)
+            
+            # Prepare result data with question context
+            result_data = {
+                "question": question,
+                "columns": columns,
+                "data": results,
+                "row_count": len(results),
+                "is_truncated": len(results) >= effective_max_rows,
+                "query": cleaned_query
+            }
+            
+            log_event(f"[SQLQueryPlugin] query_database executed successfully, returned {len(results)} rows", extra={"question": question})
+            return ResultWithMetadata(result_data, self.metadata)
+            
+        except Exception as e:
+            log_event(f"[SQLQueryPlugin] Error in query_database: {e}", extra={"question": question})
+            error_result = {
+                "error": str(e),
+                "question": question,
+                "query": query,
+                "columns": [],
+                "data": [],
+                "row_count": 0
+            }
+            return ResultWithMetadata(error_result, self.metadata)
+
     def _clean_query(self, query: str) -> str:
         """Clean query from unnecessary characters and formatting"""
         if not query:
diff --git a/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py b/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py
index 01d89aa2..7d5737f3 100644
--- a/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py
+++ b/application/single_app/semantic_kernel_plugins/sql_schema_plugin.py
@@ -165,11 +165,11 @@ def metadata(self) -> Dict[str, Any]:
         user_desc = self._metadata.get("description", f"SQL Schema plugin for {self.database_type} database")
         api_desc = (
             "This plugin connects to SQL databases and extracts schema information including tables, columns, "
-            "data types, primary keys, foreign keys, and relationships. It supports SQL Server, PostgreSQL, "
-            "MySQL, and SQLite databases. The plugin provides structured schema data that can be used by "
-            "AI agents to understand database structure and generate appropriate SQL queries. "
-            "Authentication supports connection strings, username/password, and integrated authentication. "
-            "The plugin handles database-specific SQL variations for schema extraction."
+            "data types, primary keys, foreign keys, and relationships. WORKFLOW: ALWAYS call get_database_schema "
+            "or get_table_list FIRST before executing any SQL queries via the SQL Query plugin. This ensures "
+            "you have accurate table names, column names, and relationship information to construct valid queries. "
+            "It supports SQL Server, PostgreSQL, MySQL, and SQLite databases. "
+            "Authentication supports connection strings, username/password, and integrated authentication."
         )
         full_desc = f"{user_desc}\n\n{api_desc}"
         
@@ -219,7 +219,7 @@ def get_functions(self) -> List[str]:
         return ["get_database_schema", "get_table_schema", "get_table_list", "get_relationships"]
 
     @plugin_function_logger("SQLSchemaPlugin")
-    @kernel_function(description="Get complete database schema including all tables, columns, and relationships")
+    @kernel_function(description="Get complete database schema including all tables, columns, data types, primary keys, foreign keys, and relationships. If the database schema is already provided in your instructions, use that directly and do NOT call this function. Only call this function if you need to discover the schema and it was not already provided. The returned schema should be used to construct valid SQL queries with the correct fully-qualified table names (e.g., dbo.TableName) and column references.")
     def get_database_schema(
         self, 
         include_system_tables: bool = False,
@@ -255,24 +255,26 @@ def get_database_schema(
             
             # Get schema for each table
             for table in tables:
-                if isinstance(table, tuple) and len(table) >= 2:
+                try:
+                    # Robust row parsing — works with pyodbc.Row, tuple, list, etc.
                     table_name = table[0]
-                    schema_name = table[1]
-                    qualified_table_name = f"{schema_name}.{table_name}"
-                else:
-                    table_name = table[0] if isinstance(table, tuple) else table
+                    schema_name = table[1] if len(table) >= 2 else None
+                    qualified_table_name = f"{schema_name}.{table_name}" if schema_name else str(table_name)
+                except (TypeError, IndexError):
+                    table_name = str(table)
                     schema_name = None
                     qualified_table_name = table_name
                     
                 try:
-                    table_schema = self._get_table_schema_data(cursor, table_name, schema_name)
-                    schema_data["tables"][table_name] = table_schema
-                    print(f"[SQLSchemaPlugin] Got schema for table: {qualified_table_name}")
+                    table_schema = self._get_table_schema_data(cursor, str(table_name), str(schema_name) if schema_name else None)
+                    schema_data["tables"][str(table_name)] = table_schema
+                    print(f"[SQLSchemaPlugin] Got schema for table: {qualified_table_name} ({len(table_schema.get('columns', []))} columns)")
                 except Exception as e:
                     print(f"[SQLSchemaPlugin] Error getting schema for table {qualified_table_name}: {e}")
                     log_event(f"[SQLSchemaPlugin] Error getting table schema", extra={
                         "table_name": qualified_table_name,
-                        "error": str(e)
+                        "error": str(e),
+                        "raw_row": repr(table)
                     })
             
             # Get relationships
@@ -333,7 +335,7 @@ def get_database_schema(
             log_event(f"[SQLSchemaPlugin] Error getting database schema: {e}")
             raise
 
-    @kernel_function(description="Get detailed schema for a specific table")
+    @kernel_function(description="Get the detailed schema (column names, data types, constraints) for a specific table. If the database schema is already provided in your instructions, use that directly instead of calling this function. Only call this if you need details for a specific table not already in your instructions.")
     @plugin_function_logger("SQLSchemaPlugin")
     def get_table_schema(self, table_name: str) -> ResultWithMetadata:
         """Get detailed schema for a specific table"""
@@ -350,7 +352,7 @@ def get_table_schema(self, table_name: str) -> ResultWithMetadata:
             log_event(f"[SQLSchemaPlugin] Error getting table schema for {table_name}: {e}")
             raise
 
-    @kernel_function(description="Get list of all tables in the database")
+    @kernel_function(description="Return the names of all tables in the database. If the database schema is already provided in your instructions, use that directly instead of calling this function. Only call this if you need to discover available tables and they are not already listed in your instructions.")
     @plugin_function_logger("SQLSchemaPlugin")
     def get_table_list(
         self, 
@@ -368,14 +370,14 @@ def get_table_list(
             
             table_list = []
             for table_row in tables:
-                if isinstance(table_row, (list, tuple)):
+                try:
                     table_info = {
-                        "table_name": table_row[0],
-                        "schema": table_row[1] if len(table_row) > 1 else None,
-                        "table_type": table_row[2] if len(table_row) > 2 else "TABLE"
+                        "table_name": str(table_row[0]),
+                        "schema": str(table_row[1]) if len(table_row) > 1 else None,
+                        "table_type": str(table_row[2]) if len(table_row) > 2 else "TABLE"
                     }
-                else:
-                    table_info = {"table_name": table_row, "schema": None, "table_type": "TABLE"}
+                except (TypeError, IndexError):
+                    table_info = {"table_name": str(table_row), "schema": None, "table_type": "TABLE"}
                 table_list.append(table_info)
             
             log_event(f"[SQLSchemaPlugin] Retrieved {len(table_list)} tables")
@@ -385,7 +387,7 @@ def get_table_list(
             log_event(f"[SQLSchemaPlugin] Error getting table list: {e}")
             raise
 
-    @kernel_function(description="Get foreign key relationships between tables")
+    @kernel_function(description="Get foreign key relationships between tables. If the database schema and relationships are already provided in your instructions, use those directly instead of calling this function. Only call this if you need relationship details not already in your instructions.")
     def get_relationships(self, table_name: Optional[str] = None) -> ResultWithMetadata:
         """Get foreign key relationships between tables"""
         try:
@@ -402,17 +404,20 @@ def get_relationships(self, table_name: Optional[str] = None) -> ResultWithMetad
             raise
 
     def _get_tables_query(self, include_system_tables: bool, table_filter: Optional[str]) -> str:
-        """Get database-specific query for listing tables"""
+        """Get database-specific query for listing tables.
+        Uses sys.tables/sys.schemas for SQL Server (more reliable than INFORMATION_SCHEMA
+        in Azure SQL environments with restricted permissions)."""
         if self.database_type == 'sqlserver':
             base_query = """
-                SELECT TABLE_NAME, TABLE_SCHEMA, TABLE_TYPE 
-                FROM INFORMATION_SCHEMA.TABLES 
-                WHERE TABLE_TYPE = 'BASE TABLE'
+                SELECT t.name AS TABLE_NAME, s.name AS TABLE_SCHEMA, 'BASE TABLE' AS TABLE_TYPE
+                FROM sys.tables t
+                INNER JOIN sys.schemas s ON t.schema_id = s.schema_id
+                WHERE t.type = 'U'
             """
             if not include_system_tables:
-                base_query += " AND TABLE_SCHEMA NOT IN ('sys', 'information_schema')"
+                base_query += " AND s.name NOT IN ('sys', 'information_schema')"
             if table_filter:
-                base_query += f" AND TABLE_NAME LIKE '{table_filter.replace('*', '%')}'"
+                base_query += f" AND t.name LIKE '{table_filter.replace('*', '%')}'"
             return base_query
             
         elif self.database_type == 'postgresql':
@@ -467,22 +472,30 @@ def _get_table_schema_data(self, cursor, table_name: str, schema_name: str = Non
         if pk_query:
             cursor.execute(pk_query)
             pks = cursor.fetchall()
-            schema_data["primary_keys"] = [pk[0] if isinstance(pk, (list, tuple)) else pk for pk in pks]
+            schema_data["primary_keys"] = [str(pk[0]) for pk in pks]
         
         return schema_data
 
     def _get_columns_query(self, table_name: str, schema_name: str = None) -> str:
-        """Get database-specific query for table columns"""
+        """Get database-specific query for table columns.
+        Uses sys.columns/sys.types for SQL Server (consistent with sys.tables used for enumeration)."""
         if self.database_type == 'sqlserver':
-            where_clause = f"WHERE TABLE_NAME = '{table_name}'"
-            if schema_name:
-                where_clause += f" AND TABLE_SCHEMA = '{schema_name}'"
+            schema_filter = f"AND s.name = '{schema_name}'" if schema_name else ""
             return f"""
-                SELECT COLUMN_NAME, DATA_TYPE, IS_NULLABLE, COLUMN_DEFAULT, 
-                       CHARACTER_MAXIMUM_LENGTH, NUMERIC_PRECISION, NUMERIC_SCALE
-                FROM INFORMATION_SCHEMA.COLUMNS 
-                {where_clause}
-                ORDER BY ORDINAL_POSITION
+                SELECT 
+                    c.name AS COLUMN_NAME,
+                    TYPE_NAME(c.user_type_id) AS DATA_TYPE,
+                    CASE WHEN c.is_nullable = 1 THEN 'YES' ELSE 'NO' END AS IS_NULLABLE,
+                    dc.definition AS COLUMN_DEFAULT,
+                    c.max_length AS CHARACTER_MAXIMUM_LENGTH,
+                    c.precision AS NUMERIC_PRECISION,
+                    c.scale AS NUMERIC_SCALE
+                FROM sys.columns c
+                INNER JOIN sys.tables t ON c.object_id = t.object_id
+                INNER JOIN sys.schemas s ON t.schema_id = s.schema_id
+                LEFT JOIN sys.default_constraints dc ON c.default_object_id = dc.object_id
+                WHERE t.name = '{table_name}' {schema_filter}
+                ORDER BY c.column_id
             """
         elif self.database_type == 'postgresql':
             return f"""
@@ -498,16 +511,18 @@ def _get_columns_query(self, table_name: str, schema_name: str = None) -> str:
             return f"PRAGMA table_info({table_name})"
 
     def _get_primary_keys_query(self, table_name: str, schema_name: str = None) -> Optional[str]:
-        """Get database-specific query for primary keys"""
+        """Get database-specific query for primary keys.
+        Uses sys.indexes/sys.index_columns for SQL Server (consistent with sys.tables)."""
         if self.database_type == 'sqlserver':
-            where_clause = f"WHERE TABLE_NAME = '{table_name}'"
-            if schema_name:
-                where_clause += f" AND TABLE_SCHEMA = '{schema_name}'"
+            schema_filter = f"AND s.name = '{schema_name}'" if schema_name else ""
             return f"""
-                SELECT COLUMN_NAME
-                FROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE
-                {where_clause}
-                AND CONSTRAINT_NAME LIKE 'PK_%'
+                SELECT c.name AS COLUMN_NAME
+                FROM sys.index_columns ic
+                INNER JOIN sys.columns c ON ic.object_id = c.object_id AND ic.column_id = c.column_id
+                INNER JOIN sys.indexes i ON ic.object_id = i.object_id AND ic.index_id = i.index_id
+                INNER JOIN sys.tables t ON i.object_id = t.object_id
+                INNER JOIN sys.schemas s ON t.schema_id = s.schema_id
+                WHERE i.is_primary_key = 1 AND t.name = '{table_name}' {schema_filter}
             """
         elif self.database_type == 'postgresql':
             return f"""
diff --git a/application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py b/application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py
new file mode 100644
index 00000000..1857099e
--- /dev/null
+++ b/application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py
@@ -0,0 +1,1366 @@
+# tabular_processing_plugin.py
+"""
+TabularProcessingPlugin for Semantic Kernel: provides data analysis operations
+on tabular files (CSV, XLSX, XLS, XLSM) stored in Azure Blob Storage.
+
+Works with workspace documents (user-documents, group-documents, public-documents)
+and chat-uploaded documents (personal-chat container).
+"""
+import asyncio
+import copy
+from datetime import date, datetime
+import io
+import json
+import logging
+import warnings
+import pandas
+from typing import Annotated, Optional, List
+from semantic_kernel.functions import kernel_function
+from semantic_kernel_plugins.plugin_invocation_logger import plugin_function_logger
+from functions_appinsights import log_event
+from config import (
+    CLIENTS,
+    TABULAR_EXTENSIONS,
+    storage_account_user_documents_container_name,
+    storage_account_personal_chat_container_name,
+    storage_account_group_documents_container_name,
+    storage_account_public_documents_container_name,
+)
+
+
+class TabularProcessingPlugin:
+    """Provides data analysis functions on tabular files stored in blob storage."""
+
+    SUPPORTED_EXTENSIONS = {'.csv', '.xlsx', '.xls', '.xlsm'}
+    DAY_NAME_ORDER = [
+        'Monday',
+        'Tuesday',
+        'Wednesday',
+        'Thursday',
+        'Friday',
+        'Saturday',
+        'Sunday'
+    ]
+    MONTH_NAME_ORDER = [
+        'January',
+        'February',
+        'March',
+        'April',
+        'May',
+        'June',
+        'July',
+        'August',
+        'September',
+        'October',
+        'November',
+        'December'
+    ]
+
+    def __init__(self):
+        self._df_cache = {}  # Per-instance cache: (container, blob_name, sheet_name) -> DataFrame
+        self._blob_data_cache = {}  # Per-instance cache: (container, blob_name) -> raw bytes
+        self._workbook_metadata_cache = {}  # Per-instance cache: (container, blob_name) -> workbook metadata
+        self._default_sheet_overrides = {}  # (container, blob_name) -> default sheet name
+
+    def set_default_sheet(self, container_name: str, blob_name: str, sheet_name: str):
+        """Set the default sheet for a workbook so the model doesn't need to specify it."""
+        self._default_sheet_overrides[(container_name, blob_name)] = sheet_name
+
+    def _get_blob_service_client(self):
+        """Get the blob service client from CLIENTS cache."""
+        client = CLIENTS.get("storage_account_office_docs_client")
+        if not client:
+            raise RuntimeError("Blob storage client not available. Enhanced citations must be enabled.")
+        return client
+
+    def _list_tabular_blobs(self, container_name: str, prefix: str) -> List[str]:
+        """List all tabular file blobs under a given prefix."""
+        client = self._get_blob_service_client()
+        container_client = client.get_container_client(container_name)
+        blobs = []
+        for blob in container_client.list_blobs(name_starts_with=prefix):
+            name_lower = blob['name'].lower()
+            if any(name_lower.endswith(ext) for ext in self.SUPPORTED_EXTENSIONS):
+                blobs.append(blob['name'])
+        return blobs
+
+    def _download_tabular_blob_bytes(self, container_name: str, blob_name: str) -> bytes:
+        """Download a blob once and reuse the raw bytes across sheet-aware operations."""
+        cache_key = (container_name, blob_name)
+        if cache_key in self._blob_data_cache:
+            return self._blob_data_cache[cache_key]
+
+        client = self._get_blob_service_client()
+        blob_client = client.get_blob_client(container=container_name, blob=blob_name)
+        stream = blob_client.download_blob()
+        data = stream.readall()
+        self._blob_data_cache[cache_key] = data
+        return data
+
+    def _get_excel_engine(self, blob_name: str) -> Optional[str]:
+        """Return the pandas Excel engine for a workbook, or None for CSV files."""
+        name_lower = blob_name.lower()
+        if name_lower.endswith('.xlsx') or name_lower.endswith('.xlsm'):
+            return 'openpyxl'
+        if name_lower.endswith('.xls'):
+            return 'xlrd'
+        return None
+
+    def _get_workbook_metadata(self, container_name: str, blob_name: str) -> dict:
+        """Return workbook metadata including available sheet names for Excel files."""
+        cache_key = (container_name, blob_name)
+        if cache_key in self._workbook_metadata_cache:
+            return copy.deepcopy(self._workbook_metadata_cache[cache_key])
+
+        engine = self._get_excel_engine(blob_name)
+        metadata = {
+            'is_workbook': bool(engine),
+            'sheet_names': [],
+            'sheet_count': 0,
+            'default_sheet': None,
+        }
+
+        if engine:
+            data = self._download_tabular_blob_bytes(container_name, blob_name)
+            excel_file = pandas.ExcelFile(io.BytesIO(data), engine=engine)
+            sheet_names = list(excel_file.sheet_names)
+            metadata.update({
+                'sheet_names': sheet_names,
+                'sheet_count': len(sheet_names),
+                'default_sheet': sheet_names[0] if sheet_names else None,
+            })
+
+        self._workbook_metadata_cache[cache_key] = copy.deepcopy(metadata)
+        return copy.deepcopy(metadata)
+
+    def _resolve_sheet_selection(
+        self,
+        container_name: str,
+        blob_name: str,
+        sheet_name: Optional[str] = None,
+        sheet_index: Optional[str] = None,
+        require_explicit_sheet: bool = False,
+    ) -> tuple:
+        """Resolve a workbook sheet selection and enforce explicit choice when required."""
+        workbook_metadata = self._get_workbook_metadata(container_name, blob_name)
+        if not workbook_metadata.get('is_workbook'):
+            return None, workbook_metadata
+
+        available_sheets = workbook_metadata.get('sheet_names', [])
+        if not available_sheets:
+            raise ValueError(f"Workbook '{blob_name}' does not contain any readable sheets.")
+
+        normalized_sheet_name = (sheet_name or '').strip()
+        if normalized_sheet_name:
+            for candidate in available_sheets:
+                if candidate == normalized_sheet_name:
+                    return candidate, workbook_metadata
+            for candidate in available_sheets:
+                if candidate.lower() == normalized_sheet_name.lower():
+                    return candidate, workbook_metadata
+            raise ValueError(
+                f"Sheet '{normalized_sheet_name}' was not found in workbook '{blob_name}'. "
+                f"Available sheets: {available_sheets}."
+            )
+
+        normalized_sheet_index = None if sheet_index is None else str(sheet_index).strip()
+        if normalized_sheet_index not in (None, ''):
+            try:
+                resolved_sheet_index = int(normalized_sheet_index)
+            except ValueError as exc:
+                raise ValueError(
+                    f"sheet_index must be an integer for workbook '{blob_name}'."
+                ) from exc
+
+            if resolved_sheet_index < 0 or resolved_sheet_index >= len(available_sheets):
+                raise ValueError(
+                    f"sheet_index {resolved_sheet_index} is out of range for workbook '{blob_name}'. "
+                    f"Available sheets: {available_sheets}."
+                )
+            return available_sheets[resolved_sheet_index], workbook_metadata
+
+        if len(available_sheets) == 1:
+            return available_sheets[0], workbook_metadata
+
+        # Use pre-selected default sheet if one was set by the orchestration layer
+        override_key = (container_name, blob_name)
+        if override_key in self._default_sheet_overrides:
+            override_sheet = self._default_sheet_overrides[override_key]
+            for candidate in available_sheets:
+                if candidate == override_sheet or candidate.lower() == override_sheet.lower():
+                    return candidate, workbook_metadata
+
+        if require_explicit_sheet:
+            raise ValueError(
+                f"Workbook '{blob_name}' has multiple sheets: {available_sheets}. "
+                "Specify sheet_name or sheet_index on analytical calls."
+            )
+
+        return workbook_metadata.get('default_sheet'), workbook_metadata
+
+    def _format_datetime_column_label(self, value) -> str:
+        """Render date-like Excel header labels into stable analysis-friendly strings."""
+        timestamp_value = pandas.Timestamp(value)
+
+        if (
+            timestamp_value.hour == 0
+            and timestamp_value.minute == 0
+            and timestamp_value.second == 0
+            and timestamp_value.microsecond == 0
+        ):
+            if timestamp_value.day == 1:
+                return timestamp_value.strftime('%b-%y')
+            return timestamp_value.strftime('%Y-%m-%d')
+
+        return timestamp_value.strftime('%Y-%m-%d %H:%M:%S')
+
+    def _normalize_column_label(self, label, fallback_index: int) -> str:
+        """Convert arbitrary DataFrame column labels into stable string names."""
+        if label is None or (not isinstance(label, str) and pandas.isna(label)):
+            return f"Column {fallback_index}"
+
+        if isinstance(label, pandas.Timestamp):
+            return self._format_datetime_column_label(label)
+
+        if isinstance(label, datetime):
+            return self._format_datetime_column_label(label)
+
+        if isinstance(label, date):
+            return self._format_datetime_column_label(datetime.combine(label, datetime.min.time()))
+
+        normalized_label = str(label).strip()
+        return normalized_label or f"Column {fallback_index}"
+
+    def _normalize_dataframe_columns(self, df: pandas.DataFrame) -> pandas.DataFrame:
+        """Rename DataFrame columns to unique, JSON-safe string labels."""
+        normalized_df = df.copy()
+        normalized_columns = []
+        normalized_label_counts = {}
+
+        for column_index, column_label in enumerate(normalized_df.columns, start=1):
+            base_label = self._normalize_column_label(column_label, column_index)
+            occurrence_count = normalized_label_counts.get(base_label, 0) + 1
+            normalized_label_counts[base_label] = occurrence_count
+
+            if occurrence_count == 1:
+                normalized_columns.append(base_label)
+            else:
+                normalized_columns.append(f"{base_label} ({occurrence_count})")
+
+        normalized_df.columns = normalized_columns
+        return normalized_df
+
+    def _build_sheet_schema_summary(self, df: pandas.DataFrame, sheet_name: Optional[str], preview_rows: int = 3) -> dict:
+        """Build a compact schema summary for a single table or worksheet."""
+        df = self._normalize_dataframe_columns(df)
+        df_numeric = self._try_numeric_conversion(df.copy())
+        return {
+            'selected_sheet': sheet_name,
+            'row_count': len(df),
+            'column_count': len(df.columns),
+            'columns': list(df.columns),
+            'dtypes': {col: str(dtype) for col, dtype in df_numeric.dtypes.items()},
+            'preview': df.head(preview_rows).to_dict(orient='records'),
+            'null_counts': df.isnull().sum().to_dict(),
+        }
+
+    def _build_workbook_schema_summary(self, container_name: str, blob_name: str, filename: str, preview_rows: int = 3) -> dict:
+        """Build a workbook-aware schema summary for prompt preload and file description."""
+        workbook_metadata = self._get_workbook_metadata(container_name, blob_name)
+        if not workbook_metadata.get('is_workbook'):
+            df = self._read_tabular_blob_to_dataframe(container_name, blob_name)
+            summary = self._build_sheet_schema_summary(df, None, preview_rows=preview_rows)
+            summary.update({
+                'filename': filename,
+                'is_workbook': False,
+                'sheet_names': [],
+                'sheet_count': 0,
+            })
+            return summary
+
+        per_sheet_schemas = {}
+        for workbook_sheet_name in workbook_metadata.get('sheet_names', []):
+            df = self._read_tabular_blob_to_dataframe(
+                container_name,
+                blob_name,
+                sheet_name=workbook_sheet_name,
+            )
+            per_sheet_schemas[workbook_sheet_name] = self._build_sheet_schema_summary(
+                df,
+                workbook_sheet_name,
+                preview_rows=preview_rows,
+            )
+
+        return {
+            'filename': filename,
+            'is_workbook': True,
+            'sheet_names': workbook_metadata.get('sheet_names', []),
+            'sheet_count': workbook_metadata.get('sheet_count', 0),
+            'selected_sheet': None,
+            'per_sheet_schemas': per_sheet_schemas,
+        }
+
+    def _read_tabular_blob_to_dataframe(
+        self,
+        container_name: str,
+        blob_name: str,
+        sheet_name: Optional[str] = None,
+        sheet_index: Optional[str] = None,
+        require_explicit_sheet: bool = False,
+    ) -> pandas.DataFrame:
+        """Download a blob and read it into a pandas DataFrame. Uses per-instance cache."""
+        resolved_sheet_name, workbook_metadata = self._resolve_sheet_selection(
+            container_name,
+            blob_name,
+            sheet_name=sheet_name,
+            sheet_index=sheet_index,
+            require_explicit_sheet=require_explicit_sheet,
+        )
+        sheet_cache_key = resolved_sheet_name or '__default__'
+        cache_key = (container_name, blob_name, sheet_cache_key)
+        if cache_key in self._df_cache:
+            log_event(
+                f"[TabularProcessingPlugin] Cache hit for {blob_name}"
+                + (f" [{resolved_sheet_name}]" if resolved_sheet_name else ''),
+                level=logging.DEBUG,
+            )
+            return self._df_cache[cache_key].copy()
+
+        data = self._download_tabular_blob_bytes(container_name, blob_name)
+
+        name_lower = blob_name.lower()
+        if name_lower.endswith('.csv'):
+            df = pandas.read_csv(io.BytesIO(data), keep_default_na=False, dtype=str)
+        elif name_lower.endswith('.xlsx') or name_lower.endswith('.xlsm'):
+            df = pandas.read_excel(
+                io.BytesIO(data),
+                engine='openpyxl',
+                keep_default_na=False,
+                dtype=str,
+                sheet_name=resolved_sheet_name,
+            )
+        elif name_lower.endswith('.xls'):
+            df = pandas.read_excel(
+                io.BytesIO(data),
+                engine='xlrd',
+                keep_default_na=False,
+                dtype=str,
+                sheet_name=resolved_sheet_name,
+            )
+        else:
+            raise ValueError(f"Unsupported tabular file type: {blob_name}")
+
+        df = self._normalize_dataframe_columns(df)
+        self._df_cache[cache_key] = df
+        log_event(
+            f"[TabularProcessingPlugin] Cached DataFrame for {blob_name}"
+            + (f" [{resolved_sheet_name}]" if resolved_sheet_name else '')
+            + f" ({len(df)} rows)",
+            level=logging.DEBUG,
+        )
+        return df.copy()
+
+    def _try_numeric_conversion(self, df: pandas.DataFrame) -> pandas.DataFrame:
+        """Attempt to convert string columns to numeric where possible."""
+        for col in df.columns:
+            if pandas.api.types.is_datetime64_any_dtype(df[col]) or pandas.api.types.is_timedelta64_dtype(df[col]):
+                continue
+            try:
+                df[col] = pandas.to_numeric(df[col])
+            except (ValueError, TypeError):
+                pass
+        return df
+
+    def _parse_datetime_like_series(self, series: pandas.Series) -> pandas.Series:
+        """Best-effort parsing for datetime and time-like values."""
+        if pandas.api.types.is_datetime64_any_dtype(series):
+            return pandas.to_datetime(series, errors='coerce')
+
+        cleaned_series = series.astype(str).str.strip()
+        cleaned_series = cleaned_series.replace({
+            '': None,
+            'nan': None,
+            'NaN': None,
+            'nat': None,
+            'NaT': None,
+            'none': None,
+            'None': None,
+        })
+
+        parsed = pandas.Series(pandas.NaT, index=series.index, dtype='datetime64[ns]')
+
+        common_formats = [
+            '%m/%d/%Y %I:%M:%S %p',
+            '%m/%d/%Y %I:%M %p',
+            '%m/%d/%Y %H:%M:%S',
+            '%m/%d/%Y %H:%M',
+            '%Y-%m-%d %H:%M:%S',
+            '%Y-%m-%d %H:%M',
+            '%Y-%m-%dT%H:%M:%S',
+            '%Y-%m-%dT%H:%M:%S.%f',
+            '%Y-%m-%d',
+            '%m/%d/%Y',
+        ]
+
+        for datetime_format in common_formats:
+            remaining_mask = parsed.isna() & cleaned_series.notna()
+            if not remaining_mask.any():
+                break
+
+            parsed.loc[remaining_mask] = pandas.to_datetime(
+                cleaned_series[remaining_mask],
+                format=datetime_format,
+                errors='coerce'
+            )
+
+        remaining_mask = parsed.isna() & cleaned_series.notna()
+        if remaining_mask.any():
+            digits = cleaned_series[remaining_mask].str.replace(r'[^0-9]', '', regex=True)
+
+            hhmm_mask = digits.str.match(r'^\d{3,4}$', na=False)
+            if hhmm_mask.any():
+                hhmm_values = digits[hhmm_mask].str.zfill(4)
+                parsed.loc[hhmm_values.index] = pandas.to_datetime(
+                    hhmm_values,
+                    format='%H%M',
+                    errors='coerce'
+                )
+
+            remaining_mask = parsed.isna() & cleaned_series.notna()
+            if remaining_mask.any():
+                digits = cleaned_series[remaining_mask].str.replace(r'[^0-9]', '', regex=True)
+                hhmmss_mask = digits.str.match(r'^\d{5,6}$', na=False)
+                if hhmmss_mask.any():
+                    hhmmss_values = digits[hhmmss_mask].str.zfill(6)
+                    parsed.loc[hhmmss_values.index] = pandas.to_datetime(
+                        hhmmss_values,
+                        format='%H%M%S',
+                        errors='coerce'
+                    )
+
+            remaining_mask = parsed.isna() & cleaned_series.notna()
+            if remaining_mask.any():
+                with warnings.catch_warnings():
+                    warnings.simplefilter('ignore', UserWarning)
+                    parsed.loc[remaining_mask] = pandas.to_datetime(
+                        cleaned_series[remaining_mask],
+                        errors='coerce'
+                    )
+
+        return parsed
+
+    def _normalize_datetime_component(self, component: str) -> str:
+        """Normalize datetime component aliases to a canonical value."""
+        normalized = (component or '').strip().lower()
+        aliases = {
+            'years': 'year',
+            'months': 'month',
+            'monthname': 'month_name',
+            'month_name': 'month_name',
+            'days': 'day',
+            'dayofmonth': 'day',
+            'dates': 'date',
+            'hours': 'hour',
+            'hour_of_day': 'hour',
+            'timeofday': 'hour',
+            'time_of_day': 'hour',
+            'minutes': 'minute',
+            'dayofweek': 'day_name',
+            'day_of_week': 'day_name',
+            'weekday': 'day_name',
+            'weekday_name': 'day_name',
+            'day_name': 'day_name',
+            'weekdaynumber': 'weekday_number',
+            'weekday_number': 'weekday_number',
+            'quarters': 'quarter',
+        }
+        return aliases.get(normalized, normalized)
+
+    def _extract_datetime_component(self, parsed_series: pandas.Series, component: str) -> pandas.Series:
+        """Extract a supported datetime component from a parsed datetime series."""
+        normalized = self._normalize_datetime_component(component)
+
+        if normalized == 'year':
+            return parsed_series.dt.year
+        if normalized == 'month':
+            return parsed_series.dt.month
+        if normalized == 'month_name':
+            month_names = parsed_series.dt.month_name()
+            ordered_months = pandas.Categorical(
+                month_names,
+                categories=self.MONTH_NAME_ORDER,
+                ordered=True
+            )
+            return pandas.Series(ordered_months, index=parsed_series.index)
+        if normalized == 'day':
+            return parsed_series.dt.day
+        if normalized == 'date':
+            return parsed_series.dt.strftime('%Y-%m-%d')
+        if normalized == 'hour':
+            return parsed_series.dt.hour
+        if normalized == 'minute':
+            return parsed_series.dt.minute
+        if normalized == 'day_name':
+            day_names = parsed_series.dt.day_name()
+            ordered_days = pandas.Categorical(
+                day_names,
+                categories=self.DAY_NAME_ORDER,
+                ordered=True
+            )
+            return pandas.Series(ordered_days, index=parsed_series.index)
+        if normalized == 'weekday_number':
+            return parsed_series.dt.dayofweek
+        if normalized == 'quarter':
+            return parsed_series.dt.quarter
+        if normalized == 'week':
+            return parsed_series.dt.isocalendar().week.astype(int)
+
+        raise ValueError(
+            f"Unsupported datetime component '{component}'. "
+            "Use year, month, month_name, day, date, hour, minute, day_name, weekday_number, quarter, or week."
+        )
+
+    def _parse_boolean_argument(self, value, default=True) -> bool:
+        """Parse common string boolean values for plugin inputs."""
+        if isinstance(value, bool):
+            return value
+        if value is None:
+            return default
+
+        normalized = str(value).strip().lower()
+        if normalized in {'true', '1', 'yes', 'y', 'on'}:
+            return True
+        if normalized in {'false', '0', 'no', 'n', 'off'}:
+            return False
+        return default
+
+    def _ordered_grouped_results(self, grouped: pandas.Series, component: str) -> pandas.Series:
+        """Return grouped results in a natural chronological order where possible."""
+        normalized = self._normalize_datetime_component(component)
+        if normalized == 'day_name':
+            return grouped.reindex([day for day in self.DAY_NAME_ORDER if day in grouped.index])
+        if normalized == 'month_name':
+            return grouped.reindex([month for month in self.MONTH_NAME_ORDER if month in grouped.index])
+        return grouped.sort_index()
+
+    def _series_to_json_dict(self, series: pandas.Series) -> dict:
+        """Convert a pandas Series into a JSON-safe dictionary."""
+        safe_dict = {}
+        for index, value in series.items():
+            safe_dict[str(index)] = value.item() if hasattr(value, 'item') else value
+        return safe_dict
+
+    def _scalar_to_json_value(self, value):
+        """Convert a scalar value to a JSON-safe representation."""
+        if pandas.isna(value):
+            return None
+        return value.item() if hasattr(value, 'item') else value
+
+    def _build_grouped_summary(self, grouped: pandas.Series) -> dict:
+        """Build generic summary fields for grouped metric outputs."""
+        if grouped.empty:
+            return {}
+
+        descending_values = grouped.sort_values(ascending=False)
+        ascending_values = grouped.sort_values(ascending=True)
+        summary = {
+            'highest_group': str(descending_values.index[0]),
+            'highest_value': self._scalar_to_json_value(descending_values.iloc[0]),
+            'lowest_group': str(ascending_values.index[0]),
+            'lowest_value': self._scalar_to_json_value(ascending_values.iloc[0]),
+            'average_group_value': self._scalar_to_json_value(grouped.mean()),
+            'median_group_value': self._scalar_to_json_value(grouped.median()),
+        }
+
+        if len(descending_values) > 1:
+            summary['second_highest_group'] = str(descending_values.index[1])
+            summary['second_highest_value'] = self._scalar_to_json_value(descending_values.iloc[1])
+
+        return summary
+
+    def _resolve_blob_location(self, user_id: str, conversation_id: str, filename: str, source: str,
+                               group_id: str = None, public_workspace_id: str = None) -> tuple:
+        """Resolve container name and blob path from source type."""
+        source = source.lower().strip()
+        if source == 'chat':
+            container = storage_account_personal_chat_container_name
+            blob_path = f"{user_id}/{conversation_id}/{filename}"
+        elif source == 'workspace':
+            container = storage_account_user_documents_container_name
+            blob_path = f"{user_id}/{filename}"
+        elif source == 'group':
+            if not group_id:
+                raise ValueError("group_id is required for source='group'")
+            container = storage_account_group_documents_container_name
+            blob_path = f"{group_id}/{filename}"
+        elif source == 'public':
+            if not public_workspace_id:
+                raise ValueError("public_workspace_id is required for source='public'")
+            container = storage_account_public_documents_container_name
+            blob_path = f"{public_workspace_id}/{filename}"
+        else:
+            raise ValueError(f"Unknown source '{source}'. Use 'workspace', 'chat', 'group', or 'public'.")
+        return container, blob_path
+
+    def _resolve_blob_location_with_fallback(self, user_id: str, conversation_id: str, filename: str, source: str,
+                                              group_id: str = None, public_workspace_id: str = None) -> tuple:
+        """Try primary source first, then fall back to other containers if blob not found."""
+        source = source.lower().strip()
+        attempts = []
+
+        # Primary attempt based on specified source
+        try:
+            primary = self._resolve_blob_location(user_id, conversation_id, filename, source, group_id, public_workspace_id)
+            attempts.append(primary)
+        except ValueError:
+            pass
+
+        # Fallback attempts in priority order (skip the primary source)
+        if source != 'workspace':
+            attempts.append((storage_account_user_documents_container_name, f"{user_id}/{filename}"))
+        if source != 'group' and group_id:
+            attempts.append((storage_account_group_documents_container_name, f"{group_id}/{filename}"))
+        if source != 'public' and public_workspace_id:
+            attempts.append((storage_account_public_documents_container_name, f"{public_workspace_id}/{filename}"))
+        if source != 'chat':
+            attempts.append((storage_account_personal_chat_container_name, f"{user_id}/{conversation_id}/{filename}"))
+
+        client = self._get_blob_service_client()
+        for container, blob_path in attempts:
+            try:
+                blob_client = client.get_blob_client(container=container, blob=blob_path)
+                if blob_client.exists():
+                    log_event(f"[TabularProcessingPlugin] Found blob at {container}/{blob_path}", level=logging.DEBUG)
+                    return container, blob_path
+            except Exception:
+                continue
+
+        # If nothing found, return primary for the original error message
+        if attempts:
+            return attempts[0]
+        raise ValueError(f"Could not resolve blob location for {filename}")
+
+    @kernel_function(
+        description=(
+            "List all tabular data files available for a user. Checks workspace documents "
+            "(user-documents container), chat-uploaded documents (personal-chat container), "
+            "and optionally group or public workspace documents. "
+            "Returns a JSON list of available files with their source."
+        ),
+        name="list_tabular_files"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def list_tabular_files(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON list of available tabular files"]:
+        """List all tabular files available for the user across all accessible containers."""
+        def _sync_work():
+            results = []
+            try:
+                workspace_prefix = f"{user_id}/"
+                workspace_blobs = self._list_tabular_blobs(
+                    storage_account_user_documents_container_name, workspace_prefix
+                )
+                for blob in workspace_blobs:
+                    filename = blob.split('/')[-1]
+                    workbook_metadata = self._get_workbook_metadata(
+                        storage_account_user_documents_container_name,
+                        blob,
+                    )
+                    results.append({
+                        "filename": filename,
+                        "blob_path": blob,
+                        "source": "workspace",
+                        "container": storage_account_user_documents_container_name,
+                        "sheet_names": workbook_metadata.get('sheet_names', []),
+                        "sheet_count": workbook_metadata.get('sheet_count', 0),
+                    })
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error listing workspace blobs: {e}", level=logging.WARNING)
+
+            try:
+                chat_prefix = f"{user_id}/{conversation_id}/"
+                chat_blobs = self._list_tabular_blobs(
+                    storage_account_personal_chat_container_name, chat_prefix
+                )
+                for blob in chat_blobs:
+                    filename = blob.split('/')[-1]
+                    workbook_metadata = self._get_workbook_metadata(
+                        storage_account_personal_chat_container_name,
+                        blob,
+                    )
+                    results.append({
+                        "filename": filename,
+                        "blob_path": blob,
+                        "source": "chat",
+                        "container": storage_account_personal_chat_container_name,
+                        "sheet_names": workbook_metadata.get('sheet_names', []),
+                        "sheet_count": workbook_metadata.get('sheet_count', 0),
+                    })
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error listing chat blobs: {e}", level=logging.WARNING)
+
+            if group_id:
+                try:
+                    group_prefix = f"{group_id}/"
+                    group_blobs = self._list_tabular_blobs(
+                        storage_account_group_documents_container_name, group_prefix
+                    )
+                    for blob in group_blobs:
+                        filename = blob.split('/')[-1]
+                        workbook_metadata = self._get_workbook_metadata(
+                            storage_account_group_documents_container_name,
+                            blob,
+                        )
+                        results.append({
+                            "filename": filename,
+                            "blob_path": blob,
+                            "source": "group",
+                            "container": storage_account_group_documents_container_name,
+                            "sheet_names": workbook_metadata.get('sheet_names', []),
+                            "sheet_count": workbook_metadata.get('sheet_count', 0),
+                        })
+                except Exception as e:
+                    log_event(f"[TabularProcessingPlugin] Error listing group blobs: {e}", level=logging.WARNING)
+
+            if public_workspace_id:
+                try:
+                    public_prefix = f"{public_workspace_id}/"
+                    public_blobs = self._list_tabular_blobs(
+                        storage_account_public_documents_container_name, public_prefix
+                    )
+                    for blob in public_blobs:
+                        filename = blob.split('/')[-1]
+                        workbook_metadata = self._get_workbook_metadata(
+                            storage_account_public_documents_container_name,
+                            blob,
+                        )
+                        results.append({
+                            "filename": filename,
+                            "blob_path": blob,
+                            "source": "public",
+                            "container": storage_account_public_documents_container_name,
+                            "sheet_names": workbook_metadata.get('sheet_names', []),
+                            "sheet_count": workbook_metadata.get('sheet_count', 0),
+                        })
+                except Exception as e:
+                    log_event(f"[TabularProcessingPlugin] Error listing public blobs: {e}", level=logging.WARNING)
+
+            return json.dumps(results, indent=2)
+        return await asyncio.to_thread(_sync_work)
+
+    @kernel_function(
+        description=(
+            "Get a summary of a tabular file including column names, row count, data types, "
+            "and a preview of the first few rows."
+        ),
+        name="describe_tabular_file"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def describe_tabular_file(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        filename: Annotated[str, "The filename of the tabular file"],
+        sheet_name: Annotated[Optional[str], "Optional worksheet name for Excel files. When omitted on multi-sheet workbooks, the response returns workbook-level sheet schemas."] = None,
+        sheet_index: Annotated[Optional[str], "Optional zero-based worksheet index for Excel files. Ignored when sheet_name is provided."] = None,
+        source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat",
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON summary of the tabular file"]:
+        """Get schema and preview of a tabular file."""
+        def _sync_work():
+            try:
+                container, blob_path = self._resolve_blob_location_with_fallback(
+                    user_id, conversation_id, filename, source,
+                    group_id=group_id, public_workspace_id=public_workspace_id
+                )
+                workbook_metadata = self._get_workbook_metadata(container, blob_path)
+
+                if workbook_metadata.get('is_workbook') and workbook_metadata.get('sheet_count', 0) > 1 and not (sheet_name or sheet_index):
+                    summary = self._build_workbook_schema_summary(
+                        container,
+                        blob_path,
+                        filename,
+                        preview_rows=3,
+                    )
+                else:
+                    selected_sheet, workbook_metadata = self._resolve_sheet_selection(
+                        container,
+                        blob_path,
+                        sheet_name=sheet_name,
+                        sheet_index=sheet_index,
+                        require_explicit_sheet=False,
+                    )
+                    df = self._read_tabular_blob_to_dataframe(
+                        container,
+                        blob_path,
+                        sheet_name=selected_sheet,
+                        require_explicit_sheet=False,
+                    )
+                    summary = self._build_sheet_schema_summary(df, selected_sheet, preview_rows=5)
+                    summary.update({
+                        "filename": filename,
+                        "is_workbook": workbook_metadata.get('is_workbook', False),
+                        "sheet_names": workbook_metadata.get('sheet_names', []),
+                        "sheet_count": workbook_metadata.get('sheet_count', 0),
+                    })
+
+                return json.dumps(summary, indent=2, default=str)
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error describing file: {e}", level=logging.WARNING)
+                return json.dumps({"error": str(e)})
+        return await asyncio.to_thread(_sync_work)
+
+    @kernel_function(
+        description=(
+            "Look up one or more rows by label/category in a tabular file and return the value from a target column. "
+            "Best for questions like 'What was Total Assets in Nov-25?' or 'What was Net Worth in Dec-25?'."
+        ),
+        name="lookup_value"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def lookup_value(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        filename: Annotated[str, "The filename of the tabular file"],
+        lookup_column: Annotated[str, "The label/category column to search, such as Accounts or Category"],
+        lookup_value: Annotated[str, "The row label/category value to search for, such as Total Assets"],
+        target_column: Annotated[str, "The target column containing the desired value, such as Nov-25"],
+        match_operator: Annotated[str, "Match operator: equals, contains, startswith, endswith"] = "equals",
+        sheet_name: Annotated[Optional[str], "Optional worksheet name for Excel files. Required for analytical calls on multi-sheet workbooks unless sheet_index is provided."] = None,
+        sheet_index: Annotated[Optional[str], "Optional zero-based worksheet index for Excel files. Ignored when sheet_name is provided."] = None,
+        source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat",
+        max_rows: Annotated[str, "Maximum matching rows to return"] = "25",
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON result containing matching rows and target-column values"]:
+        """Look up values from a target column for matching rows."""
+        def _sync_work():
+            try:
+                container, blob_path = self._resolve_blob_location_with_fallback(
+                    user_id, conversation_id, filename, source,
+                    group_id=group_id, public_workspace_id=public_workspace_id
+                )
+                selected_sheet, workbook_metadata = self._resolve_sheet_selection(
+                    container,
+                    blob_path,
+                    sheet_name=sheet_name,
+                    sheet_index=sheet_index,
+                    require_explicit_sheet=True,
+                )
+                df = self._read_tabular_blob_to_dataframe(
+                    container,
+                    blob_path,
+                    sheet_name=selected_sheet,
+                    require_explicit_sheet=True,
+                )
+                df = self._try_numeric_conversion(df)
+
+                if lookup_column not in df.columns:
+                    return json.dumps({"error": f"Column '{lookup_column}' not found. Available: {list(df.columns)}"})
+                if target_column not in df.columns:
+                    return json.dumps({"error": f"Column '{target_column}' not found. Available: {list(df.columns)}"})
+
+                series = df[lookup_column]
+                operator = (match_operator or 'equals').strip().lower()
+                normalized_lookup_value = str(lookup_value)
+
+                if operator in {'equals', '=='}:
+                    mask = series.astype(str).str.lower() == normalized_lookup_value.lower()
+                elif operator == 'contains':
+                    mask = series.astype(str).str.contains(normalized_lookup_value, case=False, na=False)
+                elif operator == 'startswith':
+                    mask = series.astype(str).str.lower().str.startswith(normalized_lookup_value.lower())
+                elif operator == 'endswith':
+                    mask = series.astype(str).str.lower().str.endswith(normalized_lookup_value.lower())
+                else:
+                    return json.dumps({"error": f"Unsupported match_operator: {match_operator}"})
+
+                limit = int(max_rows)
+                matches = df[mask].head(limit)
+                response = {
+                    "filename": filename,
+                    "selected_sheet": selected_sheet if workbook_metadata.get('is_workbook') else None,
+                    "lookup_column": lookup_column,
+                    "lookup_value": lookup_value,
+                    "target_column": target_column,
+                    "match_operator": operator,
+                    "total_matches": int(mask.sum()),
+                    "returned_rows": len(matches),
+                    "data": matches.to_dict(orient='records'),
+                }
+
+                if len(matches) == 1:
+                    response["value"] = matches.iloc[0][target_column]
+
+                return json.dumps(response, indent=2, default=str)
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error looking up value: {e}", level=logging.WARNING)
+                return json.dumps({"error": str(e)})
+        return await asyncio.to_thread(_sync_work)
+
+    @kernel_function(
+        description=(
+            "Execute an aggregation operation on a column of a tabular file. "
+            "Supported operations: sum, mean, count, min, max, median, std, nunique, value_counts."
+        ),
+        name="aggregate_column"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def aggregate_column(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        filename: Annotated[str, "The filename of the tabular file"],
+        column: Annotated[str, "The column name to aggregate"],
+        operation: Annotated[str, "Aggregation: sum, mean, count, min, max, median, std, nunique, value_counts"],
+        sheet_name: Annotated[Optional[str], "Optional worksheet name for Excel files. Required for analytical calls on multi-sheet workbooks unless sheet_index is provided."] = None,
+        sheet_index: Annotated[Optional[str], "Optional zero-based worksheet index for Excel files. Ignored when sheet_name is provided."] = None,
+        source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat",
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON result of the aggregation"]:
+        """Execute an aggregation operation on a column."""
+        def _sync_work():
+            try:
+                container, blob_path = self._resolve_blob_location_with_fallback(
+                    user_id, conversation_id, filename, source,
+                    group_id=group_id, public_workspace_id=public_workspace_id
+                )
+                selected_sheet, workbook_metadata = self._resolve_sheet_selection(
+                    container,
+                    blob_path,
+                    sheet_name=sheet_name,
+                    sheet_index=sheet_index,
+                    require_explicit_sheet=True,
+                )
+                df = self._read_tabular_blob_to_dataframe(
+                    container,
+                    blob_path,
+                    sheet_name=selected_sheet,
+                    require_explicit_sheet=True,
+                )
+                df = self._try_numeric_conversion(df)
+
+                if column not in df.columns:
+                    return json.dumps({"error": f"Column '{column}' not found. Available: {list(df.columns)}"})
+
+                series = df[column]
+                op = operation.lower().strip()
+
+                if op == 'sum':
+                    result = series.sum()
+                elif op == 'mean':
+                    result = series.mean()
+                elif op == 'count':
+                    result = series.count()
+                elif op == 'min':
+                    result = series.min()
+                elif op == 'max':
+                    result = series.max()
+                elif op == 'median':
+                    result = series.median()
+                elif op == 'std':
+                    result = series.std()
+                elif op == 'nunique':
+                    result = series.nunique()
+                elif op == 'value_counts':
+                    result = self._series_to_json_dict(series.value_counts())
+                else:
+                    return json.dumps({"error": f"Unsupported operation: {operation}. Use sum, mean, count, min, max, median, std, nunique, value_counts."})
+
+                return json.dumps({
+                    "filename": filename,
+                    "selected_sheet": selected_sheet if workbook_metadata.get('is_workbook') else None,
+                    "column": column,
+                    "operation": op,
+                    "result": result,
+                }, indent=2, default=str)
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error aggregating column: {e}", level=logging.WARNING)
+                return json.dumps({"error": str(e)})
+        return await asyncio.to_thread(_sync_work)
+
+    @kernel_function(
+        description=(
+            "Filter rows in a tabular file based on conditions and return matching rows. "
+            "Supports operators: ==, !=, >, <, >=, <=, contains, startswith, endswith."
+        ),
+        name="filter_rows"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def filter_rows(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        filename: Annotated[str, "The filename of the tabular file"],
+        column: Annotated[str, "The column to filter on"],
+        operator: Annotated[str, "Operator: ==, !=, >, <, >=, <=, contains, startswith, endswith"],
+        value: Annotated[str, "The value to compare against"],
+        sheet_name: Annotated[Optional[str], "Optional worksheet name for Excel files. Required for analytical calls on multi-sheet workbooks unless sheet_index is provided."] = None,
+        sheet_index: Annotated[Optional[str], "Optional zero-based worksheet index for Excel files. Ignored when sheet_name is provided."] = None,
+        source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat",
+        max_rows: Annotated[str, "Maximum rows to return"] = "100",
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON list of matching rows"]:
+        """Filter rows based on a condition."""
+        def _sync_work():
+            try:
+                container, blob_path = self._resolve_blob_location_with_fallback(
+                    user_id, conversation_id, filename, source,
+                    group_id=group_id, public_workspace_id=public_workspace_id
+                )
+                selected_sheet, workbook_metadata = self._resolve_sheet_selection(
+                    container,
+                    blob_path,
+                    sheet_name=sheet_name,
+                    sheet_index=sheet_index,
+                    require_explicit_sheet=True,
+                )
+                df = self._read_tabular_blob_to_dataframe(
+                    container,
+                    blob_path,
+                    sheet_name=selected_sheet,
+                    require_explicit_sheet=True,
+                )
+                df = self._try_numeric_conversion(df)
+
+                if column not in df.columns:
+                    return json.dumps({"error": f"Column '{column}' not found. Available: {list(df.columns)}"})
+
+                series = df[column]
+                op = operator.strip().lower()
+
+                numeric_value = None
+                try:
+                    numeric_value = float(value)
+                except (ValueError, TypeError):
+                    pass
+
+                if op == '==' or op == 'equals':
+                    if numeric_value is not None and pandas.api.types.is_numeric_dtype(series):
+                        mask = series == numeric_value
+                    else:
+                        mask = series.astype(str).str.lower() == value.lower()
+                elif op == '!=':
+                    if numeric_value is not None and pandas.api.types.is_numeric_dtype(series):
+                        mask = series != numeric_value
+                    else:
+                        mask = series.astype(str).str.lower() != value.lower()
+                elif op == '>':
+                    mask = series > numeric_value
+                elif op == '<':
+                    mask = series < numeric_value
+                elif op == '>=':
+                    mask = series >= numeric_value
+                elif op == '<=':
+                    mask = series <= numeric_value
+                elif op == 'contains':
+                    mask = series.astype(str).str.contains(value, case=False, na=False)
+                elif op == 'startswith':
+                    mask = series.astype(str).str.lower().str.startswith(value.lower())
+                elif op == 'endswith':
+                    mask = series.astype(str).str.lower().str.endswith(value.lower())
+                else:
+                    return json.dumps({"error": f"Unsupported operator: {operator}"})
+
+                limit = int(max_rows)
+                filtered = df[mask].head(limit)
+                return json.dumps({
+                    "filename": filename,
+                    "selected_sheet": selected_sheet if workbook_metadata.get('is_workbook') else None,
+                    "total_matches": int(mask.sum()),
+                    "returned_rows": len(filtered),
+                    "data": filtered.to_dict(orient='records')
+                }, indent=2, default=str)
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error filtering rows: {e}", level=logging.WARNING)
+                return json.dumps({"error": str(e)})
+        return await asyncio.to_thread(_sync_work)
+
+    @kernel_function(
+        description=(
+            "Execute a pandas query expression against a tabular file for advanced analysis. "
+            "The query string uses pandas DataFrame.query() syntax. "
+            "Examples: 'Age > 30 and State == \"CA\"', 'Price < 100'"
+        ),
+        name="query_tabular_data"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def query_tabular_data(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        filename: Annotated[str, "The filename of the tabular file"],
+        query_expression: Annotated[str, "Pandas query expression (e.g. 'Age > 30 and State == \"CA\"')"],
+        sheet_name: Annotated[Optional[str], "Optional worksheet name for Excel files. Required for analytical calls on multi-sheet workbooks unless sheet_index is provided."] = None,
+        sheet_index: Annotated[Optional[str], "Optional zero-based worksheet index for Excel files. Ignored when sheet_name is provided."] = None,
+        source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat",
+        max_rows: Annotated[str, "Maximum rows to return"] = "100",
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON result of the query"]:
+        """Execute a pandas query expression against a tabular file."""
+        def _sync_work():
+            try:
+                container, blob_path = self._resolve_blob_location_with_fallback(
+                    user_id, conversation_id, filename, source,
+                    group_id=group_id, public_workspace_id=public_workspace_id
+                )
+                selected_sheet, workbook_metadata = self._resolve_sheet_selection(
+                    container,
+                    blob_path,
+                    sheet_name=sheet_name,
+                    sheet_index=sheet_index,
+                    require_explicit_sheet=True,
+                )
+                df = self._read_tabular_blob_to_dataframe(
+                    container,
+                    blob_path,
+                    sheet_name=selected_sheet,
+                    require_explicit_sheet=True,
+                )
+                df = self._try_numeric_conversion(df)
+
+                result_df = df.query(query_expression)
+                limit = int(max_rows)
+                return json.dumps({
+                    "filename": filename,
+                    "selected_sheet": selected_sheet if workbook_metadata.get('is_workbook') else None,
+                    "total_matches": len(result_df),
+                    "returned_rows": min(len(result_df), limit),
+                    "data": result_df.head(limit).to_dict(orient='records')
+                }, indent=2, default=str)
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error querying data: {e}", level=logging.WARNING)
+                return json.dumps({"error": f"Query error: {str(e)}. Ensure column names and values are correct."})
+        return await asyncio.to_thread(_sync_work)
+
+    @kernel_function(
+        description=(
+            "Perform a group-by aggregation on a tabular file. "
+            "Groups data by one column and aggregates another column. "
+            "Supported operations: sum, mean, count, min, max, median, std. "
+            "Returns top grouped results plus highest and lowest group summary fields."
+        ),
+        name="group_by_aggregate"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def group_by_aggregate(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        filename: Annotated[str, "The filename of the tabular file"],
+        group_by_column: Annotated[str, "The column to group by"],
+        aggregate_column: Annotated[str, "The column to aggregate"],
+        operation: Annotated[str, "Aggregation operation: sum, mean, count, min, max, median, std"],
+        sheet_name: Annotated[Optional[str], "Optional worksheet name for Excel files. Required for analytical calls on multi-sheet workbooks unless sheet_index is provided."] = None,
+        sheet_index: Annotated[Optional[str], "Optional zero-based worksheet index for Excel files. Ignored when sheet_name is provided."] = None,
+        source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat",
+        top_n: Annotated[str, "How many top groups to return in descending or ascending order"] = "10",
+        sort_descending: Annotated[str, "Whether top_results should be sorted descending (true/false)"] = "true",
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON result of the group-by aggregation"]:
+        """Group by one column and aggregate another."""
+        def _sync_work():
+            try:
+                container, blob_path = self._resolve_blob_location_with_fallback(
+                    user_id, conversation_id, filename, source,
+                    group_id=group_id, public_workspace_id=public_workspace_id
+                )
+                selected_sheet, workbook_metadata = self._resolve_sheet_selection(
+                    container,
+                    blob_path,
+                    sheet_name=sheet_name,
+                    sheet_index=sheet_index,
+                    require_explicit_sheet=True,
+                )
+                df = self._read_tabular_blob_to_dataframe(
+                    container,
+                    blob_path,
+                    sheet_name=selected_sheet,
+                    require_explicit_sheet=True,
+                )
+                df = self._try_numeric_conversion(df)
+
+                for col in [group_by_column, aggregate_column]:
+                    if col not in df.columns:
+                        return json.dumps({"error": f"Column '{col}' not found. Available: {list(df.columns)}"})
+
+                op = operation.lower().strip()
+                if op not in {'count', 'sum', 'mean', 'min', 'max', 'median', 'std'}:
+                    return json.dumps({
+                        "error": "Unsupported operation. Use count, sum, mean, min, max, median, or std."
+                    })
+
+                grouped = df.groupby(group_by_column)[aggregate_column].agg(op)
+                grouped = grouped.dropna()
+                if grouped.empty:
+                    return json.dumps({"error": "No grouped results were produced."})
+
+                top_limit = max(1, int(top_n))
+                descending = self._parse_boolean_argument(sort_descending, default=True)
+                top_results = grouped.sort_values(ascending=not descending).head(top_limit)
+                ordered_results = grouped.sort_index()
+                summary = self._build_grouped_summary(grouped)
+
+                return json.dumps({
+                    "filename": filename,
+                    "selected_sheet": selected_sheet if workbook_metadata.get('is_workbook') else None,
+                    "group_by": group_by_column,
+                    "aggregate_column": aggregate_column,
+                    "operation": op,
+                    "groups": len(grouped),
+                    "top_results": self._series_to_json_dict(top_results),
+                    "result": self._series_to_json_dict(ordered_results),
+                    **summary,
+                }, indent=2, default=str)
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error in group-by: {e}", level=logging.WARNING)
+                return json.dumps({"error": str(e)})
+        return await asyncio.to_thread(_sync_work)
+
+    @kernel_function(
+        description=(
+            "Group a tabular file by a component extracted from a datetime-like column and aggregate a metric. "
+            "Use this for time-based questions such as peak hours, busiest weekdays, or monthly trends. "
+            "Supported datetime components: year, month, month_name, day, date, hour, minute, day_name, "
+            "weekday_number, quarter, week. Supported operations: count, sum, mean, min, max, median, std. "
+            "An optional pandas query filter can be applied before grouping. Returns top grouped results plus highest and lowest summary fields."
+        ),
+        name="group_by_datetime_component"
+    )
+    @plugin_function_logger("TabularProcessingPlugin")
+    async def group_by_datetime_component(
+        self,
+        user_id: Annotated[str, "The user ID (from Scope ID in Conversation Metadata)"],
+        conversation_id: Annotated[str, "The conversation ID (from Conversation Metadata)"],
+        filename: Annotated[str, "The filename of the tabular file"],
+        datetime_column: Annotated[str, "The datetime-like column to extract a component from"],
+        datetime_component: Annotated[str, "Component: year, month, month_name, day, date, hour, minute, day_name, weekday_number, quarter, or week"],
+        aggregate_column: Annotated[Optional[str], "The numeric column to aggregate. Leave empty and use operation='count' to count rows."] = "",
+        operation: Annotated[str, "Aggregation operation: count, sum, mean, min, max, median, std"] = "count",
+        sheet_name: Annotated[Optional[str], "Optional worksheet name for Excel files. Required for analytical calls on multi-sheet workbooks unless sheet_index is provided."] = None,
+        sheet_index: Annotated[Optional[str], "Optional zero-based worksheet index for Excel files. Ignored when sheet_name is provided."] = None,
+        source: Annotated[str, "Source: 'workspace', 'chat', 'group', or 'public'"] = "chat",
+        filter_expression: Annotated[Optional[str], "Optional pandas query filter applied before grouping"] = "",
+        top_n: Annotated[str, "How many top groups to return in descending order"] = "10",
+        sort_descending: Annotated[str, "Whether top_results should be sorted descending (true/false)"] = "true",
+        group_id: Annotated[Optional[str], "Group ID (for group workspace documents)"] = None,
+        public_workspace_id: Annotated[Optional[str], "Public workspace ID (for public workspace documents)"] = None,
+    ) -> Annotated[str, "JSON result of the datetime component grouping analysis"]:
+        """Group data by a datetime component and aggregate a metric."""
+        def _sync_work():
+            try:
+                container, blob_path = self._resolve_blob_location_with_fallback(
+                    user_id,
+                    conversation_id,
+                    filename,
+                    source,
+                    group_id=group_id,
+                    public_workspace_id=public_workspace_id
+                )
+                selected_sheet, workbook_metadata = self._resolve_sheet_selection(
+                    container,
+                    blob_path,
+                    sheet_name=sheet_name,
+                    sheet_index=sheet_index,
+                    require_explicit_sheet=True,
+                )
+                df = self._read_tabular_blob_to_dataframe(
+                    container,
+                    blob_path,
+                    sheet_name=selected_sheet,
+                    require_explicit_sheet=True,
+                )
+                df = self._try_numeric_conversion(df)
+
+                if filter_expression:
+                    try:
+                        df = df.query(filter_expression)
+                    except Exception as query_error:
+                        return json.dumps({
+                            "error": f"Filter query error: {query_error}. Ensure column names and values are correct."
+                        })
+
+                if datetime_column not in df.columns:
+                    return json.dumps({
+                        "error": f"Column '{datetime_column}' not found. Available: {list(df.columns)}"
+                    })
+
+                parsed_datetime = self._parse_datetime_like_series(df[datetime_column])
+                valid_mask = parsed_datetime.notna()
+                if not valid_mask.any():
+                    return json.dumps({
+                        "error": (
+                            f"Could not parse any datetime values from column '{datetime_column}'. "
+                            "Try a different datetime column or inspect the file schema preview."
+                        )
+                    })
+
+                filtered_df = df.loc[valid_mask].copy()
+                parsed_datetime = parsed_datetime.loc[valid_mask]
+                component_values = self._extract_datetime_component(parsed_datetime, datetime_component)
+
+                component_column_name = f"__datetime_component_{self._normalize_datetime_component(datetime_component)}"
+                filtered_df[component_column_name] = component_values
+
+                op = (operation or 'count').strip().lower()
+                if op not in {'count', 'sum', 'mean', 'min', 'max', 'median', 'std'}:
+                    return json.dumps({
+                        "error": "Unsupported operation. Use count, sum, mean, min, max, median, or std."
+                    })
+
+                aggregate_column_name = (aggregate_column or '').strip()
+                if op == 'count' and not aggregate_column_name:
+                    grouped = filtered_df.groupby(component_column_name).size()
+                else:
+                    if not aggregate_column_name:
+                        return json.dumps({
+                            "error": "aggregate_column is required unless operation='count'."
+                        })
+                    if aggregate_column_name not in filtered_df.columns:
+                        return json.dumps({
+                            "error": f"Column '{aggregate_column_name}' not found. Available: {list(filtered_df.columns)}"
+                        })
+                    grouped = filtered_df.groupby(component_column_name)[aggregate_column_name].agg(op)
+
+                grouped = grouped.dropna()
+                if grouped.empty:
+                    return json.dumps({
+                        "error": "No grouped results were produced after filtering and datetime parsing."
+                    })
+
+                top_limit = max(1, int(top_n))
+                descending = self._parse_boolean_argument(sort_descending, default=True)
+                top_results = grouped.sort_values(ascending=not descending).head(top_limit)
+                ordered_results = self._ordered_grouped_results(grouped, datetime_component)
+                summary = self._build_grouped_summary(grouped)
+
+                return json.dumps({
+                    "filename": filename,
+                    "selected_sheet": selected_sheet if workbook_metadata.get('is_workbook') else None,
+                    "datetime_column": datetime_column,
+                    "datetime_component": self._normalize_datetime_component(datetime_component),
+                    "aggregate_column": aggregate_column_name or None,
+                    "operation": op,
+                    "filter_expression": filter_expression or None,
+                    "parsed_rows": int(valid_mask.sum()),
+                    "dropped_rows": int((~valid_mask).sum()),
+                    "groups": int(len(grouped)),
+                    "top_results": self._series_to_json_dict(top_results),
+                    "result": self._series_to_json_dict(ordered_results),
+                    **summary,
+                }, indent=2, default=str)
+            except Exception as e:
+                log_event(f"[TabularProcessingPlugin] Error in datetime component grouping: {e}", level=logging.WARNING)
+                return json.dumps({"error": str(e)})
+        return await asyncio.to_thread(_sync_work)
diff --git a/application/single_app/static/css/chats.css b/application/single_app/static/css/chats.css
index f672b28f..7a6232a2 100644
--- a/application/single_app/static/css/chats.css
+++ b/application/single_app/static/css/chats.css
@@ -1676,4 +1676,160 @@ mark.search-highlight {
     100% {
         transform: scale(1.05);
     }
+}
+
+/* =============================================
+   Processing Thoughts
+   ============================================= */
+
+/* Loading indicator thought text */
+.thought-live-text {
+    font-style: italic;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    max-width: 300px;
+}
+
+/* Toggle button in message footer */
+.thoughts-toggle-btn {
+    font-size: 0.9rem;
+    color: #6c757d;
+    padding: 0 0.25rem;
+    border: none;
+    background: none;
+    cursor: pointer;
+    transition: color 0.15s ease-in-out;
+}
+
+.thoughts-toggle-btn:hover {
+    color: #ffc107;
+}
+
+/* Collapsible container inside message bubble */
+.thoughts-container {
+    max-height: 300px;
+    overflow-y: auto;
+    font-size: 0.85rem;
+}
+
+/* Timeline wrapper */
+.thoughts-list {
+    position: relative;
+    padding-left: 1.25rem;
+}
+
+/* Vertical timeline line */
+.thoughts-list::before {
+    content: '';
+    position: absolute;
+    left: 0.5rem;
+    top: 0.25rem;
+    bottom: 0.25rem;
+    width: 2px;
+    background: linear-gradient(to bottom, #0d6efd, #6ea8fe);
+    border-radius: 1px;
+}
+
+/* Individual thought step */
+.thought-step {
+    display: flex;
+    align-items: flex-start;
+    padding-left: 0.75rem;
+    padding-top: 0.25rem;
+    padding-bottom: 0.25rem;
+    position: relative;
+}
+
+/* Timeline node dot */
+.thought-step::before {
+    content: '';
+    position: absolute;
+    left: -1rem;
+    top: 0.55rem;
+    width: 8px;
+    height: 8px;
+    border-radius: 50%;
+    background-color: #0d6efd;
+    border: 2px solid #fff;
+    box-shadow: 0 0 0 1px #0d6efd;
+    z-index: 1;
+}
+
+/* Last thought step gets a slightly different dot */
+.thought-step:last-child::before {
+    background-color: #198754;
+    box-shadow: 0 0 0 1px #198754;
+}
+
+.thought-step i {
+    flex-shrink: 0;
+    margin-top: 2px;
+}
+
+/* Streaming cursor thought badge pulse animation */
+.animate-pulse {
+    animation: thought-pulse 1.5s ease-in-out infinite;
+}
+
+/* Streaming thought display (before content arrives) */
+.streaming-thought-display {
+    display: flex;
+    align-items: center;
+    padding: 0.5rem 0;
+}
+
+/* Light mode: use darker, more readable colors */
+.streaming-thought-display .badge {
+    background-color: rgba(13, 110, 253, 0.08) !important;
+    color: #0a58ca !important;
+    border-color: rgba(13, 110, 253, 0.25) !important;
+}
+
+/* Dark mode: lighter accent colors */
+[data-bs-theme="dark"] .streaming-thought-display .badge {
+    background-color: rgba(13, 202, 240, 0.15) !important;
+    color: #6edff6 !important;
+    border-color: rgba(13, 202, 240, 0.3) !important;
+}
+
+@keyframes thought-pulse {
+    0%, 100% {
+        opacity: 1;
+    }
+    50% {
+        opacity: 0.6;
+    }
+}
+
+/* Dark mode overrides */
+[data-bs-theme="dark"] .thoughts-toggle-btn {
+    color: #adb5bd;
+}
+
+[data-bs-theme="dark"] .thoughts-toggle-btn:hover {
+    color: #ffc107;
+}
+
+[data-bs-theme="dark"] .thought-step {
+    /* Dark mode dot border matches dark background */
+}
+
+[data-bs-theme="dark"] .thought-step::before {
+    border-color: #212529;
+    background-color: #6ea8fe;
+    box-shadow: 0 0 0 1px #6ea8fe;
+}
+
+[data-bs-theme="dark"] .thought-step:last-child::before {
+    background-color: #75b798;
+    box-shadow: 0 0 0 1px #75b798;
+}
+
+[data-bs-theme="dark"] .thoughts-list::before {
+    background: linear-gradient(to bottom, #6ea8fe, #9ec5fe);
+}
+
+[data-bs-theme="dark"] .thoughts-container {
+    border-top-color: #495057 !important;
 }
\ No newline at end of file
diff --git a/application/single_app/static/css/styles.css b/application/single_app/static/css/styles.css
index c0632a99..eacc8859 100644
--- a/application/single_app/static/css/styles.css
+++ b/application/single_app/static/css/styles.css
@@ -502,6 +502,95 @@ main {
     flex-grow: 1;
 }
 
+/* ============================================
+   Item cards (agents/actions grid view)
+   ============================================ */
+.item-card {
+    cursor: default;
+    transition: all 0.3s ease;
+    border: 1px solid #dee2e6;
+    border-radius: 0.375rem;
+    background-color: #ffffff;
+}
+
+.item-card:hover {
+    border-color: #adb5bd;
+    transform: translateY(-2px);
+    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+
+.item-card .card-title {
+    font-weight: 600;
+    font-size: 0.9rem;
+    color: #212529;
+}
+
+.item-card .card-text {
+    color: #6c757d;
+    font-size: 0.8rem;
+    line-height: 1.4;
+}
+
+.item-card .item-card-icon {
+    color: #0d6efd;
+}
+
+.item-card .item-card-buttons {
+    border-top: 1px solid #f0f0f0;
+    padding-top: 0.5rem;
+}
+
+/* Dark mode for item cards */
+[data-bs-theme="dark"] .item-card {
+    background-color: #343a40;
+    border: 1px solid #495057;
+    color: #e9ecef;
+}
+
+[data-bs-theme="dark"] .item-card:hover {
+    background-color: #3d444b;
+    border-color: #6c757d;
+}
+
+[data-bs-theme="dark"] .item-card .card-title {
+    color: #e9ecef;
+}
+
+[data-bs-theme="dark"] .item-card .card-text {
+    color: #adb5bd;
+}
+
+[data-bs-theme="dark"] .item-card .item-card-icon {
+    color: #6ea8fe;
+}
+
+[data-bs-theme="dark"] .item-card .item-card-buttons {
+    border-top-color: #495057;
+}
+
+/* Improved table column layout for agents and actions */
+.item-list-table th:nth-child(1),
+.item-list-table td:nth-child(1) {
+    width: 28%;
+    min-width: 140px;
+}
+
+.item-list-table th:nth-child(2),
+.item-list-table td:nth-child(2) {
+    width: 47%;
+    max-width: 0;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+}
+
+.item-list-table th:nth-child(3),
+.item-list-table td:nth-child(3) {
+    width: 25%;
+    min-width: 160px;
+    white-space: nowrap;
+}
+
 /* Connection type buttons */
 .connection-type-btn {
     border: 2px solid #dee2e6;
@@ -855,144 +944,170 @@ main {
     color: #b399ff !important; /* Purple-ish for visited links */
 }
 
-/* =============================================
-   SimpleMDE Prompt Content Toolbar - Bootstrap Icons replacement for Font Awesome
-   ============================================= */
-.editor-toolbar a[title] {
-    font-family: "bootstrap-icons" !important;
-    font-style: normal;
-    font-size: 1rem;
-    line-height: 30px;
-}
-
-.editor-toolbar a.fa-bold::before          { content: "\f5f0"; } /* bi-type-bold */
-.editor-toolbar a.fa-italic::before        { content: "\f5f4"; } /* bi-type-italic */
-.editor-toolbar a.fa-strikethrough::before { content: "\f5f5"; } /* bi-type-strikethrough */
-.editor-toolbar a.fa-header::before        { content: "\f5f1"; } /* bi-type-h1 */
-.editor-toolbar a.fa-quote-left::before    { content: "\f190"; } /* bi-blockquote-left */
-.editor-toolbar a.fa-list-ul::before       { content: "\f478"; } /* bi-list-ul */
-.editor-toolbar a.fa-list-ol::before       { content: "\f475"; } /* bi-list-ol */
-.editor-toolbar a.fa-code::before          { content: "\f2c6"; } /* bi-code-slash */
-.editor-toolbar a.fa-link::before          { content: "\f470"; } /* bi-link-45deg */
-.editor-toolbar a.fa-picture-o::before     { content: "\f42a"; } /* bi-image */
-.editor-toolbar a.fa-table::before         { content: "\f5aa"; } /* bi-table */
-.editor-toolbar a.fa-minus::before         { content: "\f63b"; } /* bi-dash-lg */
-.editor-toolbar a.fa-eye::before           { content: "\f341"; } /* bi-eye */
-.editor-toolbar a.fa-columns::before       { content: "\f460"; } /* bi-layout-split */
-.editor-toolbar a.fa-arrows-alt::before    { content: "\f14d"; } /* bi-arrows-fullscreen */
-.editor-toolbar a.fa-undo::before          { content: "\f117"; } /* bi-arrow-counterclockwise */
-.editor-toolbar a.fa-repeat::before        { content: "\f116"; } /* bi-arrow-clockwise */
-.editor-toolbar a.fa-question-circle::before { content: "\f505"; } /* bi-question-circle */
-.editor-toolbar a.fa-eraser::before        { content: "\f331"; } /* bi-eraser */
-
-.editor-toolbar a::before {
-    font-family: "bootstrap-icons" !important;
-    font-style: normal;
-    font-weight: normal;
-    display: inline-block;
-}
-
-.editor-toolbar {
-    opacity: 1 !important;
-}
-
-.editor-toolbar a {
-    font-size: 0;
-    width: 30px;
-    height: 30px;
-    display: inline-flex;
-    align-items: center;
-    justify-content: center;
+/* ============================================
+   Rendered Markdown — table & code block styles
+   Shared by agent detail view, template preview,
+   and any non-chat area that renders Markdown.
+   ============================================ */
+
+/* --- Tables --- */
+.rendered-markdown table {
+    width: 100%;
+    max-width: 100%;
+    margin: 0.75rem 0;
+    border-collapse: collapse;
+    border-spacing: 0;
+    border: 1px solid #dee2e6;
+    border-radius: 0.375rem;
+    overflow: hidden;
+    background-color: var(--bs-body-bg);
+    box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);
+    font-size: 0.875rem;
+    display: block;
+    overflow-x: auto;
+    white-space: nowrap;
+    -webkit-overflow-scrolling: touch;
 }
 
-.editor-toolbar a::before {
-    font-size: 1rem;
+@media (min-width: 768px) {
+    .rendered-markdown table {
+        display: table;
+        white-space: normal;
+    }
 }
 
-/* =============================================
-   SimpleMDE Dark Mode Overrides
-   ============================================= */
-[data-bs-theme="dark"] .editor-toolbar {
-    background-color: #343a40;
-    border-color: #495057;
+.rendered-markdown table th,
+.rendered-markdown table td {
+    padding: 0.5rem 0.75rem;
+    border-bottom: 1px solid #dee2e6;
+    border-right: 1px solid #dee2e6;
+    text-align: left;
+    vertical-align: top;
+    word-wrap: break-word;
+    line-height: 1.4;
 }
 
-[data-bs-theme="dark"] .editor-toolbar a {
-    color: #adb5bd !important;
+.rendered-markdown table th:last-child,
+.rendered-markdown table td:last-child {
+    border-right: none;
 }
 
-[data-bs-theme="dark"] .editor-toolbar a::before {
-    color: #adb5bd;
+.rendered-markdown table thead th {
+    background-color: #f8f9fa;
+    font-weight: 600;
+    color: #495057;
+    border-bottom: 2px solid #dee2e6;
 }
 
-[data-bs-theme="dark"] .editor-toolbar a:hover,
-[data-bs-theme="dark"] .editor-toolbar a:focus {
-    background-color: #495057;
-    border-color: #6c757d;
-    color: #e9ecef !important;
+.rendered-markdown table tbody tr:nth-child(even) {
+    background-color: rgba(0, 0, 0, 0.02);
 }
 
-[data-bs-theme="dark"] .editor-toolbar a:hover::before,
-[data-bs-theme="dark"] .editor-toolbar a:focus::before {
-    color: #e9ecef;
+.rendered-markdown table tbody tr:hover {
+    background-color: rgba(0, 0, 0, 0.04);
+    transition: background-color 0.15s ease-in-out;
 }
 
-[data-bs-theme="dark"] .editor-toolbar a.active,
-[data-bs-theme="dark"] .editor-toolbar a.active::before {
-    color: #86b7fe !important;
-    background-color: #1e3a5f;
+.rendered-markdown table th[align="center"],
+.rendered-markdown table td[align="center"] {
+    text-align: center;
 }
 
-[data-bs-theme="dark"] .editor-toolbar i.separator {
-    border-left-color: #495057;
-    border-right-color: #495057;
+.rendered-markdown table th[align="right"],
+.rendered-markdown table td[align="right"] {
+    text-align: right;
 }
 
-[data-bs-theme="dark"] .CodeMirror {
-    background-color: #212529;
+/* Dark mode tables */
+[data-bs-theme="dark"] .rendered-markdown table {
+    border-color: #495057;
+    background-color: var(--bs-dark);
     color: #e9ecef;
+}
+
+[data-bs-theme="dark"] .rendered-markdown table th,
+[data-bs-theme="dark"] .rendered-markdown table td {
     border-color: #495057;
 }
 
-[data-bs-theme="dark"] .CodeMirror-cursor {
-    border-left-color: #e9ecef;
+[data-bs-theme="dark"] .rendered-markdown table thead th {
+    background-color: #343a40;
+    color: #e9ecef;
+    border-bottom-color: #495057;
 }
 
-[data-bs-theme="dark"] .CodeMirror .CodeMirror-selected {
-    background-color: #1e3a5f !important;
+[data-bs-theme="dark"] .rendered-markdown table tbody tr:nth-child(even) {
+    background-color: rgba(255, 255, 255, 0.05);
 }
 
-[data-bs-theme="dark"] .CodeMirror-focused .CodeMirror-selected {
-    background-color: #0d6efd !important;
+[data-bs-theme="dark"] .rendered-markdown table tbody tr:hover {
+    background-color: rgba(255, 255, 255, 0.1);
 }
 
-[data-bs-theme="dark"] .CodeMirror .CodeMirror-line::selection,
-[data-bs-theme="dark"] .CodeMirror .CodeMirror-line > span::selection,
-[data-bs-theme="dark"] .CodeMirror .CodeMirror-line > span > span::selection {
-    background-color: #0d6efd !important;
-    color: #ffffff !important;
+.rendered-markdown table code {
+    background-color: rgba(0, 0, 0, 0.1);
+    padding: 0.125rem 0.25rem;
+    border-radius: 0.25rem;
+    font-size: 0.8em;
 }
 
-[data-bs-theme="dark"] .CodeMirror .CodeMirror-line::-moz-selection,
-[data-bs-theme="dark"] .CodeMirror .CodeMirror-line > span::-moz-selection,
-[data-bs-theme="dark"] .CodeMirror .CodeMirror-line > span > span::-moz-selection {
-    background-color: #0d6efd !important;
-    color: #ffffff !important;
+[data-bs-theme="dark"] .rendered-markdown table code {
+    background-color: rgba(255, 255, 255, 0.1);
 }
 
-[data-bs-theme="dark"] .editor-preview {
-    background-color: #2b3035;
-    color: #e9ecef;
+/* --- Code blocks --- */
+.rendered-markdown pre,
+.rendered-markdown pre[class*="language-"] {
+    overflow-x: auto;
+    max-width: 100%;
+    width: 100%;
+    box-sizing: border-box;
+    display: block;
+    white-space: pre;
+    background-color: #1e1e1e;
+    color: #d4d4d4;
+    border-radius: 0.375rem;
+    padding: 1rem;
+    margin: 0.75rem 0;
+    font-size: 0.85rem;
+    line-height: 1.5;
 }
 
-[data-bs-theme="dark"] .editor-preview-side {
-    background-color: #2b3035;
-    color: #e9ecef;
-    border-left-color: #495057;
+.rendered-markdown pre code {
+    display: block;
+    min-width: 0;
+    max-width: 100%;
+    overflow-x: auto;
+    white-space: pre;
+    background: transparent;
+    color: inherit;
+    padding: 0;
+    font-size: inherit;
 }
 
-[data-bs-theme="dark"] .editor-statusbar {
-    background-color: #343a40;
+/* Inline code */
+.rendered-markdown code:not(pre code) {
+    background-color: rgba(0, 0, 0, 0.06);
+    padding: 0.15rem 0.35rem;
+    border-radius: 0.25rem;
+    font-size: 0.85em;
+    color: #d63384;
+}
+
+[data-bs-theme="dark"] .rendered-markdown code:not(pre code) {
+    background-color: rgba(255, 255, 255, 0.1);
+    color: #e685b5;
+}
+
+/* Blockquotes */
+.rendered-markdown blockquote {
+    border-left: 4px solid #dee2e6;
+    padding-left: 1em;
+    color: #6c757d;
+    margin: 0.75rem 0;
+}
+
+[data-bs-theme="dark"] .rendered-markdown blockquote {
+    border-left-color: #495057;
     color: #adb5bd;
-    border-top-color: #495057;
-}
\ No newline at end of file
+}
diff --git a/application/single_app/static/images/custom_logo.png b/application/single_app/static/images/custom_logo.png
new file mode 100644
index 00000000..ecf6e652
Binary files /dev/null and b/application/single_app/static/images/custom_logo.png differ
diff --git a/application/single_app/static/images/custom_logo_dark.png b/application/single_app/static/images/custom_logo_dark.png
new file mode 100644
index 00000000..4f281945
Binary files /dev/null and b/application/single_app/static/images/custom_logo_dark.png differ
diff --git a/application/single_app/static/js/admin/admin_settings.js b/application/single_app/static/js/admin/admin_settings.js
index c6bdec36..21c989fd 100644
--- a/application/single_app/static/js/admin/admin_settings.js
+++ b/application/single_app/static/js/admin/admin_settings.js
@@ -1237,10 +1237,11 @@ function setupToggles() {
         const mathToggle = document.getElementById('toggle-math-plugin');
         const textToggle = document.getElementById('toggle-text-plugin');
         const factMemoryToggle = document.getElementById('toggle-fact-memory-plugin');
+        const tabularProcessingToggle = document.getElementById('toggle-tabular-processing-plugin');
         const embeddingToggle = document.getElementById('toggle-default-embedding-model-plugin');
         const allowUserPluginsToggle = document.getElementById('toggle-allow-user-plugins');
         const allowGroupPluginsToggle = document.getElementById('toggle-allow-group-plugins');
-        const toggles = [timeToggle, httpToggle, waitToggle, mathToggle, textToggle, factMemoryToggle, embeddingToggle, allowUserPluginsToggle, allowGroupPluginsToggle];
+        const toggles = [timeToggle, httpToggle, waitToggle, mathToggle, textToggle, factMemoryToggle, tabularProcessingToggle, embeddingToggle, allowUserPluginsToggle, allowGroupPluginsToggle];
         // Feedback area
         let feedbackDiv = document.getElementById('core-plugin-toggles-feedback');
         if (!feedbackDiv) {
@@ -1270,6 +1271,16 @@ function setupToggles() {
                 if (textToggle) textToggle.checked = !!settings.enable_text_plugin;
                 if (embeddingToggle) embeddingToggle.checked = !!settings.enable_default_embedding_model_plugin;
                 if (factMemoryToggle) factMemoryToggle.checked = !!settings.enable_fact_memory_plugin;
+                if (tabularProcessingToggle) {
+                    tabularProcessingToggle.checked = !!settings.enable_tabular_processing_plugin;
+                    const ecEnabled = !!settings.enable_enhanced_citations;
+                    tabularProcessingToggle.disabled = !ecEnabled;
+                    const depNote = document.getElementById('tabular-processing-dependency-note');
+                    if (depNote) {
+                        depNote.textContent = ecEnabled ? 'Requires Enhanced Citations' : 'Requires Enhanced Citations (currently disabled)';
+                        depNote.className = ecEnabled ? 'text-muted d-block ms-4' : 'text-danger d-block ms-4';
+                    }
+                }
                 if (allowUserPluginsToggle) allowUserPluginsToggle.checked = !!settings.allow_user_plugins;
                 if (allowGroupPluginsToggle) allowGroupPluginsToggle.checked = !!settings.allow_group_plugins;
             } catch (err) {
@@ -1291,6 +1302,7 @@ function setupToggles() {
                 enable_text_plugin: textToggle ? textToggle.checked : false,
                 enable_default_embedding_model_plugin: embeddingToggle ? embeddingToggle.checked : false,
                 enable_fact_memory_plugin: factMemoryToggle ? factMemoryToggle.checked : false,
+                enable_tabular_processing_plugin: tabularProcessingToggle ? tabularProcessingToggle.checked : false,
                 allow_user_plugins: allowUserPluginsToggle ? allowUserPluginsToggle.checked : false,
                 allow_group_plugins: allowGroupPluginsToggle ? allowGroupPluginsToggle.checked : false
             };
@@ -3844,11 +3856,12 @@ function checkOptionalFeaturesEnabled(stepNumber) {
                 return endpoint && key;
             }
         
-        case 11: // User feedback and archiving
-            // Check if feedback is enabled
+        case 11: // User feedback, archiving, and thoughts
+            // Check if feedback, archiving, or thoughts is enabled
             const feedbackEnabled = document.getElementById('enable_user_feedback')?.checked;
             const archivingEnabled = document.getElementById('enable_conversation_archiving')?.checked;
-            return feedbackEnabled || archivingEnabled;
+            const thoughtsEnabled = document.getElementById('enable_thoughts')?.checked;
+            return feedbackEnabled || archivingEnabled || thoughtsEnabled;
             
         case 12: // Enhanced citations and image generation
             // Check if enhanced citations or image generation is enabled
diff --git a/application/single_app/static/js/chat/chat-citations.js b/application/single_app/static/js/chat/chat-citations.js
index 9ec6bad3..60099398 100644
--- a/application/single_app/static/js/chat/chat-citations.js
+++ b/application/single_app/static/js/chat/chat-citations.js
@@ -11,6 +11,14 @@ import { showEnhancedCitationModal } from './chat-enhanced-citations.js';
 
 const chatboxEl = document.getElementById("chatbox");
 
+function escapeAttribute(value) {
+  return String(value)
+    .replace(/&/g, '&amp;')
+    .replace(/"/g, '&quot;')
+    .replace(/</g, '&lt;')
+    .replace(/>/g, '&gt;');
+}
+
 export function parseDocIdAndPage(citationId) {
   // ... (keep existing implementation)
   const underscoreIndex = citationId.lastIndexOf("_");
@@ -24,9 +32,9 @@ export function parseDocIdAndPage(citationId) {
 
 export function parseCitations(message) {
   // ... (keep existing implementation)
-  const citationRegex = /\(Source:\s*([^,]+),\s*Page(?:s)?:\s*([^)]+)\)\s*((?:\[#.*?\]\s*)+)/gi;
+  const citationRegex = /\(Source:\s*([^,]+),\s*(Page(?:s)?|Sheet(?:s)?|Location):\s*([^)]+)\)\s*((?:\[#.*?\]\s*)+)/gi;
 
-  let result = message.replace(citationRegex, (whole, filename, pages, bracketSection) => {
+  let result = message.replace(citationRegex, (whole, filename, locationLabel, locations, bracketSection) => {
     let filenameHtml;
     if (/^https?:\/\/.+/i.test(filename.trim())) {
       filenameHtml = `<a href="${filename.trim()}" target="_blank" rel="noopener noreferrer">${filename.trim()}</a>`;
@@ -36,6 +44,7 @@ export function parseCitations(message) {
 
     const bracketMatches = bracketSection.match(/\[#.*?\]/g) || [];
     const pageToRefMap = {};
+    const orderedRefs = [];
 
     bracketMatches.forEach((match) => {
       let inner = match.slice(2, -1).trim();
@@ -43,6 +52,7 @@ export function parseCitations(message) {
       refs.forEach((r) => {
         let ref = r.trim();
         if (ref.startsWith('#')) ref = ref.slice(1);
+        orderedRefs.push(ref);
         const parts = ref.split('_');
         const pageNumber = parts.pop();
         // Ensure docId part is also captured if needed, though ref is the full ID here
@@ -56,8 +66,15 @@ export function parseCitations(message) {
       return underscoreIndex === -1 ? ref : ref.slice(0, underscoreIndex + 1);
     }
 
-    const pagesTokens = pages.split(/,/).map(tok => tok.trim());
-    const linkedTokens = pagesTokens.map(token => {
+    const normalizedLocationLabel = locationLabel.toLowerCase();
+    const locationTokens = locations.split(/,/).map(tok => tok.trim());
+    const linkedTokens = locationTokens.map((token, index) => {
+      if (!normalizedLocationLabel.startsWith('page')) {
+        const ref = orderedRefs[index] || orderedRefs[0];
+        const sheetName = normalizedLocationLabel.startsWith('sheet') ? token : null;
+        return buildAnchorIfExists(token, ref, sheetName);
+      }
+
       const dashParts = token.split(/[–—-]/).map(p => p.trim());
 
       if (dashParts.length === 2 && dashParts[0] && dashParts[1]) {
@@ -94,7 +111,7 @@ export function parseCitations(message) {
     });
 
     const linkedPagesText = linkedTokens.join(', ');
-    return `(Source: ${filenameHtml}, Pages: ${linkedPagesText})`;
+    return `(Source: ${filenameHtml}, ${locationLabel}: ${linkedPagesText})`;
   });
 
   // Cleanup pass: strip any remaining [#guid...] bracket groups that the main regex didn't match.
@@ -107,14 +124,15 @@ export function parseCitations(message) {
 }
 
 
-export function buildAnchorIfExists(pageStr, citationId) {
+export function buildAnchorIfExists(pageStr, citationId, sheetName = null) {
   // ... (keep existing implementation)
    if (!citationId) {
     return pageStr;
   }
   // Ensure citationId doesn't have a leading # if passed accidentally
   const cleanCitationId = citationId.startsWith('#') ? citationId.slice(1) : citationId;
-  return `<a href="#" class="citation-link" data-citation-id="${cleanCitationId}" target="_blank" rel="noopener noreferrer">${pageStr}</a>`;
+  const sheetNameAttribute = sheetName ? ` data-sheet-name="${escapeAttribute(sheetName)}"` : '';
+  return `<a href="#" class="citation-link" data-citation-id="${cleanCitationId}"${sheetNameAttribute} target="_blank" rel="noopener noreferrer">${pageStr}</a>`;
 }
 
 // --- MODIFIED: fetchCitedText handles errors more gracefully ---
@@ -609,6 +627,7 @@ if (chatboxEl) {
       }
 
       const { docId, pageNumber } = parseDocIdAndPage(citationId);
+      const sheetName = target.getAttribute("data-sheet-name");
 
       // Safety check: Ensure docId and pageNumber were parsed correctly
       if (!docId || !pageNumber) {
@@ -649,7 +668,7 @@ if (chatboxEl) {
       if (attemptEnhanced) {
           // console.log(`Attempting Enhanced Citation for ${docId}, page/timestamp ${pageNumber}, citationId ${citationId}`);
           // Use new enhanced citation system that supports multiple file types
-          showEnhancedCitationModal(docId, pageNumber, citationId);
+          showEnhancedCitationModal(docId, pageNumber, citationId, sheetName);
       } else {
           // console.log(`Fetching Text Citation for ${citationId}`);
           // Use text citation if globally disabled OR explicitly disabled for this doc OR if parsing failed earlier
diff --git a/application/single_app/static/js/chat/chat-conversation-details.js b/application/single_app/static/js/chat/chat-conversation-details.js
index 19851bae..484128af 100644
--- a/application/single_app/static/js/chat/chat-conversation-details.js
+++ b/application/single_app/static/js/chat/chat-conversation-details.js
@@ -75,7 +75,7 @@ export async function showConversationDetails(conversationId) {
  * @returns {string} HTML string
  */
 function renderConversationMetadata(metadata, conversationId) {
-  const { context = [], tags = [], strict = false, classification = [], last_updated, chat_type = 'personal', is_pinned = false, is_hidden = false, scope_locked, locked_contexts = [] } = metadata;
+  const { context = [], tags = [], strict = false, classification = [], last_updated, chat_type = 'personal', is_pinned = false, is_hidden = false, scope_locked, locked_contexts = [], summary = null } = metadata;
   
   // Organize tags by category
   const tagsByCategory = {
@@ -97,6 +97,18 @@ function renderConversationMetadata(metadata, conversationId) {
   // Build HTML sections
   let html = `
     <div class="row g-3">
+      <!-- Summary Section -->
+      <div class="col-12">
+        <div class="card">
+          <div class="card-header bg-primary bg-opacity-75 text-white d-flex justify-content-between align-items-center">
+            <h6 class="mb-0"><i class="bi bi-blockquote-left me-2"></i>Summary</h6>
+            ${summary ? `<small class="opacity-75">Generated ${formatDate(summary.generated_at)}${summary.model_deployment ? ` · ${summary.model_deployment}` : ''}</small>` : ''}
+          </div>
+          <div class="card-body" id="summary-card-body">
+            ${renderSummaryContent(summary, conversationId)}
+          </div>
+        </div>
+      </div>
       <!-- Basic Info -->
       <div class="col-12">
         <div class="card">
@@ -570,8 +582,159 @@ function extractPageNumbers(chunkIds) {
   return pages.sort((a, b) => parseInt(a) - parseInt(b));
 }
 
+/**
+ * Render the summary card body content
+ * @param {Object|null} summary - Existing summary data or null
+ * @param {string} conversationId - The conversation ID
+ * @returns {string} HTML string
+ */
+function renderSummaryContent(summary, conversationId) {
+  if (summary && summary.content) {
+    return `
+      <p class="mb-2">${escapeHtml(summary.content)}</p>
+      <div class="d-flex justify-content-end">
+        <button class="btn btn-sm btn-outline-secondary" id="regenerate-summary-btn"
+                data-conversation-id="${conversationId}">
+          <i class="bi bi-arrow-clockwise me-1"></i>Regenerate
+        </button>
+      </div>
+    `;
+  }
+
+  // Build model options from the global model-select dropdown
+  const modelOptions = getAvailableModelOptions();
+  return `
+    <p class="text-muted mb-3">No summary has been generated for this conversation yet.</p>
+    <div class="d-flex align-items-center gap-2">
+      <select class="form-select form-select-sm" id="summary-model-select" style="max-width: 260px;">
+        ${modelOptions}
+      </select>
+      <button class="btn btn-sm btn-primary" id="generate-summary-btn"
+              data-conversation-id="${conversationId}">
+        <i class="bi bi-blockquote-left me-1"></i>Generate Summary
+      </button>
+    </div>
+  `;
+}
+
+/**
+ * Get available model options from the global #model-select dropdown
+ * @returns {string} HTML option elements
+ */
+function getAvailableModelOptions() {
+  const globalSelect = document.getElementById('model-select');
+  if (!globalSelect) {
+    return '<option value="">Default</option>';
+  }
+  let options = '';
+  for (const opt of globalSelect.options) {
+    options += `<option value="${escapeHtml(opt.value)}"${opt.selected ? ' selected' : ''}>${escapeHtml(opt.text)}</option>`;
+  }
+  return options || '<option value="">Default</option>';
+}
+
+/**
+ * Handle summary generation (generate or regenerate)
+ * @param {string} conversationId - The conversation ID
+ * @param {string} modelDeployment - Selected model deployment
+ */
+async function handleGenerateSummary(conversationId, modelDeployment) {
+  const cardBody = document.getElementById('summary-card-body');
+  if (!cardBody) {
+    return;
+  }
+
+  cardBody.innerHTML = `
+    <div class="d-flex align-items-center gap-2">
+      <div class="spinner-border spinner-border-sm text-primary" role="status">
+        <span class="visually-hidden">Generating...</span>
+      </div>
+      <span class="text-muted">Generating summary...</span>
+    </div>
+  `;
+
+  try {
+    const response = await fetch(`/api/conversations/${conversationId}/summary`, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ model_deployment: modelDeployment })
+    });
+
+    if (!response.ok) {
+      const errData = await response.json().catch(() => ({}));
+      throw new Error(errData.error || `HTTP ${response.status}`);
+    }
+
+    const data = await response.json();
+    const summary = data.summary;
+    cardBody.innerHTML = renderSummaryContent(summary, conversationId);
+
+    // Update card header with generation info
+    const cardHeader = cardBody.closest('.card').querySelector('.card-header');
+    if (cardHeader && summary) {
+      const smallEl = cardHeader.querySelector('small');
+      const infoText = `Generated ${formatDate(summary.generated_at)}${summary.model_deployment ? ` · ${summary.model_deployment}` : ''}`;
+      if (smallEl) {
+        smallEl.textContent = infoText;
+      } else {
+        const small = document.createElement('small');
+        small.className = 'opacity-75';
+        small.textContent = infoText;
+        cardHeader.appendChild(small);
+      }
+    }
+
+  } catch (error) {
+    console.error('Error generating summary:', error);
+    cardBody.innerHTML = `
+      <div class="text-danger mb-2">
+        <i class="bi bi-exclamation-triangle me-1"></i>
+        Failed to generate summary: ${escapeHtml(error.message)}
+      </div>
+      ${renderSummaryContent(null, conversationId)}
+    `;
+  }
+}
+
+/**
+ * Simple HTML escapefor display
+ * @param {string} str - String to escape
+ * @returns {string} Escaped string
+ */
+function escapeHtml(str) {
+  if (!str) {
+    return '';
+  }
+  const div = document.createElement('div');
+  div.textContent = str;
+  return div.innerHTML;
+}
+
 // Event listeners for details buttons
 document.addEventListener('click', function(e) {
+  // Generate summary button
+  if (e.target.closest('#generate-summary-btn')) {
+    e.preventDefault();
+    const btn = e.target.closest('#generate-summary-btn');
+    const cid = btn.getAttribute('data-conversation-id');
+    const modelSelect = document.getElementById('summary-model-select');
+    const model = modelSelect ? modelSelect.value : '';
+    handleGenerateSummary(cid, model);
+    return;
+  }
+
+  // Regenerate summary button
+  if (e.target.closest('#regenerate-summary-btn')) {
+    e.preventDefault();
+    const btn = e.target.closest('#regenerate-summary-btn');
+    const cid = btn.getAttribute('data-conversation-id');
+    // Use the currently selected global model for regeneration
+    const globalSelect = document.getElementById('model-select');
+    const model = globalSelect ? globalSelect.value : '';
+    handleGenerateSummary(cid, model);
+    return;
+  }
+
   if (e.target.closest('.details-btn')) {
     e.preventDefault();
     
diff --git a/application/single_app/static/js/chat/chat-conversations.js b/application/single_app/static/js/chat/chat-conversations.js
index 0af0a768..ebcc994f 100644
--- a/application/single_app/static/js/chat/chat-conversations.js
+++ b/application/single_app/static/js/chat/chat-conversations.js
@@ -28,6 +28,7 @@ let allConversations = []; // Store all conversations for client-side filtering
 let isLoadingConversations = false; // Prevent concurrent loads
 let showQuickSearch = false; // Track if quick search input is visible
 let quickSearchTerm = ""; // Current search term
+let pendingConversationCreation = null; // Reuse a single in-flight create request
 
 // Clear selected conversations when loading the page
 document.addEventListener('DOMContentLoaded', () => {
@@ -1067,7 +1068,21 @@ export function deleteConversation(conversationId) {
 }
 
 // Create a new conversation via API
-export async function createNewConversation(callback) {
+export async function createNewConversation(callback, options = {}) {
+    if (pendingConversationCreation) {
+      try {
+        await pendingConversationCreation;
+        if (typeof callback === "function") {
+          callback();
+        }
+      } catch (error) {
+        // The original caller already surfaced the creation failure.
+      }
+      return;
+    }
+
+  const { preserveSelections = false } = options;
+
     // Disable new button? Show loading?
     if (newConversationBtn) newConversationBtn.disabled = true;
     
@@ -1079,54 +1094,61 @@ export async function createNewConversation(callback) {
     }
     
   try {
-    const response = await fetch("/api/create_conversation", {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      credentials: "same-origin",
-    });
-    if (!response.ok) {
-      const errData = await response.json().catch(() => ({}));
-      throw new Error(errData.error || "Failed to create conversation");
-    }
-    const data = await response.json();
-    if (!data.conversation_id) {
-      throw new Error("No conversation_id returned from server.");
-    }
+    pendingConversationCreation = (async () => {
+      const response = await fetch("/api/create_conversation", {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+        },
+        credentials: "same-origin",
+      });
+      if (!response.ok) {
+        const errData = await response.json().catch(() => ({}));
+        throw new Error(errData.error || "Failed to create conversation");
+      }
+      const data = await response.json();
+      if (!data.conversation_id) {
+        throw new Error("No conversation_id returned from server.");
+      }
 
-    currentConversationId = data.conversation_id;
-    // Reset scope lock for new conversation
-    resetScopeLock();
-    // Add to list (pass empty classifications for new convo)
-    addConversationToList(data.conversation_id, data.title /* Use title from API if provided */, []);
-    
-    // Don't call selectConversation here if we're about to send a message
-    // because selectConversation clears the chatbox, which would remove
-    // the user message that's about to be appended by actuallySendMessage
-    // Instead, just update the UI elements directly
-    window.currentConversationId = data.conversation_id;
-    const titleEl = document.getElementById("current-conversation-title");
-    if (titleEl) {
-      titleEl.textContent = data.title || "New Conversation";
-    }
-    // Clear classification/tag badges from previous conversation
-    if (currentConversationClassificationsEl) {
-      currentConversationClassificationsEl.innerHTML = "";
-    }
-    updateConversationUrl(data.conversation_id);
-    console.log('[createNewConversation] Created conversation without reload:', data.conversation_id);
+      currentConversationId = data.conversation_id;
+      // Reset scope lock for new conversation
+      resetScopeLock({ preserveSelections });
+      // Add to list (pass empty classifications for new convo)
+      addConversationToList(data.conversation_id, data.title /* Use title from API if provided */, []);
+
+      // Don't call selectConversation here if we're about to send a message
+      // because selectConversation clears the chatbox, which would remove
+      // the user message that's about to be appended by actuallySendMessage
+      // Instead, just update the UI elements directly
+      window.currentConversationId = data.conversation_id;
+      const titleEl = document.getElementById("current-conversation-title");
+      if (titleEl) {
+        titleEl.textContent = data.title || "New Conversation";
+      }
+      // Clear classification/tag badges from previous conversation
+      if (currentConversationClassificationsEl) {
+        currentConversationClassificationsEl.innerHTML = "";
+      }
+      updateConversationUrl(data.conversation_id);
+      console.log('[createNewConversation] Created conversation without reload:', data.conversation_id);
+
+      return data;
+    })();
+
+    const data = await pendingConversationCreation;
 
     // Execute callback if provided (e.g., to send the first message)
     if (typeof callback === "function") {
       callback();
     }
 
-
+    return data;
   } catch (error) {
     console.error("Error creating conversation:", error);
     showToast(`Failed to create a new conversation: ${error.message}`, "danger");
   } finally {
+      pendingConversationCreation = null;
       if (newConversationBtn) newConversationBtn.disabled = false;
   }
 }
diff --git a/application/single_app/static/js/chat/chat-documents.js b/application/single_app/static/js/chat/chat-documents.js
index 44596872..17bd3166 100644
--- a/application/single_app/static/js/chat/chat-documents.js
+++ b/application/single_app/static/js/chat/chat-documents.js
@@ -160,10 +160,19 @@ export function restoreScopeLockState(lockState, contexts) {
  * Reset scope lock for a new conversation.
  * Resets to "All" with no lock.
  */
-export function resetScopeLock() {
+export function resetScopeLock(options = {}) {
+  const { preserveSelections = false } = options;
+
   scopeLocked = null;
   lockedContexts = [];
 
+  if (preserveSelections) {
+    buildScopeDropdown();
+    updateScopeLockIcon();
+    updateHeaderLockIcon();
+    return;
+  }
+
   const groups = window.userGroups || [];
   const publicWorkspaces = window.userVisiblePublicWorkspaces || [];
   selectedPersonal = true;
diff --git a/application/single_app/static/js/chat/chat-enhanced-citations.js b/application/single_app/static/js/chat/chat-enhanced-citations.js
index dcda708b..792d9e90 100644
--- a/application/single_app/static/js/chat/chat-enhanced-citations.js
+++ b/application/single_app/static/js/chat/chat-enhanced-citations.js
@@ -18,11 +18,13 @@ export function getFileType(fileName) {
     const imageExtensions = ['jpg', 'jpeg', 'png', 'bmp', 'tiff', 'tif'];
     const videoExtensions = ['mp4', 'mov', 'avi', 'mkv', 'flv', 'webm', 'wmv', 'm4v', '3gp'];
     const audioExtensions = ['mp3', 'wav', 'ogg', 'aac', 'flac', 'm4a'];
-    
+    const tabularExtensions = ['csv', 'xlsx', 'xls', 'xlsm'];
+
     if (imageExtensions.includes(ext)) return 'image';
     if (ext === 'pdf') return 'pdf';
     if (videoExtensions.includes(ext)) return 'video';
     if (audioExtensions.includes(ext)) return 'audio';
+    if (tabularExtensions.includes(ext)) return 'tabular';
     
     return 'other';
 }
@@ -32,8 +34,9 @@ export function getFileType(fileName) {
  * @param {string} docId - Document ID
  * @param {string|number} pageNumberOrTimestamp - Page number for PDF or timestamp for video/audio
  * @param {string} citationId - Citation ID for fallback
+ * @param {string|null} initialSheetName - Workbook sheet to open initially for tabular files
  */
-export function showEnhancedCitationModal(docId, pageNumberOrTimestamp, citationId) {
+export function showEnhancedCitationModal(docId, pageNumberOrTimestamp, citationId, initialSheetName = null) {
     // Get document metadata to determine file type
     const docMetadata = getDocumentMetadata(docId);
     if (!docMetadata || !docMetadata.file_name) {
@@ -66,6 +69,9 @@ export function showEnhancedCitationModal(docId, pageNumberOrTimestamp, citation
             const audioTimestamp = convertTimestampToSeconds(pageNumberOrTimestamp);
             showAudioModal(docId, audioTimestamp, docMetadata.file_name);
             break;
+        case 'tabular':
+            showTabularDownloadModal(docId, docMetadata.file_name, initialSheetName);
+            break;
         default:
             // Fall back to text citation for unsupported types
             import('./chat-citations.js').then(module => {
@@ -291,6 +297,168 @@ export function showAudioModal(docId, timestamp, fileName) {
     modalInstance.show();
 }
 
+/**
+ * Show tabular file preview modal with data table
+ * @param {string} docId - Document ID
+ * @param {string} fileName - File name
+ * @param {string|null} initialSheetName - Workbook sheet to open initially
+ */
+export function showTabularDownloadModal(docId, fileName, initialSheetName = null) {
+    console.log(`Showing tabular preview modal for docId: ${docId}, fileName: ${fileName}`);
+    showLoadingIndicator();
+
+    // Create or get tabular modal
+    let tabularModal = document.getElementById("enhanced-tabular-modal");
+    if (!tabularModal) {
+        tabularModal = createTabularModal();
+    }
+
+    const title = tabularModal.querySelector(".modal-title");
+    const tableContainer = tabularModal.querySelector("#enhanced-tabular-table-container");
+    const rowInfo = tabularModal.querySelector("#enhanced-tabular-row-info");
+    const downloadBtn = tabularModal.querySelector("#enhanced-tabular-download");
+    const errorContainer = tabularModal.querySelector("#enhanced-tabular-error");
+    const sheetControls = tabularModal.querySelector("#enhanced-tabular-sheet-controls");
+    const sheetSelect = tabularModal.querySelector("#enhanced-tabular-sheet-select");
+
+    title.textContent = `Tabular Data: ${fileName}`;
+    tableContainer.innerHTML = '<div class="text-center p-4"><div class="spinner-border text-primary" role="status"><span class="visually-hidden">Loading...</span></div><p class="mt-2 text-muted">Loading data preview...</p></div>';
+    rowInfo.textContent = '';
+    errorContainer.classList.add('d-none');
+    sheetControls.classList.add('d-none');
+    sheetSelect.innerHTML = '';
+
+    const downloadUrl = `/api/enhanced_citations/tabular_workspace?doc_id=${encodeURIComponent(docId)}`;
+    downloadBtn.href = downloadUrl;
+    downloadBtn.download = fileName;
+
+    // Show modal immediately with loading state
+    const modalInstance = new bootstrap.Modal(tabularModal);
+    modalInstance.show();
+
+    const escapeOptionValue = (value) => String(value)
+        .replace(/&/g, '&amp;')
+        .replace(/</g, '&lt;')
+        .replace(/>/g, '&gt;')
+        .replace(/"/g, '&quot;');
+
+    const loadTabularPreview = (selectedSheetName = null) => {
+        errorContainer.classList.add('d-none');
+
+        const params = new URLSearchParams({
+            doc_id: docId,
+        });
+        if (selectedSheetName) {
+            params.set('sheet_name', selectedSheetName);
+        }
+
+        const previewUrl = `/api/enhanced_citations/tabular_preview?${params.toString()}`;
+        fetch(previewUrl)
+            .then(response => {
+                if (!response.ok) throw new Error(`HTTP ${response.status}`);
+                return response.json();
+            })
+            .then(data => {
+                hideLoadingIndicator();
+                if (data.error) {
+                    showTabularError(tableContainer, errorContainer, data.error);
+                    return;
+                }
+
+                title.textContent = data.selected_sheet
+                    ? `Tabular Data: ${fileName} [${data.selected_sheet}]`
+                    : `Tabular Data: ${fileName}`;
+
+                const sheetNames = Array.isArray(data.sheet_names) ? data.sheet_names : [];
+                if (sheetNames.length > 1) {
+                    sheetControls.classList.remove('d-none');
+                    sheetSelect.innerHTML = sheetNames
+                        .map(sheetName => {
+                            const isSelected = sheetName === data.selected_sheet ? ' selected' : '';
+                            return `<option value="${escapeOptionValue(sheetName)}"${isSelected}>${escapeOptionValue(sheetName)}</option>`;
+                        })
+                        .join('');
+                    sheetSelect.onchange = () => {
+                        showLoadingIndicator();
+                        loadTabularPreview(sheetSelect.value);
+                    };
+                } else {
+                    sheetControls.classList.add('d-none');
+                    sheetSelect.innerHTML = '';
+                }
+
+                renderTabularPreview(tableContainer, rowInfo, data);
+            })
+            .catch(error => {
+                hideLoadingIndicator();
+                console.error('Error loading tabular preview:', error);
+                showTabularError(tableContainer, errorContainer, 'Could not load data preview.');
+            });
+    };
+
+    loadTabularPreview(initialSheetName);
+}
+
+/**
+ * Render tabular data as an HTML table
+ * @param {HTMLElement} container - Table container element
+ * @param {HTMLElement} rowInfo - Row info display element
+ * @param {Object} data - Preview data from API
+ */
+function renderTabularPreview(container, rowInfo, data) {
+    const { columns, rows, total_rows, truncated, selected_sheet } = data;
+
+    // Build table HTML
+    let html = '<table class="table table-striped table-bordered table-sm table-hover mb-0">';
+
+    // Header
+    html += '<thead class="table-dark sticky-top"><tr>';
+    for (const col of columns) {
+        const escaped = col.replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;');
+        html += `<th class="text-nowrap">${escaped}</th>`;
+    }
+    html += '</tr></thead>';
+
+    // Body
+    html += '<tbody>';
+    for (const row of rows) {
+        html += '<tr>';
+        for (const cell of row) {
+            const val = cell === null || cell === undefined ? '' : String(cell);
+            const escaped = val.replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;');
+            html += `<td>${escaped}</td>`;
+        }
+        html += '</tr>';
+    }
+    html += '</tbody></table>';
+
+    container.innerHTML = html;
+
+    // Row info
+    const displayedRows = rows.length;
+    const hasTotalRows = total_rows !== null && total_rows !== undefined;
+    const totalFormatted = hasTotalRows ? total_rows.toLocaleString() : displayedRows.toLocaleString();
+    const sheetPrefix = selected_sheet ? `Sheet ${selected_sheet} · ` : '';
+    if (truncated) {
+        const truncationSuffix = hasTotalRows ? `${totalFormatted} rows` : `${displayedRows.toLocaleString()}+ rows`;
+        rowInfo.textContent = `${sheetPrefix}Showing ${displayedRows.toLocaleString()} of ${truncationSuffix}`;
+    } else {
+        rowInfo.textContent = `${sheetPrefix}${totalFormatted} rows, ${columns.length} columns`;
+    }
+}
+
+/**
+ * Show error state in tabular modal with download fallback
+ * @param {HTMLElement} tableContainer - Table container element
+ * @param {HTMLElement} errorContainer - Error display element
+ * @param {string} message - Error message
+ */
+function showTabularError(tableContainer, errorContainer, message) {
+    tableContainer.innerHTML = '<div class="text-center p-4"><i class="bi bi-file-earmark-spreadsheet display-1 text-success"></i></div>';
+    errorContainer.textContent = message + ' You can still download the file below.';
+    errorContainer.classList.remove('d-none');
+}
+
 /**
  * Convert timestamp string to seconds
  * @param {string|number} timestamp - Timestamp in various formats
@@ -445,3 +613,40 @@ function createPdfModal() {
     document.body.appendChild(modal);
     return modal;
 }
+
+/**
+ * Create tabular file preview modal HTML structure
+ * @returns {HTMLElement} - Modal element
+ */
+function createTabularModal() {
+    const modal = document.createElement("div");
+    modal.id = "enhanced-tabular-modal";
+    modal.classList.add("modal", "fade");
+    modal.tabIndex = -1;
+    modal.innerHTML = `
+        <div class="modal-dialog modal-xl modal-dialog-centered">
+            <div class="modal-content">
+                <div class="modal-header">
+                    <h5 class="modal-title">Tabular Data Citation</h5>
+                    <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+                </div>
+                <div class="modal-body p-0">
+                    <div id="enhanced-tabular-error" class="alert alert-warning m-3 d-none"></div>
+                    <div id="enhanced-tabular-sheet-controls" class="px-3 pt-3 d-none">
+                        <label for="enhanced-tabular-sheet-select" class="form-label small text-muted mb-1">Worksheet</label>
+                        <select id="enhanced-tabular-sheet-select" class="form-select form-select-sm"></select>
+                    </div>
+                    <div id="enhanced-tabular-table-container" style="max-height: 60vh; overflow: auto;"></div>
+                </div>
+                <div class="modal-footer d-flex justify-content-between align-items-center">
+                    <span id="enhanced-tabular-row-info" class="text-muted small"></span>
+                    <a id="enhanced-tabular-download" class="btn btn-primary btn-sm" target="_blank" rel="noopener noreferrer">
+                        <i class="bi bi-download me-1"></i>Download File
+                    </a>
+                </div>
+            </div>
+        </div>
+    `;
+    document.body.appendChild(modal);
+    return modal;
+}
diff --git a/application/single_app/static/js/chat/chat-export.js b/application/single_app/static/js/chat/chat-export.js
index 269cbfe0..fc53d2b6 100644
--- a/application/single_app/static/js/chat/chat-export.js
+++ b/application/single_app/static/js/chat/chat-export.js
@@ -15,6 +15,8 @@ let exportConversationIds = [];
 let exportConversationTitles = {};
 let exportFormat = 'json';
 let exportPackaging = 'single';
+let includeSummaryIntro = false;
+let summaryModelDeployment = '';
 let currentStep = 1;
 let totalSteps = 3;
 let skipSelectionStep = false;
@@ -53,14 +55,16 @@ function openExportWizard(conversationIds, skipSelection) {
     exportConversationTitles = {};
     exportFormat = 'json';
     exportPackaging = conversationIds.length > 1 ? 'zip' : 'single';
+    includeSummaryIntro = false;
+    summaryModelDeployment = _getDefaultSummaryModel();
     skipSelectionStep = !!skipSelection;
 
     // Determine step configuration
     if (skipSelectionStep) {
-        totalSteps = 3;
+        totalSteps = 4;
         currentStep = 1; // Format step (mapped to visual step)
     } else {
-        totalSteps = 4;
+        totalSteps = 5;
         currentStep = 1; // Selection review step
     }
 
@@ -142,19 +146,21 @@ function _renderCurrentStep() {
     if (!stepBody) return;
 
     if (skipSelectionStep) {
-        // Steps: 1=Format, 2=Packaging, 3=Download
+        // Steps: 1=Format, 2=Packaging, 3=Summary, 4=Download
         switch (currentStep) {
             case 1: _renderFormatStep(stepBody); break;
             case 2: _renderPackagingStep(stepBody); break;
-            case 3: _renderDownloadStep(stepBody); break;
+            case 3: _renderSummaryStep(stepBody); break;
+            case 4: _renderDownloadStep(stepBody); break;
         }
     } else {
-        // Steps: 1=Selection, 2=Format, 3=Packaging, 4=Download
+        // Steps: 1=Selection, 2=Format, 3=Packaging, 4=Summary, 5=Download
         switch (currentStep) {
             case 1: _renderSelectionStep(stepBody); break;
             case 2: _renderFormatStep(stepBody); break;
             case 3: _renderPackagingStep(stepBody); break;
-            case 4: _renderDownloadStep(stepBody); break;
+            case 4: _renderSummaryStep(stepBody); break;
+            case 5: _renderDownloadStep(stepBody); break;
         }
     }
 }
@@ -210,7 +216,7 @@ function _renderFormatStep(container) {
             <p class="text-muted small mb-3">Select the format for your exported conversations.</p>
         </div>
         <div class="row g-3">
-            <div class="col-6">
+            <div class="col-4">
                 <div class="action-type-card card h-100 text-center p-3 ${exportFormat === 'json' ? 'selected' : ''}" data-format="json" role="button" tabindex="0">
                     <div class="card-body d-flex flex-column align-items-center justify-content-center">
                         <i class="bi bi-filetype-json display-5 mb-2 text-primary"></i>
@@ -219,7 +225,7 @@ function _renderFormatStep(container) {
                     </div>
                 </div>
             </div>
-            <div class="col-6">
+            <div class="col-4">
                 <div class="action-type-card card h-100 text-center p-3 ${exportFormat === 'markdown' ? 'selected' : ''}" data-format="markdown" role="button" tabindex="0">
                     <div class="card-body d-flex flex-column align-items-center justify-content-center">
                         <i class="bi bi-filetype-md display-5 mb-2 text-success"></i>
@@ -228,6 +234,15 @@ function _renderFormatStep(container) {
                     </div>
                 </div>
             </div>
+            <div class="col-4">
+                <div class="action-type-card card h-100 text-center p-3 ${exportFormat === 'pdf' ? 'selected' : ''}" data-format="pdf" role="button" tabindex="0">
+                    <div class="card-body d-flex flex-column align-items-center justify-content-center">
+                        <i class="bi bi-filetype-pdf display-5 mb-2 text-danger"></i>
+                        <h6 class="mb-1">PDF</h6>
+                        <p class="text-muted small mb-0">Print-ready format with chat bubbles. Ideal for archiving and printing.</p>
+                    </div>
+                </div>
+            </div>
         </div>`;
 
     // Wire card clicks
@@ -297,11 +312,68 @@ function _renderPackagingStep(container) {
     });
 }
 
+function _renderSummaryStep(container) {
+    const mainModelSelect = getEl('model-select');
+    const hasModelOptions = Boolean(mainModelSelect && mainModelSelect.options.length > 0);
+    const defaultSummaryModel = summaryModelDeployment || _getDefaultSummaryModel();
+    const perConversationText = exportConversationIds.length > 1
+        ? 'An intro will be generated for each exported conversation.'
+        : 'An intro will be generated for this conversation.';
+
+    container.innerHTML = `
+        <div class="mb-3">
+            <h6 class="mb-1">Optional Intro Summary</h6>
+            <p class="text-muted small mb-3">Add a short abstract before the exported transcript. ${perConversationText}</p>
+        </div>
+        <div class="form-check form-switch mb-3">
+            <input class="form-check-input" type="checkbox" role="switch" id="export-summary-toggle" ${includeSummaryIntro ? 'checked' : ''}>
+            <label class="form-check-label" for="export-summary-toggle">Include AI-generated intro summary</label>
+        </div>
+        <div id="export-summary-model-container" class="card ${includeSummaryIntro ? '' : 'd-none'}">
+            <div class="card-body">
+                <label for="export-summary-model" class="form-label">Summary model</label>
+                <select id="export-summary-model" class="form-select" ${hasModelOptions ? '' : 'disabled'}>
+                    ${hasModelOptions ? mainModelSelect.innerHTML : '<option value="">No chat models available</option>'}
+                </select>
+                <div class="form-text">Uses the same model list as the chat composer.</div>
+            </div>
+        </div>`;
+
+    const toggle = getEl('export-summary-toggle');
+    const modelContainer = getEl('export-summary-model-container');
+    const summaryModelSelect = getEl('export-summary-model');
+
+    if (summaryModelSelect && hasModelOptions) {
+        summaryModelSelect.value = defaultSummaryModel || summaryModelSelect.value;
+        summaryModelDeployment = summaryModelSelect.value;
+        summaryModelSelect.addEventListener('change', () => {
+            summaryModelDeployment = summaryModelSelect.value;
+        });
+    }
+
+    if (toggle) {
+        toggle.addEventListener('change', () => {
+            includeSummaryIntro = toggle.checked;
+            if (modelContainer) {
+                modelContainer.classList.toggle('d-none', !includeSummaryIntro);
+            }
+            if (includeSummaryIntro && summaryModelSelect && !summaryModelSelect.value) {
+                summaryModelSelect.value = _getDefaultSummaryModel();
+                summaryModelDeployment = summaryModelSelect.value;
+            }
+        });
+    }
+}
+
 function _renderDownloadStep(container) {
     const count = exportConversationIds.length;
-    const formatLabel = exportFormat === 'json' ? 'JSON' : 'Markdown';
+    const formatLabels = { json: 'JSON', markdown: 'Markdown', pdf: 'PDF' };
+    const formatLabel = formatLabels[exportFormat] || exportFormat.toUpperCase();
     const packagingLabel = exportPackaging === 'zip' ? 'ZIP Archive' : 'Single File';
-    const ext = exportPackaging === 'zip' ? '.zip' : (exportFormat === 'json' ? '.json' : '.md');
+    const extMap = { json: '.json', markdown: '.md', pdf: '.pdf' };
+    const ext = exportPackaging === 'zip' ? '.zip' : (extMap[exportFormat] || '.bin');
+    const summaryLabel = includeSummaryIntro ? 'Enabled' : 'Disabled';
+    const summaryModelLabel = includeSummaryIntro ? (summaryModelDeployment || 'Configured default') : '—';
 
     let conversationsList = '';
     exportConversationIds.forEach(id => {
@@ -328,6 +400,14 @@ function _renderDownloadStep(container) {
                     <div class="col-5 text-muted small">Packaging:</div>
                     <div class="col-7 fw-semibold small">${packagingLabel}</div>
                 </div>
+                <div class="row mb-2">
+                    <div class="col-5 text-muted small">Intro summary:</div>
+                    <div class="col-7 fw-semibold small">${summaryLabel}</div>
+                </div>
+                <div class="row mb-2">
+                    <div class="col-5 text-muted small">Summary model:</div>
+                    <div class="col-7 fw-semibold small">${_escapeHtml(summaryModelLabel)}</div>
+                </div>
                 <div class="row">
                     <div class="col-5 text-muted small">File type:</div>
                     <div class="col-7 fw-semibold small">${ext}</div>
@@ -364,6 +444,7 @@ function _updateStepIndicators() {
         steps = [
             { label: 'Format', icon: 'bi-filetype-json' },
             { label: 'Packaging', icon: 'bi-box' },
+            { label: 'Summary', icon: 'bi-card-text' },
             { label: 'Download', icon: 'bi-download' }
         ];
     } else {
@@ -371,6 +452,7 @@ function _updateStepIndicators() {
             { label: 'Select', icon: 'bi-list-check' },
             { label: 'Format', icon: 'bi-filetype-json' },
             { label: 'Packaging', icon: 'bi-box' },
+            { label: 'Summary', icon: 'bi-card-text' },
             { label: 'Download', icon: 'bi-download' }
         ];
     }
@@ -448,7 +530,9 @@ async function _executeExport() {
             body: JSON.stringify({
                 conversation_ids: exportConversationIds,
                 format: exportFormat,
-                packaging: exportPackaging
+                packaging: exportPackaging,
+                include_summary_intro: includeSummaryIntro,
+                summary_model_deployment: includeSummaryIntro ? summaryModelDeployment : null
             })
         });
 
@@ -460,7 +544,8 @@ async function _executeExport() {
         // Get filename from Content-Disposition header
         const disposition = response.headers.get('Content-Disposition') || '';
         const filenameMatch = disposition.match(/filename="?([^"]+)"?/);
-        const filename = filenameMatch ? filenameMatch[1] : `conversations_export.${exportPackaging === 'zip' ? 'zip' : (exportFormat === 'json' ? 'json' : 'md')}`;
+        const fallbackExtMap = { json: 'json', markdown: 'md', pdf: 'pdf' };
+        const filename = filenameMatch ? filenameMatch[1] : `conversations_export.${exportPackaging === 'zip' ? 'zip' : (fallbackExtMap[exportFormat] || 'bin')}`;
 
         // Download the blob
         const blob = await response.blob();
@@ -511,6 +596,15 @@ function _escapeHtml(text) {
     return div.innerHTML;
 }
 
+function _getDefaultSummaryModel() {
+    const mainModelSelect = getEl('model-select');
+    if (!mainModelSelect) {
+        return '';
+    }
+
+    return mainModelSelect.value || (mainModelSelect.options[0] ? mainModelSelect.options[0].value : '');
+}
+
 // --- Expose Globally ---
 window.chatExport = {
     openExportWizard
diff --git a/application/single_app/static/js/chat/chat-input-actions.js b/application/single_app/static/js/chat/chat-input-actions.js
index 77851319..3c8e69e0 100644
--- a/application/single_app/static/js/chat/chat-input-actions.js
+++ b/application/single_app/static/js/chat/chat-input-actions.js
@@ -127,11 +127,11 @@ export function fetchFileContent(conversationId, fileId) {
       hideLoadingIndicator();
 
       if (data.file_content && data.filename) {
-        showFileContentPopup(data.file_content, data.filename, data.is_table);
+        showFileContentPopup(data.file_content, data.filename, data.is_table, data.file_content_source, conversationId, fileId);
       } else if (data.error) {
         showToast(data.error, "danger");
       } else {
-        ashowToastlert("Unexpected response from server.", "danger");
+        showToast("Unexpected response from server.", "danger");
       }
     })
     .catch((error) => {
@@ -141,7 +141,7 @@ export function fetchFileContent(conversationId, fileId) {
     });
 }
 
-export function showFileContentPopup(fileContent, filename, isTable) {
+export function showFileContentPopup(fileContent, filename, isTable, fileContentSource, conversationId, fileId) {
   let modalContainer = document.getElementById("file-modal");
   if (!modalContainer) {
     modalContainer = document.createElement("div");
@@ -155,6 +155,7 @@ export function showFileContentPopup(fileContent, filename, isTable) {
         <div class="modal-content">
           <div class="modal-header">
             <h5 class="modal-title">Uploaded File: ${filename}</h5>
+            <div class="ms-auto me-2" id="file-modal-download-btn-container"></div>
             <button
               type="button"
               class="btn-close"
@@ -176,6 +177,16 @@ export function showFileContentPopup(fileContent, filename, isTable) {
     }
   }
 
+  // Add or remove download button for blob-stored files
+  const downloadBtnContainer = document.getElementById("file-modal-download-btn-container");
+  if (downloadBtnContainer) {
+    if (fileContentSource === 'blob' && conversationId && fileId) {
+      downloadBtnContainer.innerHTML = `<a href="/api/enhanced_citations/tabular?conversation_id=${encodeURIComponent(conversationId)}&file_id=${encodeURIComponent(fileId)}" class="btn btn-sm btn-outline-primary" download><i class="bi bi-download me-1"></i>Download Original</a>`;
+    } else {
+      downloadBtnContainer.innerHTML = '';
+    }
+  }
+
   const fileContentElement = document.getElementById("file-content");
   if (!fileContentElement) return;
 
@@ -409,7 +420,7 @@ if (fileInputEl) {
         if (!currentConversationId) {
           createNewConversation(() => {
             uploadFileToConversation(file);
-          });
+          }, { preserveSelections: true });
         } else {
           uploadFileToConversation(file);
         }
@@ -458,7 +469,7 @@ if (uploadBtn) {
       if (!currentConversationId) {
         createNewConversation(() => {
           uploadFileToConversation(file);
-        });
+        }, { preserveSelections: true });
       } else {
         uploadFileToConversation(file);
       }
diff --git a/application/single_app/static/js/chat/chat-loading-indicator.js b/application/single_app/static/js/chat/chat-loading-indicator.js
index c1ab20c8..b76ef0e1 100644
--- a/application/single_app/static/js/chat/chat-loading-indicator.js
+++ b/application/single_app/static/js/chat/chat-loading-indicator.js
@@ -39,12 +39,37 @@ export function showLoadingIndicatorInChatbox() {
     <div class="spinner-border text-primary" role="status">
       <span class="visually-hidden">AI is typing...</span>
     </div>
-    <span>AI is typing...</span>
+    <span id="loading-indicator-text">AI is typing...</span>
   `;
   chatbox.appendChild(loadingIndicator);
   chatbox.scrollTop = chatbox.scrollHeight;
 }
 
+/**
+ * Update the loading indicator text with a thought step.
+ * Called by the thought polling handler to replace "AI is typing..." dynamically.
+ * @param {string} text - The thought content to display.
+ * @param {string} iconClass - Bootstrap Icon class (e.g. 'bi-search').
+ */
+export function updateLoadingIndicatorText(text, iconClass) {
+  const textEl = document.getElementById("loading-indicator-text");
+  if (!textEl) return;
+
+  textEl.textContent = "";
+  if (iconClass) {
+    const icon = document.createElement("i");
+    icon.className = `bi ${iconClass} me-1`;
+    textEl.appendChild(icon);
+  }
+  textEl.appendChild(document.createTextNode(text));
+
+  // Scroll chatbox to keep indicator visible
+  const chatbox = document.getElementById("chatbox");
+  if (chatbox) {
+    chatbox.scrollTop = chatbox.scrollHeight;
+  }
+}
+
 export function hideLoadingIndicatorInChatbox() {
   const loadingIndicator = document.getElementById("loading-indicator");
   if (loadingIndicator) {
diff --git a/application/single_app/static/js/chat/chat-messages.js b/application/single_app/static/js/chat/chat-messages.js
index d4c54790..1957e5ab 100644
--- a/application/single_app/static/js/chat/chat-messages.js
+++ b/application/single_app/static/js/chat/chat-messages.js
@@ -20,6 +20,7 @@ import { saveUserSetting } from "./chat-layout.js";
 import { isStreamingEnabled, sendMessageWithStreaming } from "./chat-streaming.js";
 import { getCurrentReasoningEffort, isReasoningEffortEnabled } from './chat-reasoning.js';
 import { areAgentsEnabled } from './chat-agents.js';
+import { createThoughtsToggleHtml, attachThoughtsToggleListener, startThoughtPolling, stopThoughtPolling } from './chat-thoughts.js';
 
 // Conditionally import TTS if enabled
 let ttsModule = null;
@@ -372,9 +373,12 @@ function createCitationsHtml(
     hybridCitations.forEach((cite, index) => {
       const citationId =
         cite.citation_id || `${cite.chunk_id}_${cite.page_number || index}`; // Fallback ID
-      const displayText = `${escapeHtml(cite.file_name)}, Page ${
-        cite.page_number || "N/A"
-      }`;
+      const locationLabel = cite.location_label || (cite.sheet_name ? 'Sheet' : 'Page');
+      const locationValue = cite.location_value || cite.sheet_name || cite.page_number || 'N/A';
+      const displayText = `${escapeHtml(cite.file_name)}, ${escapeHtml(locationLabel)}: ${escapeHtml(locationValue)}`;
+      const sheetNameAttribute = cite.sheet_name
+        ? `data-sheet-name="${escapeHtml(cite.sheet_name)}"`
+        : '';
 
       // Check if this is a metadata citation
       const isMetadata = cite.metadata_type ? true : false;
@@ -385,6 +389,7 @@ function createCitationsHtml(
               <a href="#"
                  class="btn btn-sm citation-button hybrid-citation-link ${isMetadata ? 'metadata-citation' : ''}"
                  data-citation-id="${escapeHtml(citationId)}"
+                  ${sheetNameAttribute}
                  data-is-metadata="${isMetadata}"
                  data-metadata-type="${escapeHtml(metadataType)}"
                  data-metadata-content="${escapeHtml(metadataContent)}"
@@ -743,11 +748,13 @@ export function appendMessage(
 
     const metadataContainerId = `metadata-${messageId || Date.now()}`;
     const metadataContainerHtml = `<div class="metadata-container mt-2 pt-2 border-top" id="${metadataContainerId}" style="display: none;"><div class="text-muted">Loading metadata...</div></div>`;
-    
+
+    const thoughtsHtml = createThoughtsToggleHtml(messageId);
+
     const footerContentHtml = `<div class="message-footer d-flex justify-content-between align-items-center mt-2">
       <div class="d-flex align-items-center">${copyAndFeedbackHtml}</div>
       <div class="d-flex align-items-center"></div>
-      <div class="d-flex align-items-center gap-2">${citationToggleHtml}<button class="btn btn-sm btn-link text-muted metadata-info-btn" data-message-id="${messageId}" title="Show metadata" aria-expanded="false" aria-controls="${metadataContainerId}">
+      <div class="d-flex align-items-center gap-2">${thoughtsHtml.toggleHtml}${citationToggleHtml}<button class="btn btn-sm btn-link text-muted metadata-info-btn" data-message-id="${messageId}" title="Show metadata" aria-expanded="false" aria-controls="${metadataContainerId}">
         <i class="bi bi-info-circle"></i>
       </button></div>
     </div>`;
@@ -760,6 +767,7 @@ export function appendMessage(
                     <div class="message-sender">${senderLabel}</div>
                     ${mainMessageHtml}
                     ${citationContentContainerHtml}
+                    ${thoughtsHtml.containerHtml}
                     ${metadataContainerHtml}
                     ${footerContentHtml}
                 </div>
@@ -816,6 +824,9 @@ export function appendMessage(
         }
       });
     }
+
+    // Attach thoughts toggle listener
+    attachThoughtsToggleListener(messageDiv, messageId, currentConversationId);
     
     const maskBtn = messageDiv.querySelector(".mask-btn");
     if (maskBtn) {
@@ -1324,7 +1335,7 @@ export function sendMessage() {
   if (!currentConversationId) {
     createNewConversation(() => {
       actuallySendMessage(combinedMessage);
-    });
+    }, { preserveSelections: true });
   } else {
     actuallySendMessage(combinedMessage);
   }
@@ -1516,6 +1527,7 @@ export function actuallySendMessage(finalMessageToSend) {
   }
   
   // Regular non-streaming fetch
+  startThoughtPolling(currentConversationId);
   fetch("/api/chat", {
     method: "POST",
     headers: {
@@ -1547,6 +1559,7 @@ export function actuallySendMessage(finalMessageToSend) {
     })
     .then((data) => {
       // Only successful responses reach here
+      stopThoughtPolling();
       hideLoadingIndicatorInChatbox();
 
       console.log("--- Data received from /api/chat ---");
@@ -1688,6 +1701,7 @@ export function actuallySendMessage(finalMessageToSend) {
       }
     })
     .catch((error) => {
+      stopThoughtPolling();
       hideLoadingIndicatorInChatbox();
       console.error("Error sending message:", error);
 
diff --git a/application/single_app/static/js/chat/chat-onload.js b/application/single_app/static/js/chat/chat-onload.js
index 43e1eba3..9cf2b322 100644
--- a/application/single_app/static/js/chat/chat-onload.js
+++ b/application/single_app/static/js/chat/chat-onload.js
@@ -1,6 +1,6 @@
 // chat-onload.js
 
-import { loadConversations, selectConversation, ensureConversationPresent } from "./chat-conversations.js";
+import { loadConversations, selectConversation, ensureConversationPresent, createNewConversation } from "./chat-conversations.js";
 // Import handleDocumentSelectChange
 import { loadAllDocs, populateDocumentSelectScope, handleDocumentSelectChange, loadTagsForScope, filterDocumentsBySelectedTags, setScopeFromUrlParam } from "./chat-documents.js";
 import { getUrlParameter } from "./chat-utils.js"; // Assuming getUrlParameter is in chat-utils.js now
@@ -44,7 +44,7 @@ window.addEventListener('DOMContentLoaded', async () => {
   if (userInput && newConversationBtn) {
     userInput.addEventListener("focus", () => {
       if (!currentConversationId) {
-        newConversationBtn.click();
+                createNewConversation(null, { preserveSelections: true });
       }
     });
   }
@@ -55,7 +55,7 @@ window.addEventListener('DOMContentLoaded', async () => {
       if (!currentConversationId) {
         // Optionally prevent the default action if it does something immediately
         // event.preventDefault(); 
-        newConversationBtn.click();
+                createNewConversation(null, { preserveSelections: true });
 
         // (Optional) If you need the prompt UI to appear *after* the conversation is created,
         // you can open the prompt UI programmatically in a small setTimeout or callback.
@@ -69,7 +69,7 @@ window.addEventListener('DOMContentLoaded', async () => {
     fileBtn.addEventListener("click", (event) => {
       if (!currentConversationId) {
         // event.preventDefault(); // If file dialog should only open once conversation is created
-        newConversationBtn.click();
+                createNewConversation(null, { preserveSelections: true });
 
         // (Optional) If you want the file dialog to appear *after* the conversation is created,
         // do it in a short setTimeout or callback:
diff --git a/application/single_app/static/js/chat/chat-streaming.js b/application/single_app/static/js/chat/chat-streaming.js
index faf6f59e..d2b5b218 100644
--- a/application/single_app/static/js/chat/chat-streaming.js
+++ b/application/single_app/static/js/chat/chat-streaming.js
@@ -5,6 +5,7 @@ import { loadUserSettings, saveUserSetting } from './chat-layout.js';
 import { showToast } from './chat-toast.js';
 import { updateSidebarConversationTitle } from './chat-sidebar-conversations.js';
 import { applyScopeLock } from './chat-documents.js';
+import { handleStreamingThought } from './chat-thoughts.js';
 
 let streamingEnabled = false;
 let currentEventSource = null;
@@ -207,8 +208,11 @@ export function sendMessageWithStreaming(messageData, tempUserMessageId, current
                                 handleStreamError(tempAiMessageId, data.partial_content || accumulatedContent, data.error);
                                 return;
                             }
-                            
-                            if (data.content) {
+
+                            if (data.type === 'thought') {
+                                handleStreamingThought(data);
+                                // Continue reading — don't fall through to content handling
+                            } else if (data.content) {
                                 // Append chunk to accumulated content
                                 accumulatedContent += data.content;
                                 updateStreamingMessage(tempAiMessageId, accumulatedContent);
diff --git a/application/single_app/static/js/chat/chat-thoughts.js b/application/single_app/static/js/chat/chat-thoughts.js
new file mode 100644
index 00000000..a780bd3f
--- /dev/null
+++ b/application/single_app/static/js/chat/chat-thoughts.js
@@ -0,0 +1,215 @@
+// chat-thoughts.js
+
+import { updateLoadingIndicatorText } from './chat-loading-indicator.js';
+import { escapeHtml } from './chat-utils.js';
+
+let thoughtPollingInterval = null;
+let lastSeenThoughtIndex = -1;
+
+// ---------------------------------------------------------------------------
+// Icon map: step_type → Bootstrap Icon class
+// ---------------------------------------------------------------------------
+function getThoughtIcon(stepType) {
+    const iconMap = {
+        'search': 'bi-search',
+        'tabular_analysis': 'bi-table',
+        'web_search': 'bi-globe',
+        'agent_tool_call': 'bi-robot',
+        'generation': 'bi-lightning',
+        'content_safety': 'bi-shield-check'
+    };
+    return iconMap[stepType] || 'bi-stars';
+}
+
+// ---------------------------------------------------------------------------
+// Polling (non-streaming mode)
+// ---------------------------------------------------------------------------
+
+/**
+ * Start polling for pending thoughts while waiting for a non-streaming response.
+ * @param {string} conversationId - The current conversation ID.
+ */
+export function startThoughtPolling(conversationId) {
+    if (!conversationId) return;
+    if (!window.appSettings?.enable_thoughts) return;
+
+    stopThoughtPolling(); // clear any previous interval
+    lastSeenThoughtIndex = -1;
+
+    thoughtPollingInterval = setInterval(() => {
+        fetch(`/api/conversations/${conversationId}/thoughts/pending`, {
+            credentials: 'same-origin'
+        })
+            .then(r => r.json())
+            .then(data => {
+                if (data.thoughts && data.thoughts.length > 0) {
+                    const latest = data.thoughts[data.thoughts.length - 1];
+                    if (latest.step_index > lastSeenThoughtIndex) {
+                        lastSeenThoughtIndex = latest.step_index;
+                        const icon = getThoughtIcon(latest.step_type);
+                        updateLoadingIndicatorText(latest.content, icon);
+                    }
+                }
+            })
+            .catch(() => { /* ignore polling errors */ });
+    }, 2000);
+}
+
+/**
+ * Stop the thought polling interval.
+ */
+export function stopThoughtPolling() {
+    if (thoughtPollingInterval) {
+        clearInterval(thoughtPollingInterval);
+        thoughtPollingInterval = null;
+    }
+    lastSeenThoughtIndex = -1;
+}
+
+// ---------------------------------------------------------------------------
+// Streaming handler
+// ---------------------------------------------------------------------------
+
+/**
+ * Handle a streaming thought event received via SSE.
+ * Updates the streaming message placeholder with a styled thought indicator.
+ * When actual content starts streaming, updateStreamingMessage() will overwrite this.
+ * @param {object} thoughtData - { step_index, step_type, content }
+ */
+export function handleStreamingThought(thoughtData) {
+    // Find the streaming message's content area
+    const messageElement = document.querySelector('[data-message-id^="temp_ai_"]');
+    if (!messageElement) return;
+
+    const contentElement = messageElement.querySelector('.message-text');
+    if (!contentElement) return;
+
+    const icon = getThoughtIcon(thoughtData.step_type);
+    // Replace entire content with styled thought indicator (visually distinct from AI response)
+    contentElement.innerHTML = `<div class="streaming-thought-display">
+        <span class="badge bg-info bg-opacity-10 text-info border border-info-subtle px-3 py-2 animate-pulse" style="font-size: 0.85rem; font-weight: 500;">
+            <i class="bi ${icon} me-2"></i>${escapeHtml(thoughtData.content)}
+        </span>
+    </div>`;
+}
+
+// ---------------------------------------------------------------------------
+// Per-message collapsible: toggle button + container HTML
+// ---------------------------------------------------------------------------
+
+/**
+ * Create HTML for the thoughts toggle button and hidden container.
+ * Returns an object with { toggleHtml, containerHtml }.
+ * @param {string} messageId
+ */
+export function createThoughtsToggleHtml(messageId) {
+    if (!window.appSettings?.enable_thoughts) {
+        return { toggleHtml: '', containerHtml: '' };
+    }
+
+    const containerId = `thoughts-${messageId || Date.now()}`;
+    const toggleHtml = `<button class="btn btn-sm btn-link text-muted thoughts-toggle-btn" title="Show processing thoughts" aria-expanded="false" aria-controls="${containerId}"><i class="bi bi-stars"></i></button>`;
+    const containerHtml = `<div id="${containerId}" class="thoughts-container d-none mt-2 pt-2 border-top"><div class="text-muted small">Loading thoughts...</div></div>`;
+
+    return { toggleHtml, containerHtml };
+}
+
+/**
+ * Attach event listener for the thoughts toggle button inside a message div.
+ * @param {HTMLElement} messageDiv
+ * @param {string} messageId
+ * @param {string} conversationId
+ */
+export function attachThoughtsToggleListener(messageDiv, messageId, conversationId) {
+    const toggleBtn = messageDiv.querySelector('.thoughts-toggle-btn');
+    if (!toggleBtn) return;
+
+    toggleBtn.addEventListener('click', () => {
+        const targetId = toggleBtn.getAttribute('aria-controls');
+        const container = messageDiv.querySelector(`#${targetId}`);
+        if (!container) return;
+
+        // Store scroll position
+        const scrollContainer = document.getElementById('chat-messages-container');
+        const currentScroll = scrollContainer?.scrollTop || window.pageYOffset;
+
+        const isExpanded = !container.classList.contains('d-none');
+        if (isExpanded) {
+            container.classList.add('d-none');
+            toggleBtn.setAttribute('aria-expanded', 'false');
+            toggleBtn.title = 'Show processing thoughts';
+            toggleBtn.innerHTML = '<i class="bi bi-stars"></i>';
+        } else {
+            container.classList.remove('d-none');
+            toggleBtn.setAttribute('aria-expanded', 'true');
+            toggleBtn.title = 'Hide processing thoughts';
+            toggleBtn.innerHTML = '<i class="bi bi-chevron-up"></i>';
+
+            // Lazy-load thoughts on first expand
+            if (container.innerHTML.includes('Loading thoughts')) {
+                loadThoughtsForMessage(conversationId, messageId, container);
+            }
+        }
+
+        // Restore scroll position
+        setTimeout(() => {
+            if (scrollContainer) {
+                scrollContainer.scrollTop = currentScroll;
+            } else {
+                window.scrollTo(0, currentScroll);
+            }
+        }, 10);
+    });
+}
+
+// ---------------------------------------------------------------------------
+// Fetch + render thoughts for a message
+// ---------------------------------------------------------------------------
+
+/**
+ * Fetch thoughts for a specific message from the API and render them.
+ * @param {string} conversationId
+ * @param {string} messageId
+ * @param {HTMLElement} container
+ */
+function loadThoughtsForMessage(conversationId, messageId, container) {
+    fetch(`/api/conversations/${conversationId}/messages/${messageId}/thoughts`, {
+        credentials: 'same-origin'
+    })
+        .then(r => r.json())
+        .then(data => {
+            if (!data.enabled) {
+                container.innerHTML = '<div class="text-muted small">Processing thoughts are disabled.</div>';
+                return;
+            }
+            if (!data.thoughts || data.thoughts.length === 0) {
+                container.innerHTML = '<div class="text-muted small">No processing thoughts recorded for this message.</div>';
+                return;
+            }
+            container.innerHTML = renderThoughtsList(data.thoughts);
+        })
+        .catch(err => {
+            console.error('Error loading thoughts:', err);
+            container.innerHTML = '<div class="text-danger small">Failed to load processing thoughts.</div>';
+        });
+}
+
+/**
+ * Render a list of thought steps as HTML.
+ * @param {Array} thoughts
+ * @returns {string} HTML string
+ */
+function renderThoughtsList(thoughts) {
+    let html = '<div class="thoughts-list">';
+    thoughts.forEach(t => {
+        const icon = getThoughtIcon(t.step_type);
+        const durationStr = t.duration_ms != null ? `<span class="text-muted ms-2">(${t.duration_ms}ms)</span>` : '';
+        html += `<div class="thought-step small py-1">
+            <i class="bi ${icon} me-2 text-muted"></i>
+            <span>${escapeHtml(t.content || '')}</span>
+            ${durationStr}
+        </div>`;
+    });
+    html += '</div>';
+    return html;
+}
diff --git a/application/single_app/static/js/plugin_common.js b/application/single_app/static/js/plugin_common.js
index e40158b9..29a88a24 100644
--- a/application/single_app/static/js/plugin_common.js
+++ b/application/single_app/static/js/plugin_common.js
@@ -2,6 +2,10 @@
 // Shared logic for admin_plugins.js and workspace_plugins.js
 // Exports: functions for modal field handling, validation, label toggling, table rendering, and plugin CRUD
 import { showToast } from "./chat/chat-toast.js"
+import {
+    humanizeName, truncateDescription,
+    openViewModal, createActionCard
+} from './workspace/view-utils.js';
 
 // Fetch merged plugin settings from backend given type and current settings
 export async function fetchAndMergePluginSettings(pluginType, currentSettings = {}) {
@@ -60,8 +64,7 @@ export function escapeHtml(str) {
 }
 
 // Render plugins table (parameterized for tbody selector and button handlers)
-export function renderPluginsTable({plugins, tbodySelector, onEdit, onDelete, ensureTable = true, isAdmin = false}) {
-  console.log('Rendering plugins table with %d plugins', plugins.length);
+export function renderPluginsTable({plugins, tbodySelector, onEdit, onDelete, onView, ensureTable = true, isAdmin = false}) {
   // Optionally ensure the table is present before rendering
   if (ensureTable) {
     ensurePluginsTableInRoot();
@@ -75,29 +78,33 @@ export function renderPluginsTable({plugins, tbodySelector, onEdit, onDelete, en
   plugins.forEach(plugin => {
     const tr = document.createElement('tr');
     const safeName = escapeHtml(plugin.name);
-    const safeDisplayName = escapeHtml(plugin.display_name || plugin.name);
-    const safeDesc = escapeHtml(plugin.description || 'No description available');
+    const displayName = humanizeName(plugin.display_name || plugin.name);
+    const safeDisplayName = escapeHtml(displayName);
+    const description = plugin.description || 'No description available';
+    const truncatedDesc = escapeHtml(truncateDescription(description, 90));
     let actionButtons = '';
     let globalBadge = plugin.is_global ? ' <span class="badge bg-info text-dark">Global</span>' : '';
     
-    // Show action buttons for:
-    // - Admin context: all actions (global and personal)
-    // - User context: only personal actions (not global)
+    // View button always shown
+    let viewButton = `<button type="button" class="btn btn-sm btn-outline-info view-plugin-btn me-1" data-plugin-name="${safeName}" title="View details">
+            <i class="bi bi-eye"></i>
+          </button>`;
+    
+    // Edit/Delete buttons based on context
+    let editDeleteButtons = '';
     if (isAdmin || !plugin.is_global) {
-      actionButtons = `
-        <div class="d-flex gap-1">
+      editDeleteButtons = `
           <button type="button" class="btn btn-sm btn-outline-secondary edit-plugin-btn" data-plugin-name="${safeName}" title="Edit action">
             <i class="bi bi-pencil"></i>
           </button>
           <button type="button" class="btn btn-sm btn-outline-danger delete-plugin-btn" data-plugin-name="${safeName}" title="Delete action">
             <i class="bi bi-trash"></i>
-          </button>
-        </div>
-      `;
+          </button>`;
     }
+    actionButtons = `<div class="d-flex gap-1">${viewButton}${editDeleteButtons}</div>`;
     tr.innerHTML = `
-      <td><strong>${safeDisplayName}</strong>${globalBadge}</td>
-      <td class="text-muted small">${safeDesc}</td>
+      <td><strong title="${escapeHtml(plugin.display_name || plugin.name || '')}">${safeDisplayName}</strong>${globalBadge}</td>
+      <td class="text-muted small" title="${escapeHtml(description)}">${truncatedDesc}</td>
       <td>${actionButtons}</td>
     `;
     tbody.appendChild(tr);
@@ -109,6 +116,34 @@ export function renderPluginsTable({plugins, tbodySelector, onEdit, onDelete, en
   tbody.querySelectorAll('.delete-plugin-btn').forEach(btn => {
     btn.onclick = () => onDelete(btn.getAttribute('data-plugin-name'));
   });
+  tbody.querySelectorAll('.view-plugin-btn').forEach(btn => {
+    btn.onclick = () => {
+      if (onView) {
+        onView(btn.getAttribute('data-plugin-name'));
+      }
+    };
+  });
+}
+
+// Render plugins grid (card-based view)
+export function renderPluginsGrid({plugins, containerSelector, onEdit, onDelete, onView, isAdmin = false}) {
+  const container = document.querySelector(containerSelector);
+  if (!container) return;
+  container.innerHTML = '';
+  if (!plugins.length) {
+    container.innerHTML = '<div class="col-12 text-center text-muted p-4">No actions found.</div>';
+    return;
+  }
+  plugins.forEach(plugin => {
+    const card = createActionCard(plugin, {
+      onView: (p) => { if (onView) onView(p.name); },
+      onEdit: (p) => onEdit(p.name),
+      onDelete: (p) => onDelete(p.name),
+      canManage: isAdmin || !plugin.is_global,
+      isAdmin
+    });
+    container.appendChild(card);
+  });
 }
 
 // Toggle auth fields and labels (parameterized for DOM elements)
diff --git a/application/single_app/static/js/plugin_modal_stepper.js b/application/single_app/static/js/plugin_modal_stepper.js
index 89076076..aa5b4e01 100644
--- a/application/single_app/static/js/plugin_modal_stepper.js
+++ b/application/single_app/static/js/plugin_modal_stepper.js
@@ -1,6 +1,10 @@
 // plugin_modal_stepper.js
 // Multi-step modal functionality for action/plugin creation
 import { showToast } from "./chat/chat-toast.js";
+import { getTypeIcon } from "./workspace/view-utils.js";
+
+// Action types hidden from the creation UI (backend plugins remain intact)
+const HIDDEN_ACTION_TYPES = ['sql_schema', 'ui_test', 'queue_storage', 'blob_storage', 'embedding_model'];
 
 export class PluginModalStepper {
   
@@ -129,6 +133,12 @@ export class PluginModalStepper {
     
     document.getElementById('sql-auth-type').addEventListener('change', () => this.handleSqlAuthTypeChange());
     
+    // Test SQL connection button
+    const testConnBtn = document.getElementById('sql-test-connection-btn');
+    if (testConnBtn) {
+      testConnBtn.addEventListener('click', () => this.testSqlConnection());
+    }
+    
     // Set up display name to generated name conversion
     this.setupNameGeneration();
     
@@ -193,6 +203,8 @@ export class PluginModalStepper {
       if (!res.ok) throw new Error('Failed to load action types');
       
       this.availableTypes = await res.json();
+      // Hide deprecated/internal action types from the creation UI
+      this.availableTypes = this.availableTypes.filter(t => !HIDDEN_ACTION_TYPES.includes(t.type));
       // Sort action types alphabetically by display name
       this.availableTypes.sort((a, b) => {
         const nameA = (a.display || a.displayName || a.type || a.name || '').toLowerCase();
@@ -271,10 +283,15 @@ export class PluginModalStepper {
       description.substring(0, maxLength) + '...' : description;
     const needsTruncation = description.length > maxLength;
     
+    const iconClass = getTypeIcon(type.type || type.name);
+
     col.innerHTML = `
       <div class="card action-type-card h-100" data-type="${type.type || type.name}">
         <div class="card-body">
-          <h6 class="card-title">${this.escapeHtml(displayName)}</h6>
+          <div class="d-flex align-items-center mb-2">
+            <i class="bi ${iconClass} me-2" style="font-size: 1.25rem; color: #0d6efd;"></i>
+            <h6 class="card-title mb-0">${this.escapeHtml(displayName)}</h6>
+          </div>
           <p class="card-text">
             <span class="description-short">${this.escapeHtml(truncatedDescription)}</span>
             ${needsTruncation ? `
@@ -538,43 +555,52 @@ export class PluginModalStepper {
     }
 
     if (stepNumber === 4) {
-      // Load additional settings schema for selected type
-      let options = {forceReload: true};
-      this.getAdditionalSettingsSchema(this.selectedType, options);
+      const isSqlType = this.selectedType === 'sql_query' || this.selectedType === 'sql_schema';
       const additionalFieldsDiv = document.getElementById('plugin-additional-fields-div');
-      if (additionalFieldsDiv) {
-        // Only clear and rebuild if type changes
-        if (this.selectedType !== this.lastAdditionalFieldsType) {
-          additionalFieldsDiv.innerHTML = '';
-          additionalFieldsDiv.classList.remove('d-none');
-          if (this.selectedType) {
-            this.getAdditionalSettingsSchema(this.selectedType)
-              .then(schema => {
-                if (schema) {
-                  this.buildAdditionalFieldsUI(schema, additionalFieldsDiv);
-                  try {
-                    if (this.isEditMode && this.originalPlugin && this.originalPlugin.additionalFields) {
-                      this.populateDynamicAdditionalFields(this.originalPlugin.additionalFields);
+
+      // For SQL types, hide additional fields entirely since Step 3 covers all SQL config
+      if (isSqlType && additionalFieldsDiv) {
+        additionalFieldsDiv.innerHTML = '';
+        additionalFieldsDiv.classList.add('d-none');
+        this.lastAdditionalFieldsType = this.selectedType;
+      } else {
+        // Load additional settings schema for selected type
+        let options = {forceReload: true};
+        this.getAdditionalSettingsSchema(this.selectedType, options);
+        if (additionalFieldsDiv) {
+          // Only clear and rebuild if type changes
+          if (this.selectedType !== this.lastAdditionalFieldsType) {
+            additionalFieldsDiv.innerHTML = '';
+            additionalFieldsDiv.classList.remove('d-none');
+            if (this.selectedType) {
+              this.getAdditionalSettingsSchema(this.selectedType)
+                .then(schema => {
+                  if (schema) {
+                    this.buildAdditionalFieldsUI(schema, additionalFieldsDiv);
+                    try {
+                      if (this.isEditMode && this.originalPlugin && this.originalPlugin.additionalFields) {
+                        this.populateDynamicAdditionalFields(this.originalPlugin.additionalFields);
+                      }
+                    } catch (error) {
+                      console.error('Error populating dynamic additional fields:', error);
                     }
-                  } catch (error) {
-                    console.error('Error populating dynamic additional fields:', error);
+                  } else {
+                    console.log('No additional settings schema found');
+                    additionalFieldsDiv.classList.add('d-none');
                   }
-                } else {
-                  console.log('No additional settings schema found');
+                })
+                .catch(error => {
+                  console.error(`Error fetching additional settings schema for type: ${this.selectedType} -- ${error}`);
                   additionalFieldsDiv.classList.add('d-none');
-                }
-              })
-              .catch(error => {
-                console.error(`Error fetching additional settings schema for type: ${this.selectedType} -- ${error}`);
-                additionalFieldsDiv.classList.add('d-none');
-              });
-          } else {
-            console.warn('No plugin type selected');
-            additionalFieldsDiv.classList.add('d-none');
+                });
+            } else {
+              console.warn('No plugin type selected');
+              additionalFieldsDiv.classList.add('d-none');
+            }
+            this.lastAdditionalFieldsType = this.selectedType;
           }
-          this.lastAdditionalFieldsType = this.selectedType;
+          // Otherwise, preserve user data and do not redraw
         }
-        // Otherwise, preserve user data and do not redraw
       }
 
       if (!this.isEditMode) {
@@ -1230,6 +1256,80 @@ export class PluginModalStepper {
     this.updateSqlAuthInfo();
   }
 
+  async testSqlConnection() {
+    const btn = document.getElementById('sql-test-connection-btn');
+    const resultDiv = document.getElementById('sql-test-connection-result');
+    const alertDiv = document.getElementById('sql-test-connection-alert');
+    if (!btn || !resultDiv || !alertDiv) return;
+
+    // Collect current SQL config from Step 3
+    const databaseType = document.querySelector('input[name="sql-database-type"]:checked')?.value;
+    const connectionMethod = document.querySelector('input[name="sql-connection-method"]:checked')?.value || 'parameters';
+    const authType = document.getElementById('sql-auth-type')?.value || 'username_password';
+
+    if (!databaseType) {
+      resultDiv.classList.remove('d-none');
+      alertDiv.className = 'alert alert-warning mb-0 py-2 px-3 small';
+      alertDiv.textContent = 'Please select a database type first.';
+      return;
+    }
+
+    const payload = {
+      database_type: databaseType,
+      connection_method: connectionMethod,
+      auth_type: authType
+    };
+
+    if (connectionMethod === 'connection_string') {
+      payload.connection_string = document.getElementById('sql-connection-string')?.value?.trim() || '';
+    } else {
+      payload.server = document.getElementById('sql-server')?.value?.trim() || '';
+      payload.database = document.getElementById('sql-database')?.value?.trim() || '';
+      payload.port = document.getElementById('sql-port')?.value?.trim() || '';
+      if (databaseType === 'sqlserver' || databaseType === 'azure_sql') {
+        payload.driver = document.getElementById('sql-driver')?.value || '';
+      }
+    }
+
+    if (authType === 'username_password') {
+      payload.username = document.getElementById('sql-username')?.value?.trim() || '';
+      payload.password = document.getElementById('sql-password')?.value?.trim() || '';
+    }
+
+    payload.timeout = parseInt(document.getElementById('sql-timeout')?.value) || 10;
+
+    // Show loading state
+    const originalText = btn.innerHTML;
+    btn.innerHTML = '<span class="spinner-border spinner-border-sm me-2" role="status" aria-hidden="true"></span>Testing...';
+    btn.disabled = true;
+    resultDiv.classList.add('d-none');
+
+    try {
+      const response = await fetch('/api/plugins/test-sql-connection', {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify(payload)
+      });
+      const data = await response.json();
+
+      resultDiv.classList.remove('d-none');
+      if (data.success) {
+        alertDiv.className = 'alert alert-success mb-0 py-2 px-3 small';
+        alertDiv.innerHTML = '<i class="bi bi-check-circle me-2"></i>' + (data.message || 'Connection successful!');
+      } else {
+        alertDiv.className = 'alert alert-danger mb-0 py-2 px-3 small';
+        alertDiv.innerHTML = '<i class="bi bi-x-circle me-2"></i>' + (data.error || 'Connection failed.');
+      }
+    } catch (error) {
+      resultDiv.classList.remove('d-none');
+      alertDiv.className = 'alert alert-danger mb-0 py-2 px-3 small';
+      alertDiv.innerHTML = '<i class="bi bi-x-circle me-2"></i>Test failed: ' + (error.message || 'Network error');
+    } finally {
+      btn.innerHTML = originalText;
+      btn.disabled = false;
+    }
+  }
+
   updateSqlConnectionExamples() {
     const selectedType = document.querySelector('input[name="sql-database-type"]:checked')?.value;
     const examplesDiv = document.getElementById('sql-connection-examples');
@@ -1720,12 +1820,17 @@ export class PluginModalStepper {
     
     // Collect additional fields from the dynamic UI and MERGE with existing additionalFields
     // This preserves OpenAPI spec content and other auto-populated fields
-    try {
-      const dynamicFields = this.collectAdditionalFields();
-      // Merge dynamicFields into additionalFields (preserving existing values)
-      additionalFields = { ...additionalFields, ...dynamicFields };
-    } catch (e) {
-      throw new Error('Invalid additional fields input');
+    // For SQL types, Step 3 already provides all necessary config — skip dynamic field merge
+    // to prevent empty Step 4 fields from overwriting populated Step 3 values
+    const isSqlType = this.selectedType === 'sql_query' || this.selectedType === 'sql_schema';
+    if (!isSqlType) {
+      try {
+        const dynamicFields = this.collectAdditionalFields();
+        // Merge dynamicFields into additionalFields (preserving existing values)
+        additionalFields = { ...additionalFields, ...dynamicFields };
+      } catch (e) {
+        throw new Error('Invalid additional fields input');
+      }
     }
     
     let metadata = {};
@@ -2106,6 +2211,7 @@ export class PluginModalStepper {
 
   populateAdvancedSummary() {
     const advancedSection = document.getElementById('summary-advanced-section');
+    const isSqlType = this.selectedType === 'sql_query' || this.selectedType === 'sql_schema';
     
     // Check if there's any metadata or additional fields
     const metadata = document.getElementById('plugin-metadata').value.trim();
@@ -2123,9 +2229,33 @@ export class PluginModalStepper {
       hasMetadata = metadata.length > 0 && metadata !== '{}';
     }
     
-    // DRY: Use private helper to collect additional fields
-    let additionalFieldsObj = this.collectAdditionalFields();
-    hasAdditionalFields = Object.keys(additionalFieldsObj).length > 0;
+    // For SQL types, additional fields are already shown in the SQL Database Configuration
+    // summary section, so skip showing them again in Advanced to avoid redundancy
+    if (!isSqlType) {
+      // DRY: Use private helper to collect additional fields
+      let additionalFieldsObj = this.collectAdditionalFields();
+      hasAdditionalFields = Object.keys(additionalFieldsObj).length > 0;
+
+      // Show/hide additional fields preview
+      const additionalFieldsPreview = document.getElementById('summary-additional-fields-preview');
+      if (hasAdditionalFields) {
+        let previewContent = '';
+        if (typeof additionalFieldsObj === 'object' && additionalFieldsObj !== null) {
+          previewContent = JSON.stringify(additionalFieldsObj, null, 2);
+        } else {
+          previewContent = '';
+        }
+        document.getElementById('summary-additional-fields-content').textContent = previewContent;
+        additionalFieldsPreview.style.display = '';
+      } else {
+        additionalFieldsPreview.style.display = 'none';
+      }
+    } else {
+      // Hide additional fields for SQL types
+      const additionalFieldsPreview = document.getElementById('summary-additional-fields-preview');
+      if (additionalFieldsPreview) additionalFieldsPreview.style.display = 'none';
+      hasAdditionalFields = false;
+    }
     
     // Update has metadata/additional fields indicators
     document.getElementById('summary-has-metadata').textContent = hasMetadata ? 'Yes' : 'No';
@@ -2140,21 +2270,6 @@ export class PluginModalStepper {
       metadataPreview.style.display = 'none';
     }
     
-    // Show/hide additional fields preview
-    const additionalFieldsPreview = document.getElementById('summary-additional-fields-preview');
-    if (hasAdditionalFields) {
-      let previewContent = '';
-      if (typeof additionalFieldsObj === 'object' && additionalFieldsObj !== null) {
-        previewContent = JSON.stringify(additionalFieldsObj, null, 2);
-      } else {
-        previewContent = '';
-      }
-      document.getElementById('summary-additional-fields-content').textContent = previewContent;
-      additionalFieldsPreview.style.display = '';
-    } else {
-      additionalFieldsPreview.style.display = 'none';
-    }
-    
     // Show advanced section if there's any advanced content
     if (hasMetadata || hasAdditionalFields) {
       advancedSection.style.display = '';
diff --git a/application/single_app/static/js/workspace/group_agents.js b/application/single_app/static/js/workspace/group_agents.js
index f97dbd07..608f029e 100644
--- a/application/single_app/static/js/workspace/group_agents.js
+++ b/application/single_app/static/js/workspace/group_agents.js
@@ -4,16 +4,23 @@
 import { showToast } from "../chat/chat-toast.js";
 import * as agentsCommon from "../agents_common.js";
 import { AgentModalStepper } from "../agent_modal_stepper.js";
+import {
+  humanizeName, truncateDescription, escapeHtml as escapeHtmlUtil,
+  setupViewToggle, switchViewContainers, openViewModal, createAgentCard
+} from './view-utils.js';
 
 const tableBody = document.getElementById("group-agents-table-body");
 const errorContainer = document.getElementById("group-agents-error");
 const searchInput = document.getElementById("group-agents-search");
 const createButton = document.getElementById("create-group-agent-btn");
 const permissionWarning = document.getElementById("group-agents-permission-warning");
+const agentsListView = document.getElementById("group-agents-list-view");
+const agentsGridView = document.getElementById("group-agents-grid-view");
 
 let agents = [];
 let filteredAgents = [];
 let agentStepper = null;
+let currentViewMode = 'list';
 let currentContext = window.groupWorkspaceContext || {
   activeGroupId: null,
   activeGroupName: "",
@@ -21,14 +28,7 @@ let currentContext = window.groupWorkspaceContext || {
 };
 
 function escapeHtml(value) {
-  if (!value) return "";
-  return value.replace(/[&<>"']/g, (char) => ({
-    "&": "&amp;",
-    "<": "&lt;",
-    ">": "&gt;",
-    '"': "&quot;",
-    "'": "&#39;"
-  }[char] || char));
+  return escapeHtmlUtil(value);
 }
 
 function canManageAgents() {
@@ -46,6 +46,7 @@ function groupAllowsModifications() {
 }
 
 function truncateName(name, maxLength = 18) {
+  // Kept for backward compat; prefer humanizeName for display
   if (!name || name.length <= maxLength) return name || "";
   return `${name.substring(0, maxLength)}…`;
 }
@@ -114,29 +115,61 @@ function renderAgentsTable(list) {
 
   list.forEach((agent) => {
     const tr = document.createElement("tr");
-    const displayName = truncateName(agent.display_name || agent.displayName || agent.name || "");
-    const description = escapeHtml(agent.description || "No description available.");
-
-    let actionsHtml = "<span class=\"text-muted small\">—</span>";
+    const rawName = agent.display_name || agent.displayName || agent.name || "";
+    const displayName = humanizeName(rawName);
+    const fullDesc = agent.description || "No description available.";
+    const shortDesc = truncateDescription(fullDesc, 90);
+
+    let actionsHtml = `
+        <button type="button" class="btn btn-sm btn-outline-info me-1 view-group-agent-btn" data-agent-name="${escapeHtml(agent.name || '')}" title="View details">
+          <i class="bi bi-eye"></i>
+        </button>
+        <button type="button" class="btn btn-sm btn-primary me-1 chat-group-agent-btn" data-agent-name="${escapeHtml(agent.name || '')}" title="Chat with this agent">
+          <i class="bi bi-chat-dots me-1"></i>Chat
+        </button>`;
     if (canManage) {
-      actionsHtml = `
-        <button type="button" class="btn btn-sm btn-outline-secondary me-1 edit-group-agent-btn" data-agent-id="${escapeHtml(agent.id || agent.name || "")}">
+      actionsHtml += `
+        <button type="button" class="btn btn-sm btn-outline-secondary me-1 edit-group-agent-btn" data-agent-id="${escapeHtml(agent.id || agent.name || '')}">
           <i class="bi bi-pencil"></i>
         </button>
-        <button type="button" class="btn btn-sm btn-outline-danger delete-group-agent-btn" data-agent-id="${escapeHtml(agent.id || agent.name || "")}">
+        <button type="button" class="btn btn-sm btn-outline-danger delete-group-agent-btn" data-agent-id="${escapeHtml(agent.id || agent.name || '')}">
           <i class="bi bi-trash"></i>
         </button>`;
     }
 
     tr.innerHTML = `
-      <td><strong title="${escapeHtml(agent.display_name || agent.displayName || agent.name || "")}">${escapeHtml(displayName)}</strong></td>
-      <td class="text-muted small">${description}</td>
+      <td><strong title="${escapeHtml(rawName)}">${escapeHtml(displayName)}</strong></td>
+      <td class="text-muted small" title="${escapeHtml(fullDesc)}">${escapeHtml(shortDesc)}</td>
       <td>${actionsHtml}</td>`;
 
     tableBody.appendChild(tr);
   });
 }
 
+function renderAgentsGrid(list) {
+  if (!agentsGridView) return;
+  agentsGridView.innerHTML = '';
+
+  if (!list.length) {
+    agentsGridView.innerHTML = '<div class="col-12 text-center text-muted p-4">No group agents found.</div>';
+    return;
+  }
+
+  const canManage = canManageAgents() && groupAllowsModifications();
+  list.forEach(agent => {
+    const col = createAgentCard(agent, {
+      onChat: a => chatWithGroupAgent(a.name || a),
+      onView: a => openGroupAgentViewModal(a),
+      onEdit: canManage ? a => {
+        const found = agents.find(x => x.id === (a.id || a.name || a) || x.name === (a.name || a));
+        openAgentModal(found || null);
+      } : null,
+      onDelete: canManage ? a => deleteGroupAgent(a.id || a.name || a) : null
+    });
+    agentsGridView.appendChild(col);
+  });
+}
+
 function filterAgents(term) {
   if (!term) {
     filteredAgents = agents.slice();
@@ -149,6 +182,23 @@ function filterAgents(term) {
     });
   }
   renderAgentsTable(filteredAgents);
+  renderAgentsGrid(filteredAgents);
+}
+
+// Open the view modal for a group agent with Chat/Edit/Delete actions
+function openGroupAgentViewModal(agent) {
+  const canManage = canManageAgents() && groupAllowsModifications();
+  const callbacks = {
+    onChat: (a) => chatWithGroupAgent(a.name)
+  };
+  if (canManage) {
+    callbacks.onEdit = (a) => {
+      const found = agents.find(x => x.id === a.id || x.name === a.name);
+      openAgentModal(found || a);
+    };
+    callbacks.onDelete = (a) => deleteGroupAgent(a.id || a.name);
+  }
+  openViewModal(agent, 'agent', callbacks);
 }
 
 function overrideAgentStepper(stepper) {
@@ -343,7 +393,57 @@ async function fetchGroupAgents() {
   }
 }
 
+async function chatWithGroupAgent(agentName) {
+  try {
+    const agent = agents.find(a => a.name === agentName);
+    if (!agent) {
+      throw new Error("Agent not found");
+    }
+
+    const payloadData = {
+      selected_agent: {
+        name: agentName,
+        display_name: agent.display_name || agent.displayName || agentName,
+        is_global: !!agent.is_global,
+        is_group: true,
+        group_id: currentContext.activeGroupId,
+        group_name: currentContext.activeGroupName
+      }
+    };
+
+    const resp = await fetch("/api/user/settings/selected_agent", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(payloadData)
+    });
+
+    if (!resp.ok) {
+      throw new Error("Failed to select agent");
+    }
+
+    window.location.href = "/chats";
+  } catch (err) {
+    console.error("Error selecting group agent for chat:", err);
+    showToast("Error selecting agent for chat. Please try again.", "danger");
+  }
+}
+
 function handleTableClick(event) {
+  const viewBtn = event.target.closest(".view-group-agent-btn");
+  if (viewBtn) {
+    const agentName = viewBtn.dataset.agentName;
+    const agent = agents.find(a => a.name === agentName);
+    if (agent) openGroupAgentViewModal(agent);
+    return;
+  }
+
+  const chatBtn = event.target.closest(".chat-group-agent-btn");
+  if (chatBtn) {
+    const agentName = chatBtn.dataset.agentName;
+    chatWithGroupAgent(agentName);
+    return;
+  }
+
   const editBtn = event.target.closest(".edit-group-agent-btn");
   if (editBtn) {
     const agentId = editBtn.dataset.agentId;
@@ -384,6 +484,11 @@ function initialize() {
   updatePermissionUI();
   bindEventHandlers();
 
+  setupViewToggle('groupAgents', 'groupAgentsViewPreference', (mode) => {
+    currentViewMode = mode;
+    switchViewContainers(mode, agentsListView, agentsGridView);
+  });
+
   if (document.getElementById("group-agents-tab-btn")?.classList.contains("active")) {
     fetchGroupAgents();
   }
diff --git a/application/single_app/static/js/workspace/group_plugins.js b/application/single_app/static/js/workspace/group_plugins.js
index 60a7f42e..8acdf5bd 100644
--- a/application/single_app/static/js/workspace/group_plugins.js
+++ b/application/single_app/static/js/workspace/group_plugins.js
@@ -3,6 +3,10 @@
 
 import { ensurePluginsTableInRoot, validatePluginManifest } from "../plugin_common.js";
 import { showToast } from "../chat/chat-toast.js";
+import {
+  humanizeName, truncateDescription, escapeHtml as escapeHtmlUtil,
+  setupViewToggle, switchViewContainers, openViewModal, createActionCard
+} from './view-utils.js';
 
 const root = document.getElementById("group-plugins-root");
 const permissionWarning = document.getElementById("group-plugins-permission-warning");
@@ -11,6 +15,7 @@ let plugins = [];
 let filteredPlugins = [];
 let templateReady = false;
 let listenersBound = false;
+let currentViewMode = 'list';
 let currentContext = window.groupWorkspaceContext || {
   activeGroupId: null,
   activeGroupName: "",
@@ -18,14 +23,7 @@ let currentContext = window.groupWorkspaceContext || {
 };
 
 function escapeHtml(value) {
-  if (!value) return "";
-  return value.replace(/[&<>"']/g, (char) => ({
-    "&": "&amp;",
-    "<": "&lt;",
-    ">": "&gt;",
-    '"': "&quot;",
-    "'": "&#39;"
-  }[char] || char));
+  return escapeHtmlUtil(value);
 }
 
 function canManagePlugins() {
@@ -66,6 +64,14 @@ function bindRootEvents() {
   });
 
   root.addEventListener("click", async (event) => {
+    const viewBtn = event.target.closest(".view-group-plugin-btn");
+    if (viewBtn) {
+      const pluginId = viewBtn.dataset.pluginId;
+      const plugin = plugins.find(x => x.id === pluginId || x.name === pluginId);
+      if (plugin) openGroupPluginViewModal(plugin);
+      return;
+    }
+
     const createBtn = event.target.closest("#create-group-plugin-btn");
     if (createBtn) {
       event.preventDefault();
@@ -148,23 +154,28 @@ function renderPluginsTable(list) {
   const canManage = canManagePlugins() && groupAllowsModifications();
   list.forEach((plugin) => {
     const tr = document.createElement("tr");
-    const displayName = plugin.displayName || plugin.display_name || plugin.name || "";
-    const description = plugin.description || "No description available.";
+    const rawName = plugin.displayName || plugin.display_name || plugin.name || "";
+    const displayName = humanizeName(rawName);
+    const fullDesc = plugin.description || "No description available.";
+    const shortDesc = truncateDescription(fullDesc, 90);
     const isGlobal = Boolean(plugin.is_global);
 
-    let actionsHtml = "<span class=\"text-muted small\">—</span>";
+    // View button always visible
+    let actionsHtml = `
+      <button type="button" class="btn btn-sm btn-outline-info me-1 view-group-plugin-btn" data-plugin-id="${escapeHtml(plugin.id || plugin.name || '')}" title="View details">
+        <i class="bi bi-eye"></i>
+      </button>`;
+
     if (canManage && !isGlobal) {
-      actionsHtml = `
-        <div class="d-flex gap-1">
-          <button type="button" class="btn btn-sm btn-outline-secondary edit-group-plugin-btn" data-plugin-id="${escapeHtml(plugin.id || plugin.name || "")}">
-            <i class="bi bi-pencil"></i>
-          </button>
-          <button type="button" class="btn btn-sm btn-outline-danger delete-group-plugin-btn" data-plugin-id="${escapeHtml(plugin.id || plugin.name || "")}">
-            <i class="bi bi-trash"></i>
-          </button>
-        </div>`;
+      actionsHtml += `
+        <button type="button" class="btn btn-sm btn-outline-secondary me-1 edit-group-plugin-btn" data-plugin-id="${escapeHtml(plugin.id || plugin.name || '')}">
+          <i class="bi bi-pencil"></i>
+        </button>
+        <button type="button" class="btn btn-sm btn-outline-danger delete-group-plugin-btn" data-plugin-id="${escapeHtml(plugin.id || plugin.name || '')}">
+          <i class="bi bi-trash"></i>
+        </button>`;
     } else if (canManage && isGlobal) {
-      actionsHtml = "<span class=\"text-muted small\">Managed globally</span>";
+      actionsHtml += `<span class="text-muted small ms-1">Managed globally</span>`;
     }
 
     const titleHtml = isGlobal
@@ -172,14 +183,36 @@ function renderPluginsTable(list) {
       : escapeHtml(displayName);
 
     tr.innerHTML = `
-      <td><strong title="${escapeHtml(displayName)}">${titleHtml}</strong></td>
-      <td class="text-muted small">${escapeHtml(description)}</td>
+      <td><strong title="${escapeHtml(rawName)}">${titleHtml}</strong></td>
+      <td class="text-muted small" title="${escapeHtml(fullDesc)}">${escapeHtml(shortDesc)}</td>
       <td>${actionsHtml}</td>`;
 
     tbody.appendChild(tr);
   });
 }
 
+function renderPluginsGrid(list) {
+  const gridView = document.getElementById('group-plugins-grid-view');
+  if (!gridView) return;
+  gridView.innerHTML = '';
+
+  if (!list.length) {
+    gridView.innerHTML = '<div class="col-12 text-center text-muted p-4">No group actions found.</div>';
+    return;
+  }
+
+  const canManage = canManagePlugins() && groupAllowsModifications();
+  list.forEach(plugin => {
+    const isGlobal = Boolean(plugin.is_global);
+    const col = createActionCard(plugin, {
+      onView: p => openGroupPluginViewModal(p),
+      onEdit: (canManage && !isGlobal) ? p => openPluginModal(p.id || p.name) : null,
+      onDelete: (canManage && !isGlobal) ? p => deleteGroupPlugin(p.id || p.name) : null
+    });
+    gridView.appendChild(col);
+  });
+}
+
 function filterPlugins(term) {
   if (!term) {
     filteredPlugins = plugins.slice();
@@ -192,6 +225,19 @@ function filterPlugins(term) {
     });
   }
   renderPluginsTable(filteredPlugins);
+  renderPluginsGrid(filteredPlugins);
+}
+
+// Open the view modal for a group action with Edit/Delete actions
+function openGroupPluginViewModal(plugin) {
+  const canManage = canManagePlugins() && groupAllowsModifications();
+  const isGlobal = Boolean(plugin.is_global);
+  const callbacks = {};
+  if (canManage && !isGlobal) {
+    callbacks.onEdit = (p) => openPluginModal(p.id || p.name);
+    callbacks.onDelete = (p) => deleteGroupPlugin(p.id || p.name);
+  }
+  openViewModal(plugin, 'action', callbacks);
 }
 
 async function fetchGroupPlugins() {
@@ -220,7 +266,17 @@ async function fetchGroupPlugins() {
     filteredPlugins = plugins.slice();
 
     renderPluginsTable(filteredPlugins);
+    renderPluginsGrid(filteredPlugins);
     updatePermissionUI();
+
+    // Set up view toggle (only once after template is in DOM)
+    setupViewToggle('groupPlugins', 'groupPluginsViewPreference', (mode) => {
+      currentViewMode = mode;
+      switchViewContainers(mode,
+        document.getElementById('group-plugins-list-view'),
+        document.getElementById('group-plugins-grid-view')
+      );
+    });
   } catch (error) {
     console.error("Error loading group actions:", error);
     renderError(error.message || "Unable to load group actions.");
diff --git a/application/single_app/static/js/workspace/view-utils.js b/application/single_app/static/js/workspace/view-utils.js
new file mode 100644
index 00000000..3b78bc15
--- /dev/null
+++ b/application/single_app/static/js/workspace/view-utils.js
@@ -0,0 +1,523 @@
+// view-utils.js
+// Shared utilities for list/grid view toggle, name humanization, and view modal
+// Used by personal and group agents/actions workspace modules
+
+/**
+ * Convert a technical name to a human-readable display name.
+ * Handles underscores, camelCase, PascalCase, and consecutive uppercase.
+ * Examples:
+ *   "sql_query" → "Sql Query"
+ *   "myAgentName" → "My Agent Name"
+ *   "OpenAPIPlugin" → "Open API Plugin"
+ *   "log_analytics" → "Log Analytics"
+ */
+export function humanizeName(name) {
+    if (!name) return "";
+    // Replace underscores and hyphens with spaces
+    let result = name.replace(/[_-]/g, " ");
+    // Insert space before uppercase letters that follow lowercase letters (camelCase)
+    result = result.replace(/([a-z])([A-Z])/g, "$1 $2");
+    // Insert space between consecutive uppercase followed by lowercase (e.g., "APIPlugin" → "API Plugin")
+    result = result.replace(/([A-Z]+)([A-Z][a-z])/g, "$1 $2");
+    // Capitalize first letter of each word
+    result = result.replace(/\b\w/g, (c) => c.toUpperCase());
+    // Collapse multiple spaces
+    result = result.replace(/\s+/g, " ").trim();
+    return result;
+}
+
+/**
+ * Truncate a description string to maxLen characters, appending "…" if truncated.
+ */
+export function truncateDescription(text, maxLen = 100) {
+    if (!text) return "";
+    if (text.length <= maxLen) return text;
+    return text.substring(0, maxLen).trimEnd() + "…";
+}
+
+/**
+ * Escape HTML entities to prevent XSS.
+ */
+export function escapeHtml(str) {
+    if (!str) return "";
+    return str.replace(/[&<>"']/g, (c) =>
+        ({ "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;" }[c])
+    );
+}
+
+/**
+ * Get an appropriate Bootstrap icon class for an action/plugin type.
+ */
+export function getTypeIcon(type) {
+    if (!type) return "bi-lightning-charge";
+    const t = type.toLowerCase();
+    if (t.includes("sql")) return "bi-database";
+    if (t.includes("openapi")) return "bi-globe";
+    if (t.includes("log_analytics")) return "bi-graph-up";
+    if (t.includes("msgraph")) return "bi-microsoft";
+    if (t.includes("databricks")) return "bi-bricks";
+    if (t.includes("http") || t.includes("smart_http")) return "bi-cloud-arrow-up";
+    if (t.includes("azure_function")) return "bi-lightning";
+    if (t.includes("blob")) return "bi-file-earmark";
+    if (t.includes("queue")) return "bi-inbox";
+    if (t.includes("embedding")) return "bi-vector-pen";
+    if (t.includes("fact_memory")) return "bi-brain";
+    if (t.includes("math")) return "bi-calculator";
+    if (t.includes("text")) return "bi-fonts";
+    if (t.includes("time")) return "bi-clock";
+    return "bi-lightning-charge";
+}
+
+/**
+ * Create the HTML string for a list/grid view toggle button group.
+ * @param {string} prefix - Unique prefix for element IDs (e.g., "agents", "plugins", "group-agents")
+ * @returns {string} HTML string
+ */
+export function createViewToggleHtml(prefix) {
+    return `
+        <div class="btn-group btn-group-sm" role="group" aria-label="View mode">
+            <input type="radio" class="btn-check" name="${prefix}-view-mode" id="${prefix}-view-list" autocomplete="off" checked>
+            <label class="btn btn-outline-secondary" for="${prefix}-view-list">
+                <i class="bi bi-list-ul"></i>
+            </label>
+            <input type="radio" class="btn-check" name="${prefix}-view-mode" id="${prefix}-view-grid" autocomplete="off">
+            <label class="btn btn-outline-secondary" for="${prefix}-view-grid">
+                <i class="bi bi-grid-3x3-gap"></i>
+            </label>
+        </div>`;
+}
+
+/**
+ * Set up view toggle event listeners and restore saved preference.
+ * @param {string} prefix - Unique prefix matching createViewToggleHtml
+ * @param {string} storageKey - localStorage key for persistence
+ * @param {function} onSwitch - Callback receiving 'list' or 'grid'
+ */
+export function setupViewToggle(prefix, storageKey, onSwitch) {
+    const listRadio = document.getElementById(`${prefix}-view-list`);
+    const gridRadio = document.getElementById(`${prefix}-view-grid`);
+    if (!listRadio || !gridRadio) return;
+
+    listRadio.addEventListener("change", () => {
+        if (listRadio.checked) {
+            localStorage.setItem(storageKey, "list");
+            onSwitch("list");
+        }
+    });
+
+    gridRadio.addEventListener("change", () => {
+        if (gridRadio.checked) {
+            localStorage.setItem(storageKey, "grid");
+            onSwitch("grid");
+        }
+    });
+
+    // Restore saved preference
+    const saved = localStorage.getItem(storageKey);
+    if (saved === "grid") {
+        gridRadio.checked = true;
+        listRadio.checked = false;
+        onSwitch("grid");
+    } else {
+        onSwitch("list");
+    }
+}
+
+/**
+ * Toggle visibility of list and grid containers.
+ * @param {string} mode - 'list' or 'grid'
+ * @param {HTMLElement} listContainer - The list/table container element
+ * @param {HTMLElement} gridContainer - The grid container element
+ */
+export function switchViewContainers(mode, listContainer, gridContainer) {
+    if (listContainer) {
+        listContainer.classList.toggle("d-none", mode !== "list");
+    }
+    if (gridContainer) {
+        gridContainer.classList.toggle("d-none", mode !== "grid");
+    }
+}
+
+// ============================================================================
+// VIEW MODAL — Lightweight read-only detail view
+// ============================================================================
+
+/**
+ * Open a read-only view modal for an agent or action.
+ * @param {object} item - The agent or action data object
+ * @param {'agent'|'action'} type - What kind of item this is
+ * @param {object} [callbacks] - Optional action callbacks { onChat, onEdit, onDelete }
+ */
+export function openViewModal(item, type, callbacks = {}) {
+    const modalEl = document.getElementById("item-view-modal");
+    if (!modalEl) return;
+
+    const titleEl = modalEl.querySelector(".modal-title");
+    const bodyEl = modalEl.querySelector(".modal-body");
+    const footerEl = modalEl.querySelector(".modal-footer");
+    if (!titleEl || !bodyEl || !footerEl) return;
+
+    if (type === "agent") {
+        titleEl.textContent = "Agent Details";
+        bodyEl.innerHTML = buildAgentViewHtml(item);
+    } else {
+        titleEl.textContent = "Action Details";
+        bodyEl.innerHTML = buildActionViewHtml(item);
+    }
+
+    // Build footer buttons dynamically
+    footerEl.innerHTML = '';
+    const { onChat, onEdit, onDelete } = callbacks;
+
+    if (onChat && typeof onChat === 'function') {
+        const chatBtn = document.createElement('button');
+        chatBtn.type = 'button';
+        chatBtn.className = 'btn btn-primary';
+        chatBtn.innerHTML = '<i class="bi bi-chat-dots me-1"></i>Chat';
+        chatBtn.addEventListener('click', () => {
+            bootstrap.Modal.getInstance(modalEl)?.hide();
+            onChat(item);
+        });
+        footerEl.appendChild(chatBtn);
+    }
+
+    if (onEdit && typeof onEdit === 'function') {
+        const editBtn = document.createElement('button');
+        editBtn.type = 'button';
+        editBtn.className = 'btn btn-outline-secondary';
+        editBtn.innerHTML = '<i class="bi bi-pencil me-1"></i>Edit';
+        editBtn.addEventListener('click', () => {
+            bootstrap.Modal.getInstance(modalEl)?.hide();
+            onEdit(item);
+        });
+        footerEl.appendChild(editBtn);
+    }
+
+    if (onDelete && typeof onDelete === 'function') {
+        const delBtn = document.createElement('button');
+        delBtn.type = 'button';
+        delBtn.className = 'btn btn-outline-danger';
+        delBtn.innerHTML = '<i class="bi bi-trash me-1"></i>Delete';
+        delBtn.addEventListener('click', () => {
+            bootstrap.Modal.getInstance(modalEl)?.hide();
+            onDelete(item);
+        });
+        footerEl.appendChild(delBtn);
+    }
+
+    const closeBtn = document.createElement('button');
+    closeBtn.type = 'button';
+    closeBtn.className = 'btn btn-secondary';
+    closeBtn.textContent = 'Close';
+    closeBtn.setAttribute('data-bs-dismiss', 'modal');
+    footerEl.appendChild(closeBtn);
+
+    const modal = new bootstrap.Modal(modalEl);
+    modal.show();
+}
+
+function buildAgentViewHtml(agent) {
+    const displayName = escapeHtml(agent.display_name || agent.displayName || agent.name || "");
+    const name = escapeHtml(agent.name || "");
+    const description = escapeHtml(agent.description || "No description available.");
+    const model = escapeHtml(agent.azure_openai_gpt_deployment || agent.model || "Default");
+    const agentType = agent.agent_type === "aifoundry" ? "Azure AI Foundry" : "Local (Semantic Kernel)";
+    const rawInstructions = agent.instructions || "No instructions defined.";
+    // Render instructions as Markdown (marked + DOMPurify are loaded globally in base.html)
+    const renderedInstructions = (typeof marked !== 'undefined' && typeof DOMPurify !== 'undefined')
+        ? DOMPurify.sanitize(marked.parse(rawInstructions))
+        : escapeHtml(rawInstructions);
+    const isGlobal = agent.is_global;
+    const scopeBadge = isGlobal
+        ? '<span class="badge bg-info text-dark">Global</span>'
+        : '<span class="badge bg-secondary">Personal</span>';
+
+    return `
+        <div class="card mb-3 border-0 shadow-sm">
+            <div class="card-header text-white py-2" style="background: linear-gradient(135deg, #007bff 0%, #0056b3 100%);">
+                <i class="bi bi-info-circle me-2"></i><strong>Basic Information</strong>
+            </div>
+            <div class="card-body">
+                <div class="row g-3">
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Display Name</label>
+                        <span class="fw-medium">${displayName}</span>
+                    </div>
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Generated Name</label>
+                        <span class="fw-medium font-monospace">${name}</span>
+                    </div>
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Scope</label>
+                        ${scopeBadge}
+                    </div>
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Agent Type</label>
+                        <span class="badge bg-info text-dark">${escapeHtml(agentType)}</span>
+                    </div>
+                    <div class="col-12">
+                        <label class="text-muted small mb-1 d-block">Description</label>
+                        <span class="fw-medium">${description}</span>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <div class="card mb-3 border-0 shadow-sm">
+            <div class="card-header text-white py-2" style="background: linear-gradient(135deg, #ffc107 0%, #e0a800 100%);">
+                <i class="bi bi-gear me-2"></i><strong>Model Configuration</strong>
+            </div>
+            <div class="card-body">
+                <div class="row g-3">
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Model / Deployment</label>
+                        <span class="fw-medium font-monospace">${model}</span>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <div class="card mb-3 border-0 shadow-sm">
+            <div class="card-header text-white py-2" style="background: linear-gradient(135deg, #28a745 0%, #1e7e34 100%);">
+                <i class="bi bi-file-text me-2"></i><strong>Instructions</strong>
+            </div>
+            <div class="card-body">
+                <div class="p-3 bg-light border rounded rendered-markdown" style="max-height: 300px; overflow-y: auto; font-size: 0.9rem;">
+${renderedInstructions}
+                </div>
+            </div>
+        </div>`;
+}
+
+function buildActionViewHtml(action) {
+    const displayName = escapeHtml(action.display_name || action.displayName || action.name || "");
+    const name = escapeHtml(action.name || "");
+    const description = escapeHtml(action.description || "No description available.");
+    const type = escapeHtml(action.type || "unknown");
+    const typeIcon = getTypeIcon(action.type);
+    const authType = escapeHtml(formatAuthType(action.auth?.type || action.auth_type || ""));
+    const endpoint = escapeHtml(action.endpoint || action.base_url || "");
+    const isGlobal = action.is_global;
+    const scopeBadge = isGlobal
+        ? '<span class="badge bg-info text-dark">Global</span>'
+        : '<span class="badge bg-secondary">Personal</span>';
+
+    let configHtml = "";
+    if (endpoint) {
+        configHtml = `
+        <div class="card mb-3 border-0 shadow-sm">
+            <div class="card-header text-white py-2" style="background: linear-gradient(135deg, #28a745 0%, #1e7e34 100%);">
+                <i class="bi bi-gear me-2"></i><strong>Configuration</strong>
+            </div>
+            <div class="card-body">
+                <div class="row g-3">
+                    <div class="col-12">
+                        <label class="text-muted small mb-1 d-block">Endpoint</label>
+                        <span class="fw-medium font-monospace text-break">${endpoint}</span>
+                    </div>
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Authentication</label>
+                        <span class="fw-medium">${authType || "None"}</span>
+                    </div>
+                </div>
+            </div>
+        </div>`;
+    }
+
+    return `
+        <div class="card mb-3 border-0 shadow-sm">
+            <div class="card-header text-white py-2" style="background: linear-gradient(135deg, #007bff 0%, #0056b3 100%);">
+                <i class="bi bi-info-circle me-2"></i><strong>Basic Information</strong>
+            </div>
+            <div class="card-body">
+                <div class="row g-3">
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Display Name</label>
+                        <span class="fw-medium">${displayName}</span>
+                    </div>
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Generated Name</label>
+                        <span class="fw-medium font-monospace">${name}</span>
+                    </div>
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Type</label>
+                        <span class="fw-medium"><i class="bi ${typeIcon} me-1"></i>${humanizeName(type)}</span>
+                    </div>
+                    <div class="col-md-6">
+                        <label class="text-muted small mb-1 d-block">Scope</label>
+                        ${scopeBadge}
+                    </div>
+                    <div class="col-12">
+                        <label class="text-muted small mb-1 d-block">Description</label>
+                        <span class="fw-medium">${description}</span>
+                    </div>
+                </div>
+            </div>
+        </div>
+        ${configHtml}`;
+}
+
+function formatAuthType(type) {
+    if (!type) return "";
+    const map = {
+        "key": "API Key",
+        "identity": "Managed Identity",
+        "user": "User (Delegated)",
+        "servicePrincipal": "Service Principal",
+        "connection_string": "Connection String",
+        "basic": "Basic Auth",
+        "username_password": "Username / Password",
+        "NoAuth": "No Authentication"
+    };
+    return map[type] || type;
+}
+
+// ============================================================================
+// GRID CARD RENDERERS
+// ============================================================================
+
+/**
+ * Create a grid card element for an agent.
+ * @param {object} agent - Agent data object
+ * @param {object} options - { onChat, onView, onEdit, onDelete, canManage, isGroup }
+ * @returns {HTMLElement}
+ */
+export function createAgentCard(agent, options = {}) {
+    const { onChat, onView, onEdit, onDelete, canManage = false, isGroup = false } = options;
+    const col = document.createElement("div");
+    col.className = "col-sm-6 col-md-4 col-lg-3";
+
+    const displayName = humanizeName(agent.display_name || agent.displayName || agent.name || "");
+    const description = agent.description || "No description available.";
+    const isGlobal = agent.is_global;
+
+    let badgeHtml = "";
+    if (isGlobal) {
+        badgeHtml = '<span class="badge bg-info text-dark ms-1" style="font-size: 0.65rem;">Global</span>';
+    }
+
+    let buttonsHtml = `
+        <button class="btn btn-sm btn-primary item-card-chat-btn me-1" title="Chat with this agent">
+            <i class="bi bi-chat-dots me-1"></i>Chat
+        </button>
+        <button class="btn btn-sm btn-outline-info item-card-view-btn me-1" title="View details">
+            <i class="bi bi-eye"></i>
+        </button>`;
+
+    if (canManage && !isGlobal) {
+        buttonsHtml += `
+        <button class="btn btn-sm btn-outline-secondary item-card-edit-btn me-1" title="Edit">
+            <i class="bi bi-pencil"></i>
+        </button>
+        <button class="btn btn-sm btn-outline-danger item-card-delete-btn" title="Delete">
+            <i class="bi bi-trash"></i>
+        </button>`;
+    }
+
+    col.innerHTML = `
+        <div class="card item-card h-100">
+            <div class="card-body d-flex flex-column">
+                <div class="item-card-icon mb-2">
+                    <i class="bi bi-robot" style="font-size: 1.75rem;"></i>
+                </div>
+                <h6 class="card-title mb-1">${escapeHtml(displayName)}${badgeHtml}</h6>
+                <p class="card-text small text-muted flex-grow-1">${escapeHtml(truncateDescription(description, 120))}</p>
+                <div class="item-card-buttons mt-2 d-flex flex-wrap gap-1">
+                    ${buttonsHtml}
+                </div>
+            </div>
+        </div>`;
+
+    // Bind button events
+    const chatBtn = col.querySelector(".item-card-chat-btn");
+    const viewBtn = col.querySelector(".item-card-view-btn");
+    const editBtn = col.querySelector(".item-card-edit-btn");
+    const deleteBtn = col.querySelector(".item-card-delete-btn");
+
+    if (chatBtn && onChat) chatBtn.addEventListener("click", (e) => { e.stopPropagation(); onChat(agent); });
+    if (viewBtn && onView) viewBtn.addEventListener("click", (e) => { e.stopPropagation(); onView(agent); });
+    if (editBtn && onEdit) editBtn.addEventListener("click", (e) => { e.stopPropagation(); onEdit(agent); });
+    if (deleteBtn && onDelete) deleteBtn.addEventListener("click", (e) => { e.stopPropagation(); onDelete(agent); });
+
+    // Clicking anywhere on the card opens the detail view
+    const cardEl = col.querySelector(".item-card");
+    if (cardEl && onView) {
+        cardEl.style.cursor = "pointer";
+        cardEl.addEventListener("click", () => onView(agent));
+    }
+
+    return col;
+}
+
+/**
+ * Create a grid card element for an action/plugin.
+ * @param {object} plugin - Action/plugin data object
+ * @param {object} options - { onView, onEdit, onDelete, canManage, isAdmin }
+ * @returns {HTMLElement}
+ */
+export function createActionCard(plugin, options = {}) {
+    const { onView, onEdit, onDelete, canManage = true, isAdmin = false } = options;
+    const col = document.createElement("div");
+    col.className = "col-sm-6 col-md-4 col-lg-3";
+
+    const displayName = humanizeName(plugin.display_name || plugin.displayName || plugin.name || "");
+    const description = plugin.description || "No description available.";
+    const type = plugin.type || "";
+    const typeIcon = getTypeIcon(type);
+    const isGlobal = plugin.is_global;
+
+    let badgeHtml = "";
+    if (isGlobal) {
+        badgeHtml = '<span class="badge bg-info text-dark ms-1" style="font-size: 0.65rem;">Global</span>';
+    }
+
+    const typeBadge = type
+        ? `<span class="badge bg-light text-dark border me-1" style="font-size: 0.65rem;"><i class="bi ${typeIcon} me-1"></i>${escapeHtml(humanizeName(type))}</span>`
+        : "";
+
+    let buttonsHtml = `
+        <button class="btn btn-sm btn-outline-info item-card-view-btn me-1" title="View details">
+            <i class="bi bi-eye"></i>
+        </button>`;
+
+    if ((isAdmin || (canManage && !isGlobal))) {
+        buttonsHtml += `
+        <button class="btn btn-sm btn-outline-secondary item-card-edit-btn me-1" title="Edit">
+            <i class="bi bi-pencil"></i>
+        </button>
+        <button class="btn btn-sm btn-outline-danger item-card-delete-btn" title="Delete">
+            <i class="bi bi-trash"></i>
+        </button>`;
+    }
+
+    col.innerHTML = `
+        <div class="card item-card h-100">
+            <div class="card-body d-flex flex-column">
+                <div class="item-card-icon mb-2">
+                    <i class="bi ${typeIcon}" style="font-size: 1.75rem;"></i>
+                </div>
+                <h6 class="card-title mb-1">${escapeHtml(displayName)}${badgeHtml}</h6>
+                <div class="mb-2">${typeBadge}</div>
+                <p class="card-text small text-muted flex-grow-1">${escapeHtml(truncateDescription(description, 120))}</p>
+                <div class="item-card-buttons mt-2 d-flex flex-wrap gap-1">
+                    ${buttonsHtml}
+                </div>
+            </div>
+        </div>`;
+
+    // Bind button events
+    const viewBtn = col.querySelector(".item-card-view-btn");
+    const editBtn = col.querySelector(".item-card-edit-btn");
+    const deleteBtn = col.querySelector(".item-card-delete-btn");
+
+    if (viewBtn && onView) viewBtn.addEventListener("click", (e) => { e.stopPropagation(); onView(plugin); });
+    if (editBtn && onEdit) editBtn.addEventListener("click", (e) => { e.stopPropagation(); onEdit(plugin); });
+    if (deleteBtn && onDelete) deleteBtn.addEventListener("click", (e) => { e.stopPropagation(); onDelete(plugin); });
+
+    // Clicking anywhere on the card opens the detail view
+    const cardEl = col.querySelector(".item-card");
+    if (cardEl && onView) {
+        cardEl.style.cursor = "pointer";
+        cardEl.addEventListener("click", () => onView(plugin));
+    }
+
+    return col;
+}
diff --git a/application/single_app/static/js/workspace/workspace_agents.js b/application/single_app/static/js/workspace/workspace_agents.js
index a0839b25..623be234 100644
--- a/application/single_app/static/js/workspace/workspace_agents.js
+++ b/application/single_app/static/js/workspace/workspace_agents.js
@@ -4,14 +4,22 @@
 import { showToast } from "../chat/chat-toast.js";
 import * as agentsCommon from '../agents_common.js';
 import { AgentModalStepper } from '../agent_modal_stepper.js';
+import {
+    humanizeName, truncateDescription, escapeHtml,
+    setupViewToggle, switchViewContainers,
+    openViewModal, createAgentCard
+} from './view-utils.js';
 
 // --- DOM Elements & Globals ---
 const agentsTbody = document.getElementById('agents-table-body');
 const agentsErrorDiv = document.getElementById('workspace-agents-error');
 const createAgentBtn = document.getElementById('create-agent-btn');
 const agentsSearchInput = document.getElementById('agents-search');
+const agentsListView = document.getElementById('agents-list-view');
+const agentsGridView = document.getElementById('agents-grid-view');
 let agents = [];
 let filteredAgents = [];
+let currentViewMode = 'list';
 
 
 // --- Function Definitions ---
@@ -43,104 +51,87 @@ function filterAgents(searchTerm) {
     });
   }
   renderAgentsTable(filteredAgents);
+  renderAgentsGrid(filteredAgents);
 }
 
-// --- Helper Functions ---
-
-function truncateDisplayName(displayName, maxLength = 12) {
-  if (!displayName || displayName.length <= maxLength) {
-    return displayName;
+// Open the view modal for an agent with Chat/Edit/Delete actions in the footer
+function openAgentViewModal(agent) {
+  const callbacks = {
+    onChat: (a) => chatWithAgent(a.name),
+    onDelete: !agent.is_global ? (a) => { if (confirm(`Delete agent '${a.name}'?`)) deleteAgent(a.name); } : null
+  };
+  if (!agent.is_global) {
+    callbacks.onEdit = (a) => openAgentModal(a);
   }
-  return displayName.substring(0, maxLength) + '...';
+  openViewModal(agent, 'agent', callbacks);
 }
 
+// --- Rendering Functions ---
 function renderAgentsTable(agentsList) {
   if (!agentsTbody) return;
   agentsTbody.innerHTML = '';
   if (!agentsList.length) {
     const tr = document.createElement('tr');
-    tr.innerHTML = '<td colspan="4" class="text-center text-muted">No agents found.</td>';
+    tr.innerHTML = '<td colspan="3" class="text-center text-muted">No agents found.</td>';
     agentsTbody.appendChild(tr);
     return;
   }
-  // Fetch selected_agent from user settings (async)
-  fetch('/api/user/settings').then(res => {
-    if (!res.ok) throw new Error('Failed to load user settings');
-    return res.json();
-  }).then(settings => {
-    let selectedAgentObj = settings.selected_agent;
-    if (!selectedAgentObj && settings.settings && settings.settings.selected_agent) {
-      selectedAgentObj = settings.settings.selected_agent;
-    }
-    let selectedAgentName = typeof selectedAgentObj === 'object' ? selectedAgentObj.name : selectedAgentObj;
-    agentsTbody.innerHTML = '';
-    for (const agent of agentsList) {
-      const tr = document.createElement('tr');
-      
-      // Create action buttons
-      let actionButtons = `<button class="btn btn-sm btn-primary chat-agent-btn me-1" data-name="${agent.name}" title="Chat with this agent">
-        <i class="bi bi-chat-dots me-1"></i>Chat
-      </button>`;
-      
-      if (!agent.is_global) {
-        actionButtons += `
-          <button class="btn btn-sm btn-outline-secondary edit-agent-btn me-1" data-name="${agent.name}" title="Edit agent">
-            <i class="bi bi-pencil"></i>
-          </button>
-          <button class="btn btn-sm btn-outline-danger delete-agent-btn" data-name="${agent.name}" title="Delete agent">
-            <i class="bi bi-trash"></i>
-          </button>
-        `;
-      }
-      
-      const truncatedDisplayName = truncateDisplayName(agent.display_name || agent.name || '');
-      
-      tr.innerHTML = `
-        <td>
-          <strong>${truncatedDisplayName}</strong>
-          ${agent.is_global ? ' <span class="badge bg-info text-dark">Global</span>' : ''}
-        </td>
-        <td class="text-muted small">${agent.description || 'No description available'}</td>
-        <td>${actionButtons}</td>
-      `;
-      agentsTbody.appendChild(tr);
-    }
-  }).catch(e => {
-    renderError('Could not load agent settings: ' + e.message);
-    // Fallback: render table without settings
-    agentsTbody.innerHTML = '';
-    for (const agent of agentsList) {
-      const tr = document.createElement('tr');
-      
-      // Create action buttons
-      let actionButtons = `<button class="btn btn-sm btn-primary chat-agent-btn me-1" data-name="${agent.name}" title="Chat with this agent">
+
+  for (const agent of agentsList) {
+    const tr = document.createElement('tr');
+    const displayName = humanizeName(agent.display_name || agent.name || '');
+    const description = agent.description || 'No description available';
+    const truncatedDesc = truncateDescription(description, 90);
+    const isGlobal = agent.is_global;
+
+    // Action buttons — Chat + View always, Edit/Delete for non-global
+    let actionButtons = `<button class="btn btn-sm btn-primary chat-agent-btn me-1" data-name="${escapeHtml(agent.name)}" title="Chat with this agent">
         <i class="bi bi-chat-dots me-1"></i>Chat
+      </button>
+      <button class="btn btn-sm btn-outline-info view-agent-btn me-1" data-name="${escapeHtml(agent.name)}" title="View details">
+        <i class="bi bi-eye"></i>
       </button>`;
-      
-      if (!agent.is_global) {
-        actionButtons += `
-          <button class="btn btn-sm btn-outline-secondary edit-agent-btn me-1" data-name="${agent.name}" title="Edit agent">
-            <i class="bi bi-pencil"></i>
-          </button>
-          <button class="btn btn-sm btn-outline-danger delete-agent-btn" data-name="${agent.name}" title="Delete agent">
-            <i class="bi bi-trash"></i>
-          </button>
-        `;
-      }
-      
-      const truncatedDisplayName = truncateDisplayName(agent.display_name || agent.name || '');
-      
-      tr.innerHTML = `
-        <td>
-          <strong>${truncatedDisplayName}</strong>
-          ${agent.is_global ? ' <span class="badge bg-info text-dark">Global</span>' : ''}
-        </td>
-        <td class="text-muted small">${agent.description || 'No description available'}</td>
-        <td>${actionButtons}</td>
-      `;
-      agentsTbody.appendChild(tr);
+
+    if (!isGlobal) {
+      actionButtons += `
+        <button class="btn btn-sm btn-outline-secondary edit-agent-btn me-1" data-name="${escapeHtml(agent.name)}" title="Edit agent">
+          <i class="bi bi-pencil"></i>
+        </button>
+        <button class="btn btn-sm btn-outline-danger delete-agent-btn" data-name="${escapeHtml(agent.name)}" title="Delete agent">
+          <i class="bi bi-trash"></i>
+        </button>`;
     }
-  });
+
+    tr.innerHTML = `
+      <td>
+        <strong title="${escapeHtml(agent.display_name || agent.name || '')}">${escapeHtml(displayName)}</strong>
+        ${isGlobal ? ' <span class="badge bg-info text-dark">Global</span>' : ''}
+      </td>
+      <td class="text-muted small" title="${escapeHtml(description)}">${escapeHtml(truncatedDesc)}</td>
+      <td>${actionButtons}</td>
+    `;
+    agentsTbody.appendChild(tr);
+  }
+}
+
+function renderAgentsGrid(agentsList) {
+  if (!agentsGridView) return;
+  agentsGridView.innerHTML = '';
+  if (!agentsList.length) {
+    agentsGridView.innerHTML = '<div class="col-12 text-center text-muted p-4">No agents found.</div>';
+    return;
+  }
+
+  for (const agent of agentsList) {
+    const card = createAgentCard(agent, {
+      onChat: (a) => chatWithAgent(a.name),
+      onView: (a) => openAgentViewModal(a),
+      onEdit: (a) => openAgentModal(a),
+      onDelete: (a) => { if (confirm(`Delete agent '${a.name}'?`)) deleteAgent(a.name); },
+      canManage: !agent.is_global
+    });
+    agentsGridView.appendChild(card);
+  }
 }
 
 async function fetchAgents() {
@@ -151,6 +142,7 @@ async function fetchAgents() {
     agents = await res.json();
     filteredAgents = agents; // Initialize filtered list
     renderAgentsTable(filteredAgents);
+    renderAgentsGrid(filteredAgents);
   } catch (e) {
     renderError(e.message);
   }
@@ -177,17 +169,14 @@ function attachAgentTableEvents() {
   }
   
   agentsTbody.addEventListener('click', function (e) {
-    console.log('Agent table clicked, target:', e.target);
-    
     // Find the button element (could be the target or a parent)
     const editBtn = e.target.closest('.edit-agent-btn');
     const deleteBtn = e.target.closest('.delete-agent-btn');
     const chatBtn = e.target.closest('.chat-agent-btn');
+    const viewBtn = e.target.closest('.view-agent-btn');
     
     if (editBtn) {
-      console.log('Edit agent button clicked, dataset:', editBtn.dataset);
       const agent = agents.find(a => a.name === editBtn.dataset.name);
-      console.log('Found agent:', agent);
       openAgentModal(agent);
     }
     
@@ -201,33 +190,27 @@ function attachAgentTableEvents() {
       const agentName = chatBtn.dataset.name;
       chatWithAgent(agentName);
     }
+
+    if (viewBtn) {
+      const agent = agents.find(a => a.name === viewBtn.dataset.name);
+      if (agent) openAgentViewModal(agent);
+    }
   });
 }
 
 async function chatWithAgent(agentName) {
   try {
-    console.log('DEBUG: chatWithAgent called with agentName:', agentName);
-    console.log('DEBUG: Available agents:', agents);
-    
-    // Find the agent to get its is_global status
     const agent = agents.find(a => a.name === agentName);
-    console.log('DEBUG: Found agent:', agent);
-    
     if (!agent) {
       throw new Error('Agent not found');
     }
     
-    console.log('DEBUG: Agent is_global flag:', agent.is_global);
-    console.log('DEBUG: !!agent.is_global:', !!agent.is_global);
-    
-    // Set the selected agent with proper is_global flag
     const payloadData = { 
       selected_agent: { 
         name: agentName, 
         is_global: !!agent.is_global 
       } 
     };
-    console.log('DEBUG: Sending payload:', payloadData);
     
     const resp = await fetch('/api/user/settings/selected_agent', {
       method: 'POST',
@@ -239,9 +222,6 @@ async function chatWithAgent(agentName) {
       throw new Error('Failed to select agent');
     }
     
-    console.log('DEBUG: Agent selection saved successfully');
-    
-    // Navigate to chat page
     window.location.href = '/chats';
   } catch (err) {
     console.error('Error selecting agent for chat:', err);
@@ -353,6 +333,17 @@ async function deleteAgent(name) {
 function initializeWorkspaceAgentUI() {
   window.agentModalStepper = new AgentModalStepper(false);
   attachAgentTableEvents();
+
+  // Set up view toggle
+  setupViewToggle('agents', 'agentsViewPreference', (mode) => {
+    currentViewMode = mode;
+    switchViewContainers(mode, agentsListView, agentsGridView);
+    // Re-render grid if switching to grid and we have data
+    if (mode === 'grid' && filteredAgents.length) {
+      renderAgentsGrid(filteredAgents);
+    }
+  });
+
   fetchAgents();
 }
 
diff --git a/application/single_app/static/js/workspace/workspace_plugins.js b/application/single_app/static/js/workspace/workspace_plugins.js
index 30fef0d5..84f1eb46 100644
--- a/application/single_app/static/js/workspace/workspace_plugins.js
+++ b/application/single_app/static/js/workspace/workspace_plugins.js
@@ -1,10 +1,14 @@
 // workspace_plugins.js (refactored to use plugin_common.js and new multi-step modal)
-import { renderPluginsTable, ensurePluginsTableInRoot, validatePluginManifest } from '../plugin_common.js';
+import { renderPluginsTable, renderPluginsGrid, ensurePluginsTableInRoot, validatePluginManifest } from '../plugin_common.js';
 import { showToast } from "../chat/chat-toast.js"
+import {
+    setupViewToggle, switchViewContainers, openViewModal
+} from './view-utils.js';
 
 const root = document.getElementById('workspace-plugins-root');
 let plugins = [];
 let filteredPlugins = [];
+let currentViewMode = 'list';
 
 function renderLoading() {
   root.innerHTML = `<div class="text-center p-4"><div class="spinner-border" role="status"><span class="visually-hidden">Loading...</span></div></div>`;
@@ -14,6 +18,22 @@ function renderError(msg) {
   root.innerHTML = `<div class="alert alert-danger">${msg}</div>`;
 }
 
+function getViewHandlers() {
+  return {
+    onEdit: name => openPluginModal(plugins.find(p => p.name === name)),
+    onDelete: name => deletePlugin(name),
+    onView: name => {
+      const plugin = plugins.find(p => p.name === name);
+      if (plugin) {
+        openViewModal(plugin, 'action', {
+          onEdit: (item) => openPluginModal(item),
+          onDelete: (item) => deletePlugin(item.name)
+        });
+      }
+    }
+  };
+}
+
 function filterPlugins(searchTerm) {
   if (!searchTerm || !searchTerm.trim()) {
     filteredPlugins = plugins;
@@ -26,14 +46,18 @@ function filterPlugins(searchTerm) {
     });
   }
   
-  // Ensure table template is in place
   ensurePluginsTableInRoot();
+  const handlers = getViewHandlers();
   
   renderPluginsTable({
     plugins: filteredPlugins,
     tbodySelector: '#plugins-table-body',
-    onEdit: name => openPluginModal(plugins.find(p => p.name === name)),
-    onDelete: name => deletePlugin(name)
+    ...handlers
+  });
+  renderPluginsGrid({
+    plugins: filteredPlugins,
+    containerSelector: '#plugins-grid-view',
+    ...handlers
   });
 }
 
@@ -47,12 +71,26 @@ async function fetchPlugins() {
     
     // Ensure table template is in place
     ensurePluginsTableInRoot();
+    const handlers = getViewHandlers();
     
     renderPluginsTable({
       plugins: filteredPlugins,
       tbodySelector: '#plugins-table-body',
-      onEdit: name => openPluginModal(plugins.find(p => p.name === name)),
-      onDelete: name => deletePlugin(name)
+      ...handlers
+    });
+    renderPluginsGrid({
+      plugins: filteredPlugins,
+      containerSelector: '#plugins-grid-view',
+      ...handlers
+    });
+    
+    // Set up view toggle (only once after template is in DOM)
+    setupViewToggle('plugins', 'pluginsViewPreference', (mode) => {
+      currentViewMode = mode;
+      switchViewContainers(mode,
+        document.getElementById('plugins-list-view'),
+        document.getElementById('plugins-grid-view')
+      );
     });
     
     // Set up the create action button
diff --git a/application/single_app/static/json/schemas/sql_query.definition.json b/application/single_app/static/json/schemas/sql_query.definition.json
index d38a41a8..6903c22a 100644
--- a/application/single_app/static/json/schemas/sql_query.definition.json
+++ b/application/single_app/static/json/schemas/sql_query.definition.json
@@ -1,6 +1,9 @@
 {
   "$schema": "./plugin.definition.schema.json",
   "allowedAuthTypes": [
+    "user",
+    "identity",
+    "servicePrincipal",
     "connection_string"
   ]
 }
diff --git a/application/single_app/static/json/schemas/sql_query_plugin.additional_settings.schema.json b/application/single_app/static/json/schemas/sql_query_plugin.additional_settings.schema.json
index 9e4f6d34..f7f46ebd 100644
--- a/application/single_app/static/json/schemas/sql_query_plugin.additional_settings.schema.json
+++ b/application/single_app/static/json/schemas/sql_query_plugin.additional_settings.schema.json
@@ -3,13 +3,13 @@
   "title": "SQL Query Plugin Additional Settings",
   "type": "object",
   "properties": {
-    "connection_string__Secret": {
+    "connection_string": {
       "type": "string",
       "description": "Database connection string. Required if server/database not provided."
     },
     "database_type": {
       "type": "string",
-      "enum": ["sqlserver", "postgresql", "mysql", "sqlite", "azure_sql", "azuresql"],
+      "enum": ["sqlserver", "postgresql", "mysql", "sqlite", "azure_sql"],
       "description": "Type of database engine."
     },
     "server": {
@@ -24,7 +24,7 @@
       "type": "string",
       "description": "Username for authentication."
     },
-    "password__Secret": {
+    "password": {
       "type": "string",
       "description": "Password for authentication."
     },
@@ -50,6 +50,6 @@
       "description": "Query timeout in seconds."
     }
   },
-  "required": ["database_type", "database"],
+  "required": ["database_type"],
   "additionalProperties": false
 }
diff --git a/application/single_app/static/json/schemas/sql_schema.definition.json b/application/single_app/static/json/schemas/sql_schema.definition.json
index d38a41a8..6903c22a 100644
--- a/application/single_app/static/json/schemas/sql_schema.definition.json
+++ b/application/single_app/static/json/schemas/sql_schema.definition.json
@@ -1,6 +1,9 @@
 {
   "$schema": "./plugin.definition.schema.json",
   "allowedAuthTypes": [
+    "user",
+    "identity",
+    "servicePrincipal",
     "connection_string"
   ]
 }
diff --git a/application/single_app/static/json/schemas/sql_schema_plugin.additional_settings.schema.json b/application/single_app/static/json/schemas/sql_schema_plugin.additional_settings.schema.json
index e97c7b4b..29fb6b3f 100644
--- a/application/single_app/static/json/schemas/sql_schema_plugin.additional_settings.schema.json
+++ b/application/single_app/static/json/schemas/sql_schema_plugin.additional_settings.schema.json
@@ -3,13 +3,13 @@
   "title": "SQL Schema Plugin Additional Settings",
   "type": "object",
   "properties": {
-    "connection_string__Secret": {
+    "connection_string": {
       "type": "string",
       "description": "Database connection string. Required if server/database not provided."
     },
     "database_type": {
       "type": "string",
-      "enum": ["sqlserver", "postgresql", "mysql", "sqlite", "azure_sql", "azuresql"],
+      "enum": ["sqlserver", "postgresql", "mysql", "sqlite", "azure_sql"],
       "description": "Type of database engine."
     },
     "server": {
@@ -24,7 +24,7 @@
       "type": "string",
       "description": "Username for authentication."
     },
-    "password__Secret": {
+    "password": {
       "type": "string",
       "description": "Password for authentication."
     },
@@ -33,6 +33,6 @@
       "description": "ODBC or DB driver name."
     }
   },
-  "required": ["database_type", "database"],
+  "required": ["database_type"],
   "additionalProperties": false
 }
diff --git a/application/single_app/templates/_agent_examples_modal.html b/application/single_app/templates/_agent_examples_modal.html
index 52f95cdc..398e930c 100644
--- a/application/single_app/templates/_agent_examples_modal.html
+++ b/application/single_app/templates/_agent_examples_modal.html
@@ -92,7 +92,7 @@ <h5 class="modal-title mb-0" id="agentExampleDetailsModalLabel">Agent Template</
         <div class="mb-3" id="agent-example-details-actions"></div>
         <div class="mb-3">
           <label class="fw-semibold text-muted small">Instructions</label>
-          <pre class="bg-dark text-white rounded p-3" id="agent-example-details-instructions"></pre>
+          <div class="border rounded p-3 rendered-markdown" id="agent-example-details-instructions" style="max-height: 400px; overflow-y: auto; font-size: 0.9rem;"></div>
         </div>
         <div class="mb-3 d-none" id="agent-example-details-settings-wrapper">
           <label class="fw-semibold text-muted small">Additional Settings</label>
@@ -427,7 +427,12 @@ <h5 class="modal-title mb-0" id="agentExampleDetailsModalLabel">Agent Template</
                 detailDescription.textContent = template.description || template.helper_text || 'No description provided.';
               }
               if (detailInstructions) {
-                detailInstructions.textContent = template.instructions || '';
+                const rawInstructions = template.instructions || '';
+                if (typeof marked !== 'undefined' && typeof DOMPurify !== 'undefined' && rawInstructions) {
+                  detailInstructions.innerHTML = DOMPurify.sanitize(marked.parse(rawInstructions));
+                } else {
+                  detailInstructions.textContent = rawInstructions;
+                }
               }
               if (detailSettingsWrapper && detailSettings) {
                 if (template.additional_settings) {
diff --git a/application/single_app/templates/_plugin_modal.html b/application/single_app/templates/_plugin_modal.html
index 3af18019..8acef22d 100644
--- a/application/single_app/templates/_plugin_modal.html
+++ b/application/single_app/templates/_plugin_modal.html
@@ -425,21 +425,46 @@ <h6><i class="bi bi-info-circle me-2"></i>API Information</h6>
                 </div>
               </div>
             </div>
+
+            <!-- Test Connection Button -->
+            <div class="mt-4 pt-3 border-top" id="sql-test-connection-section">
+              <div class="d-flex align-items-center gap-3">
+                <button type="button" class="btn btn-outline-primary" id="sql-test-connection-btn">
+                  <i class="bi bi-plug me-2"></i>Test Connection
+                </button>
+                <div id="sql-test-connection-status" class="d-none">
+                  <span id="sql-test-connection-spinner" class="spinner-border spinner-border-sm me-2 d-none" role="status" aria-hidden="true"></span>
+                  <span id="sql-test-connection-message"></span>
+                </div>
+              </div>
+              <div id="sql-test-connection-result" class="mt-2 d-none">
+                <div class="alert mb-0 py-2 px-3 small" id="sql-test-connection-alert" role="alert"></div>
+              </div>
+            </div>
           </div>
         </div>
 
         <!-- Step 4: Advanced -->
         <div class="plugin-step d-none" id="plugin-step-4">
           <h6 class="mb-3">Advanced</h6>
+          <p class="text-muted small mb-3">Advanced settings are typically not required. Expand below if you need to customize metadata or additional fields.</p>
           <div class="mb-3">
-            <label for="plugin-metadata" class="form-label">Metadata (JSON)</label>
-            <textarea class="form-control" id="plugin-metadata" rows="4" placeholder="{}"></textarea>
-            <div class="form-text">Optional metadata for this action.</div>
+            <button class="btn btn-sm btn-outline-secondary w-100 d-flex align-items-center justify-content-between" type="button" data-bs-toggle="collapse" data-bs-target="#plugin-advanced-collapse" aria-expanded="false" aria-controls="plugin-advanced-collapse">
+              <span><i class="bi bi-gear me-2"></i>Show Advanced Settings</span>
+              <i class="bi bi-chevron-down" id="plugin-advanced-toggle-icon"></i>
+            </button>
           </div>
-          <div class="mb-3" id="plugin-additional-fields-div">
-            <label for="plugin-additional-fields" class="form-label">Additional Fields (JSON)</label>
-            <textarea class="form-control" id="plugin-additional-fields" rows="6" placeholder="{}"></textarea>
-            <div class="form-text">Additional configuration fields specific to this action type.</div>
+          <div class="collapse" id="plugin-advanced-collapse">
+            <div class="mb-3">
+              <label for="plugin-metadata" class="form-label">Metadata (JSON)</label>
+              <textarea class="form-control" id="plugin-metadata" rows="4" placeholder="{}"></textarea>
+              <div class="form-text">Optional metadata for this action.</div>
+            </div>
+            <div class="mb-3" id="plugin-additional-fields-div">
+              <label for="plugin-additional-fields" class="form-label">Additional Fields (JSON)</label>
+              <textarea class="form-control" id="plugin-additional-fields" rows="6" placeholder="{}"></textarea>
+              <div class="form-text">Additional configuration fields specific to this action type.</div>
+            </div>
           </div>
         </div>
 
@@ -777,6 +802,15 @@ <h6 class="mb-0 fw-bold">
   background-color: #f8f9fa;
 }
 
+/* Advanced toggle chevron animation */
+#plugin-advanced-toggle-icon {
+  transition: transform 0.3s ease;
+}
+#plugin-advanced-collapse.show ~ .mb-3 #plugin-advanced-toggle-icon,
+[aria-expanded="true"] #plugin-advanced-toggle-icon {
+  transform: rotate(180deg);
+}
+
 .sql-connection-config, 
 .sql-auth-config {
   background-color: white;
diff --git a/application/single_app/templates/_sidebar_nav.html b/application/single_app/templates/_sidebar_nav.html
index a0bceee8..33a89b04 100644
--- a/application/single_app/templates/_sidebar_nav.html
+++ b/application/single_app/templates/_sidebar_nav.html
@@ -287,6 +287,11 @@
                   <i class="bi bi-chat-square-text me-2" style="font-size: 0.8em;"></i><span class="nav-text">GPT Configuration</span>
                 </a>
               </li>
+              <li class="nav-item">
+                <a class="nav-link d-flex align-items-center admin-nav-section" href="#" data-tab="ai-models" data-section="processing-thoughts-section">
+                  <i class="bi bi-stars me-2" style="font-size: 0.8em;"></i><span class="nav-text">Processing Thoughts</span>
+                </a>
+              </li>
               <li class="nav-item">
                 <a class="nav-link d-flex align-items-center admin-nav-section" href="#" data-tab="ai-models" data-section="embeddings-config">
                   <i class="bi bi-vector-pen me-2" style="font-size: 0.8em;"></i><span class="nav-text">Embeddings</span>
diff --git a/application/single_app/templates/admin_settings.html b/application/single_app/templates/admin_settings.html
index 9c385b0e..8e6a9212 100644
--- a/application/single_app/templates/admin_settings.html
+++ b/application/single_app/templates/admin_settings.html
@@ -953,6 +953,12 @@ <h6 class="mb-2">Core Action Toggles</h6>
                                     <label class="form-check-label ms-2" for="toggle-fact-memory-plugin">Enable Fact Memory Action</label>
                                     <i class="bi bi-info-circle ms-2" data-bs-toggle="tooltip" title="Enables agents to store and retrieve facts and information across conversations for improved memory and context retention."></i>
                                 </div>
+                                <div class="form-check form-switch mb-2" id="tabular-processing-toggle-wrapper">
+                                    <input class="form-check-input" type="checkbox" role="switch" id="toggle-tabular-processing-plugin" name="enable_tabular_processing_plugin">
+                                    <label class="form-check-label ms-2" for="toggle-tabular-processing-plugin">Enable Tabular Processing Action</label>
+                                    <i class="bi bi-info-circle ms-2" data-bs-toggle="tooltip" title="Enables agents to analyze tabular data files (CSV, XLSX) using pandas for aggregations, filtering, and full dataset operations. Requires Enhanced Citations."></i>
+                                    <small class="text-muted d-block ms-4" id="tabular-processing-dependency-note">Requires Enhanced Citations</small>
+                                </div>
                             </div>
                             <table class="table table-bordered" id="plugins-table">
                                 <thead>
@@ -1580,6 +1586,27 @@ <h5 class="card-title">
                 <div id="test_gpt_result" class="mt-2"></div>
                 </div>
 
+                <!-- Processing Thoughts Section -->
+                <div class="card p-3 mb-4" id="processing-thoughts-section">
+                    <h5>
+                        <i class="bi bi-stars me-2"></i>Processing Thoughts
+                    </h5>
+                    <p class="text-muted">When enabled, real-time processing steps are shown to users during chat responses and persisted for later review.</p>
+                    <div class="form-group form-check form-switch mb-3">
+                        <input
+                            type="checkbox"
+                            class="form-check-input"
+                            id="enable_thoughts"
+                            name="enable_thoughts"
+                            {% if settings.enable_thoughts %}checked{% endif %}
+                        >
+                        <label class="form-check-label ms-2" for="enable_thoughts">
+                            Enable Processing Thoughts
+                        </label>
+                        <i class="bi bi-info-circle ms-2" data-bs-toggle="tooltip" title="Show real-time processing steps (searching documents, web search, agent calls, etc.) to users during chat. Steps are stored in Cosmos DB and can be reviewed per-message via a stars icon."></i>
+                    </div>
+                </div>
+
                 <!-- Embeddings Configuration Section -->
                 <div class="card p-3 mb-4" id="embeddings-configuration">
                     <h5 class="card-title">
@@ -2924,6 +2951,23 @@ <h6><strong>All filetypes</strong></h6>
                             </div>
                         </div>
 
+                        <div class="card mb-3 p-3">
+                            <h6><strong>Tabular Preview Limits</strong></h6>
+                            <div class="mb-3">
+                                <label for="tabular_preview_max_blob_size_mb" class="form-label">
+                                    Maximum File Size for Tabular Preview (MB)
+                                </label>
+                                <input type="number" class="form-control" id="tabular_preview_max_blob_size_mb"
+                                    name="tabular_preview_max_blob_size_mb"
+                                    min="1" max="1024"
+                                    value="{{ settings.tabular_preview_max_blob_size_mb or 200 }}">
+                                <small class="form-text text-muted">
+                                    Maximum blob size (in MB) allowed for tabular file previews (CSV, XLSX). Files larger than this will not be previewed. 
+                                    Increase for larger files if your compute has sufficient memory, or decrease to protect smaller instances. Default: 200 MB.
+                                </small>
+                            </div>
+                        </div>
+
                         
                         <!-- <div class="card mb-3 p-3">
                             <h6><strong>Video Files</strong></h6>
@@ -3229,9 +3273,10 @@ <h5>
                         <i class="bi bi-info-circle ms-2" data-bs-toggle="tooltip" title="Archive conversations instead of permanently deleting them, allowing for recovery and compliance with data retention policies."></i>
                     </div>
                 </div>
+
             </div>
 
-            
+
             <div class="tab-pane fade" id="search-extract" role="tabpanel" aria-labelledby="search-extract-tab">
                 
                  <p class="text-muted">
diff --git a/application/single_app/templates/chats.html b/application/single_app/templates/chats.html
index b6c212cc..e845cc53 100644
--- a/application/single_app/templates/chats.html
+++ b/application/single_app/templates/chats.html
@@ -1039,7 +1039,8 @@ <h5 class="modal-title" id="scopeLockModalLabel">
         enable_text_to_speech: {{ 'true' if app_settings.enable_text_to_speech else 'false' }},
         enable_speech_to_text_input: {{ 'true' if app_settings.enable_speech_to_text_input else 'false' }},
         enable_web_search_user_notice: {{ 'true' if settings.enable_web_search_user_notice else 'false' }},
-        enforce_workspace_scope_lock: {{ 'true' if settings.enforce_workspace_scope_lock else 'false' }}
+        enforce_workspace_scope_lock: {{ 'true' if settings.enforce_workspace_scope_lock else 'false' }},
+        enable_thoughts: {{ 'true' if settings.enable_thoughts else 'false' }}
     };
 
     // Layout related globals (can stay here or move entirely into chat-layout.js if preferred)
diff --git a/application/single_app/templates/group_workspaces.html b/application/single_app/templates/group_workspaces.html
index e14fe8a3..62937fc3 100644
--- a/application/single_app/templates/group_workspaces.html
+++ b/application/single_app/templates/group_workspaces.html
@@ -763,33 +763,42 @@ <h2>Group Workspace</h2>
         <div id="group-agents-permission-warning" class="alert alert-warning d-none">
           You do not have permission to manage group agents.
         </div>
-        <div class="mb-3">
+        <div class="d-flex align-items-center gap-2 mb-3">
           <input
             type="search"
             class="form-control"
             id="group-agents-search"
             placeholder="Search group agents by name or description..."
           />
+          <div class="btn-group btn-group-sm" role="group" aria-label="View toggle">
+            <input type="radio" class="btn-check" name="groupAgentsViewToggle" id="groupAgents-view-list" value="list" autocomplete="off" checked>
+            <label class="btn btn-outline-secondary" for="groupAgents-view-list" title="List view"><i class="bi bi-list-ul"></i></label>
+            <input type="radio" class="btn-check" name="groupAgentsViewToggle" id="groupAgents-view-grid" value="grid" autocomplete="off">
+            <label class="btn btn-outline-secondary" for="groupAgents-view-grid" title="Grid view"><i class="bi bi-grid-3x3-gap"></i></label>
+          </div>
         </div>
-        <table class="table table-striped" id="group-agents-table">
-          <thead>
-            <tr>
-              <th>Display Name</th>
-              <th>Description</th>
-              <th style="width: 160px;">Actions</th>
-            </tr>
-          </thead>
-          <tbody id="group-agents-table-body">
-            <tr class="table-loading-row">
-              <td colspan="3">
-                <div class="spinner-border spinner-border-sm me-2" role="status">
-                  <span class="visually-hidden">Loading...</span>
-                </div>
-                Select a group to load agents.
-              </td>
-            </tr>
-          </tbody>
-        </table>
+        <div id="group-agents-list-view">
+          <table class="table table-striped item-list-table" id="group-agents-table">
+            <thead>
+              <tr>
+                <th>Display Name</th>
+                <th>Description</th>
+                <th>Actions</th>
+              </tr>
+            </thead>
+            <tbody id="group-agents-table-body">
+              <tr class="table-loading-row">
+                <td colspan="3">
+                  <div class="spinner-border spinner-border-sm me-2" role="status">
+                    <span class="visually-hidden">Loading...</span>
+                  </div>
+                  Select a group to load agents.
+                </td>
+              </tr>
+            </tbody>
+          </table>
+        </div>
+        <div id="group-agents-grid-view" class="row g-3 d-none"></div>
       </div>
       <div id="group-agents-error"></div>
     </div>
@@ -813,33 +822,42 @@ <h2>Group Workspace</h2>
           <div class="d-flex justify-content-between align-items-center mb-2">
             <button class="btn btn-success btn-sm" id="create-group-plugin-btn">New Action</button>
           </div>
-          <div class="mb-3">
+          <div class="d-flex align-items-center gap-2 mb-3">
             <input
               type="search"
               class="form-control"
               id="group-plugins-search"
               placeholder="Search group actions by name or description..."
             />
+            <div class="btn-group btn-group-sm" role="group" aria-label="View toggle">
+              <input type="radio" class="btn-check" name="groupPluginsViewToggle" id="groupPlugins-view-list" value="list" autocomplete="off" checked>
+              <label class="btn btn-outline-secondary" for="groupPlugins-view-list" title="List view"><i class="bi bi-list-ul"></i></label>
+              <input type="radio" class="btn-check" name="groupPluginsViewToggle" id="groupPlugins-view-grid" value="grid" autocomplete="off">
+              <label class="btn btn-outline-secondary" for="groupPlugins-view-grid" title="Grid view"><i class="bi bi-grid-3x3-gap"></i></label>
+            </div>
           </div>
-          <table class="table table-striped" id="group-plugins-table">
-            <thead>
-              <tr>
-                <th>Display Name</th>
-                <th>Description</th>
-                <th style="width: 140px;">Actions</th>
-              </tr>
-            </thead>
-            <tbody id="group-plugins-table-body">
-              <tr class="table-loading-row">
-                <td colspan="3">
-                  <div class="spinner-border spinner-border-sm me-2" role="status">
-                    <span class="visually-hidden">Loading...</span>
-                  </div>
-                  Select a group to load actions.
-                </td>
-              </tr>
-            </tbody>
-          </table>
+          <div id="group-plugins-list-view">
+            <table class="table table-striped item-list-table" id="group-plugins-table">
+              <thead>
+                <tr>
+                  <th>Display Name</th>
+                  <th>Description</th>
+                  <th>Actions</th>
+                </tr>
+              </thead>
+              <tbody id="group-plugins-table-body">
+                <tr class="table-loading-row">
+                  <td colspan="3">
+                    <div class="spinner-border spinner-border-sm me-2" role="status">
+                      <span class="visually-hidden">Loading...</span>
+                    </div>
+                    Select a group to load actions.
+                  </td>
+                </tr>
+              </tbody>
+            </table>
+          </div>
+          <div id="group-plugins-grid-view" class="row g-3 d-none"></div>
         </div>
       </template>
     </div>
@@ -851,6 +869,22 @@ <h2>Group Workspace</h2>
 </div>
 <!-- End Container -->
 
+<!-- Item View Modal (shared by agents & actions grid/list view buttons) -->
+<div class="modal fade" id="item-view-modal" tabindex="-1" aria-labelledby="item-view-modal-title" aria-hidden="true">
+  <div class="modal-dialog modal-lg modal-dialog-scrollable">
+    <div class="modal-content">
+      <div class="modal-header">
+        <h5 class="modal-title" id="item-view-modal-title">View Item</h5>
+        <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+      </div>
+      <div class="modal-body" id="item-view-modal-body"></div>
+      <div class="modal-footer">
+        <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">Close</button>
+      </div>
+    </div>
+  </div>
+</div>
+
 <!-- Loading Modal (Keep As Is - Used by Upload) -->
 <div
   class="modal fade"
diff --git a/application/single_app/templates/workspace.html b/application/single_app/templates/workspace.html
index c17ff5f5..798cff37 100644
--- a/application/single_app/templates/workspace.html
+++ b/application/single_app/templates/workspace.html
@@ -688,21 +688,32 @@ <h2>Personal Workspace</h2>
                   </button>
                   <button class="btn btn-success btn-sm" id="create-agent-btn" type="button">New Agent</button>
                 </div>
+                <div class="btn-group btn-group-sm" role="group" aria-label="Agents view mode">
+                  <input type="radio" class="btn-check" name="agents-view-mode" id="agents-view-list" autocomplete="off" checked>
+                  <label class="btn btn-outline-secondary" for="agents-view-list"><i class="bi bi-list-ul"></i></label>
+                  <input type="radio" class="btn-check" name="agents-view-mode" id="agents-view-grid" autocomplete="off">
+                  <label class="btn btn-outline-secondary" for="agents-view-grid"><i class="bi bi-grid-3x3-gap"></i></label>
+                </div>
               </div>
               <div class="mb-3">
                 <input type="search" class="form-control" id="agents-search" placeholder="Search agents by name or description...">
               </div>
-              <table class="table table-striped" id="agents-table">
-                <thead><tr><th>Display Name</th><th>Description</th><th>Actions</th></tr></thead>
-                <tbody id="agents-table-body">
-                  <tr class="table-loading-row">
-                    <td colspan="3">
-                      <div class="spinner-border spinner-border-sm me-2" role="status"><span class="visually-hidden">Loading...</span></div>
-                      Loading agents...
-                    </td>
-                  </tr>
-                </tbody>
-              </table>
+              <!-- List View -->
+              <div id="agents-list-view">
+                <table class="table table-striped item-list-table" id="agents-table">
+                  <thead><tr><th>Display Name</th><th>Description</th><th>Actions</th></tr></thead>
+                  <tbody id="agents-table-body">
+                    <tr class="table-loading-row">
+                      <td colspan="3">
+                        <div class="spinner-border spinner-border-sm me-2" role="status"><span class="visually-hidden">Loading...</span></div>
+                        Loading agents...
+                      </td>
+                    </tr>
+                  </tbody>
+                </table>
+              </div>
+              <!-- Grid View -->
+              <div id="agents-grid-view" class="row g-3 d-none"></div>
             </div>
             <div id="workspace-agents-error"></div>
           </div>
@@ -730,16 +741,27 @@ <h2>Personal Workspace</h2>
             <div class="card p-3 my-3">
               <div class="d-flex justify-content-between align-items-center mb-2">
                 <button class="btn btn-success btn-sm" id="create-plugin-btn">New Action</button>
+                <div class="btn-group btn-group-sm" role="group" aria-label="Actions view mode">
+                  <input type="radio" class="btn-check" name="plugins-view-mode" id="plugins-view-list" autocomplete="off" checked>
+                  <label class="btn btn-outline-secondary" for="plugins-view-list"><i class="bi bi-list-ul"></i></label>
+                  <input type="radio" class="btn-check" name="plugins-view-mode" id="plugins-view-grid" autocomplete="off">
+                  <label class="btn btn-outline-secondary" for="plugins-view-grid"><i class="bi bi-grid-3x3-gap"></i></label>
+                </div>
               </div>
               <div class="mb-3">
                 <input type="search" class="form-control" id="plugins-search" placeholder="Search actions by name or description...">
               </div>
-              <table class="table table-striped" id="plugins-table">
-                <thead><tr><th>Display Name</th><th>Description</th><th style="width: 140px;">Actions</th></tr></thead>
-                <tbody id="plugins-table-body">
-                  <!-- Populated by JS -->
-                </tbody>
-              </table>
+              <!-- List View -->
+              <div id="plugins-list-view">
+                <table class="table table-striped item-list-table" id="plugins-table">
+                  <thead><tr><th>Display Name</th><th>Description</th><th>Actions</th></tr></thead>
+                  <tbody id="plugins-table-body">
+                    <!-- Populated by JS -->
+                  </tbody>
+                </table>
+              </div>
+              <!-- Grid View -->
+              <div id="plugins-grid-view" class="row g-3 d-none"></div>
             </div>
           </template>
           </div>
@@ -754,6 +776,24 @@ <h2>Personal Workspace</h2>
   </div>
   <!--</div>-->
 
+  <!-- Shared Item View Modal (read-only details for agents/actions) -->
+  <div class="modal fade" id="item-view-modal" tabindex="-1" aria-labelledby="item-view-modal-label" aria-hidden="true">
+    <div class="modal-dialog modal-lg modal-dialog-scrollable">
+      <div class="modal-content">
+        <div class="modal-header">
+          <h5 class="modal-title" id="item-view-modal-label">Details</h5>
+          <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+        </div>
+        <div class="modal-body">
+          <!-- Populated dynamically by view-utils.js -->
+        </div>
+        <div class="modal-footer">
+          <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">Close</button>
+        </div>
+      </div>
+    </div>
+  </div>
+
   <!-- Modal for Creating/Editing Prompts (Keep As Is) -->
   <div
     class="modal fade"
diff --git a/application/single_app/tmp2xmw9c75.xlsx b/application/single_app/tmp2xmw9c75.xlsx
new file mode 100644
index 00000000..e2603075
Binary files /dev/null and b/application/single_app/tmp2xmw9c75.xlsx differ
diff --git a/artifacts/faa_flight_operations_dataset.csv b/artifacts/faa_flight_operations_dataset.csv
new file mode 100644
index 00000000..91a41f8c
--- /dev/null
+++ b/artifacts/faa_flight_operations_dataset.csv
@@ -0,0 +1,10001 @@
+"FlightID","Callsign","Airline","AircraftType","TailNumber","OriginAirport","DestinationAirport","Gate","Runway","TaxiRoute","ScheduledDeparture","ActualDeparture","ScheduledArrival","ActualArrival","TaxiOutMinutes","TaxiInMinutes","DelayMinutes","DelayCategory","DelayReason","VisibilityMiles","WindSpeed","WindDirection","TemperatureC","Precipitation","Sector","CruiseAltitudeFt","RouteMiles","FuelBurnKg","PassengerCount","CargoWeightKg","LoadFactor","GateTurnTimeMinutes","ADSBSignalQuality","TransponderCode","ConflictAlert","GoAroundFlag","ATCControllerID","ClimbRateFPM","DescentRateFPM","CruiseSpeedKts","HoldingMinutes","DeicingRequired","RunwayOccupancySeconds","SurfaceDelayMinutes","DepartureQueueLength","ArrivalQueueLength"
+"1","FL3211","SWA","B739","N5431G","DEN","LAX","G31","26","TX17","5/14/2026 7:51:36 AM","5/14/2026 8:31:36 AM","5/14/2026 12:30:36 PM","5/14/2026 1:10:36 PM","25","22","40","Operational","Crew","5","34","217","31","None","ZFW","31236","2296","10842","169","2286","7435048.87","55","84","3108","0","0","ATC417","1193","1896","486","22","1","102","10","20","3"
+"2","FL3699","NKS","B738","N4842","MIA","DFW","G12","19","TX2","1/11/2026 12:54:36 AM","1/11/2026 12:58:36 AM","1/11/2026 4:29:36 AM","1/11/2026 4:33:36 AM","28","23","4","Operational","Crew","9","36","120","24","None","ZNY","31778","1805","11177","199","3671","20680436.07","46","83","1554","0","1","ATC377","1835","1106","453","8","0","106","20","10","15"
+"3","FL4430","FFT","B739","N7715F","JFK","IAH","G8","7","TX7","2/15/2026 8:42:36 PM","2/15/2026 9:00:36 PM","2/16/2026 12:53:36 AM","2/16/2026 1:11:36 AM","11","25","18","Operational","Security","1","26","95","36","Snow","ZSE","24138","1929","2588","136","6062","6417088.38","70","85","7085","0","0","ATC580","2525","2026","507","26","1","84","35","22","4"
+"4","FL1871","JBU","B763","N1302D","DEN","LAX","G8","17","TX19","2/24/2026 9:01:36 AM","2/24/2026 10:41:36 AM","2/24/2026 11:21:36 AM","2/24/2026 1:01:36 PM","17","16","100","Operational","ATC","6","6","165","4","Snow","ZTL","35498","1606","4635","164","6246","5926961.68","32","83","7581","1","0","ATC146","2259","2587","501","20","1","98","53","21","3"
+"5","FL5578","FFT","B752","N2223F","MIA","JFK","G29","24","TX13","2/10/2026 11:31:36 PM","2/11/2026 1:25:36 AM","2/11/2026 2:29:36 AM","2/11/2026 4:23:36 AM","35","28","114","Operational","ATC","3","3","204","-13","Fog","ZTL","20214","1266","9047","65","4386","18997579.88","70","93","3662","0","1","ATC140","1894","2826","454","18","0","98","20","16","16"
+"6","FL1251","AAL","B738","N5811I","SEA","ORD","G49","35","TX6","1/9/2026 1:05:36 AM","1/9/2026 1:47:36 AM","1/9/2026 2:46:36 AM","1/9/2026 3:28:36 AM","24","29","42","Operational","Maintenance","8","9","248","-8","Thunderstorm","ZOB","23284","1895","6252","138","7847","4721713.47","74","87","1326","0","1","ATC230","2406","1708","452","25","1","37","50","24","4"
+"7","FL6785","AAL","B739","N6874","ORD","SEA","G29","5","TX15","3/25/2026 6:48:36 PM","3/25/2026 7:49:36 PM","3/25/2026 10:18:36 PM","3/25/2026 11:19:36 PM","11","22","61","Operational","None","2","33","124","4","Thunderstorm","ZTL","33770","485","4592","99","7626","18423972.13","59","95","4314","0","0","ATC828","2104","2981","504","3","1","41","10","3","17"
+"8","FL1466","DAL","A320","N670NQ","DEN","IAH","G42","27","TX18","4/13/2026 10:39:36 AM","4/13/2026 11:30:36 AM","4/13/2026 11:42:36 AM","4/13/2026 12:33:36 PM","14","29","51","Operational","ATC","2","23","203","-14","Snow","ZNY","29504","1691","8732","172","3948","13917818.08","44","91","3629","1","1","ATC383","2939","1415","466","28","0","50","10","23","13"
+"9","FL3591","DAL","A320","N6034","ORD","MIA","G27","14","TX8","1/6/2026 10:36:36 PM","1/6/2026 11:35:36 PM","1/7/2026 1:30:36 AM","1/7/2026 2:29:36 AM","24","20","59","Operational","None","6","0","139","0","Fog","ZFW","37473","1633","8155","101","2877","10383076.23","52","87","4735","0","1","ATC996","2178","1371","485","29","0","98","28","14","21"
+"10","FL6433","NKS","B737","N828QV","DFW","ORD","G32","12","TX6","1/26/2026 10:18:36 PM","1/26/2026 11:31:36 PM","1/27/2026 1:44:36 AM","1/27/2026 2:57:36 AM","7","14","73","Operational","ATC","8","30","50","-1","Rain","ZDC","36217","1454","2549","141","327","14577212.75","46","88","2485","0","1","ATC591","2549","2521","496","17","1","108","30","6","10"
+"11","FL5505","UAL","E175","N6478FY","ORD","DFW","G12","35","TX18","1/9/2026 1:06:36 AM","1/9/2026 2:38:36 AM","1/9/2026 3:50:36 AM","1/9/2026 5:22:36 AM","19","8","92","Operational","Crew","6","10","315","10","Thunderstorm","ZOB","34197","2440","3026","66","3959","13853943.16","73","94","6590","0","0","ATC940","2758","2806","457","10","0","70","42","8","16"
+"12","FL7887","DAL","CRJ9","N8639E","IAH","IAH","G25","34","TX7","4/29/2026 9:56:36 AM","4/29/2026 10:29:36 AM","4/29/2026 12:01:36 PM","4/29/2026 12:34:36 PM","38","12","33","Operational","Crew","5","22","135","22","Rain","ZDC","32293","2454","6907","155","1775","17257432.88","87","87","3632","0","0","ATC564","3232","1009","442","5","0","72","33","16","0"
+"13","FL2822","FFT","CRJ9","N5795C","DEN","SEA","G22","27","TX17","4/17/2026 10:14:36 AM","4/17/2026 11:01:36 AM","4/17/2026 12:21:36 PM","4/17/2026 1:08:36 PM","20","23","47","Operational","Crew","8","17","49","18","None","ZDC","38898","201","4314","64","2292","14532623.73","85","92","6197","1","1","ATC574","2959","2410","430","18","0","64","2","9","1"
+"14","FL6318","NKS","E175","N8031OY","DFW","IAH","G34","9","TX4","5/4/2026 2:06:36 AM","5/4/2026 3:03:36 AM","5/4/2026 4:26:36 AM","5/4/2026 5:23:36 AM","20","18","57","Operational","Weather","5","23","103","24","Snow","ZOB","39676","593","7509","189","6899","1912657.39","74","92","5712","1","1","ATC759","2034","2252","455","15","1","84","3","9","7"
+"15","FL8680","AAL","B738","N9116","DEN","JFK","G4","26","TX9","5/19/2026 7:30:36 PM","5/19/2026 9:22:36 PM","5/19/2026 11:35:36 PM","5/20/2026 1:27:36 AM","9","23","112","Operational","Maintenance","3","20","37","34","Rain","ZNY","26932","1296","9956","79","305","3505127.8","77","86","5412","1","1","ATC276","1862","2961","431","14","0","100","48","20","16"
+"16","FL3604","NKS","CRJ9","N7531I","MIA","IAH","G24","1","TX7","3/12/2026 8:32:36 PM","3/12/2026 9:10:36 PM","3/12/2026 11:42:36 PM","3/13/2026 12:20:36 AM","15","6","38","Operational","Crew","1","25","283","9","Snow","ZNY","39690","1138","5705","55","5831","6805716.21","74","98","7010","1","1","ATC353","1536","1604","428","26","0","115","40","11","6"
+"17","FL8394","JBU","A321","N5195","SEA","MIA","G36","24","TX15","3/3/2026 9:04:36 AM","3/3/2026 9:06:36 AM","3/3/2026 11:52:36 AM","3/3/2026 11:54:36 AM","21","26","2","Operational","Crew","6","20","331","19","Rain","ZFW","31784","628","7657","215","5390","8169596.43","87","80","7210","1","0","ATC856","2319","2968","501","0","0","34","37","11","23"
+"18","FL8079","DAL","B763","N7835XP","DEN","LAX","G37","3","TX10","2/19/2026 4:05:36 AM","2/19/2026 4:27:36 AM","2/19/2026 7:22:36 AM","2/19/2026 7:44:36 AM","35","23","22","Operational","Weather","6","17","189","-19","Snow","ZNY","21387","1653","2362","126","2810","4726618.72","29","81","6055","1","0","ATC877","3026","2545","470","5","1","100","2","19","19"
+"19","FL1598","UAL","A320","N7413P","DFW","IAH","G23","26","TX12","2/6/2026 7:42:36 PM","2/6/2026 8:52:36 PM","2/6/2026 11:14:36 PM","2/7/2026 12:24:36 AM","9","10","70","Operational","Weather","1","26","141","-20","Snow","ZNY","21977","1296","2054","56","2029","12085422.29","50","92","5297","1","0","ATC667","1039","2994","440","8","0","34","32","18","4"
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+"9995","FL7022","DAL","E175","N3427","MIA","ORD","G4","9","TX10","4/16/2026 3:06:41 PM","4/16/2026 3:38:41 PM","4/16/2026 4:09:41 PM","4/16/2026 4:41:41 PM","13","24","32","Operational","Weather","2","7","79","-10","Fog","ZOB","26221","960","11855","174","2746","12438998.74","42","93","4905","1","0","ATC781","2500","1552","497","7","1","36","2","2","12"
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diff --git a/docs/explanation/features/CONVERSATION_EXPORT.md b/docs/explanation/features/CONVERSATION_EXPORT.md
index c56d261a..a674dca4 100644
--- a/docs/explanation/features/CONVERSATION_EXPORT.md
+++ b/docs/explanation/features/CONVERSATION_EXPORT.md
@@ -1,139 +1,186 @@
 # Conversation Export
 
 ## Overview
-The Conversation Export feature allows users to export one or multiple conversations directly from the Chats experience. A multi-step wizard modal guides users through format selection, output packaging, and downloading the final file.
+The Conversation Export feature lets users export one or more chats from the Chats experience as JSON, Markdown, or PDF. The export now mirrors the live conversation view more closely by excluding deleted and inactive-thread messages, including processing thoughts, and preserving the modern citation buckets used by the chat UI.
 
-**Version Implemented:** 0.237.050
+**Version Implemented:** 0.239.022 (base), 0.239.023 (PDF export), 0.239.030 (persistent summaries)
 
 ## Dependencies
-- Flask (backend route)
-- Azure Cosmos DB (conversation and message storage)
-- Bootstrap 5 (modal, step indicators, cards)
-- ES modules (chat-export.js)
+- Flask backend route and file responses
+- Azure Cosmos DB conversation, message, and thought containers
+- Bootstrap 5 modal workflow
+- ES modules in `static/js/chat/chat-export.js`
+- Azure OpenAI / APIM-backed chat model access for optional intro summaries
+- PyMuPDF (fitz) for HTML-to-PDF rendering via the Story API
+
+## Implemented in version: **0.239.030**
 
 ## Architecture Overview
 
 ### Backend
-- **Route file:** `route_backend_conversation_export.py`
+- **Route file:** `application/single_app/route_backend_conversation_export.py`
 - **Endpoint:** `POST /api/conversations/export`
-- **Registration:** Called via `register_route_backend_conversation_export(app)` in `app.py`
+- **Registration:** `register_route_backend_conversation_export(app)`
+
+The export route now:
+- verifies the current user owns each requested conversation
+- loads messages ordered by timestamp, then reapplies thread ordering to match the chat UI
+- removes soft-deleted messages and inactive-thread retries
+- joins processing thoughts from the thoughts container by `message_id`
+- builds both normalized citation summaries and raw citation buckets
+- optionally generates a per-conversation intro summary using the selected chat model
+
+### Frontend
+- **JS module:** `application/single_app/static/js/chat/chat-export.js`
+- **Modal host:** `application/single_app/templates/chats.html`
+- **Entry point:** `window.chatExport.openExportWizard(conversationIds, skipSelection)`
+
+The wizard now has up to 5 steps:
+1. **Selection** — Review selected conversations (multi-select exports only)
+2. **Format** — Choose JSON, Markdown, or PDF
+3. **Packaging** — Choose a single file or ZIP archive
+4. **Summary** — Optionally generate a per-conversation intro summary and choose the summary model from the same model list used in the chat composer
+5. **Download** — Review settings and download the export
+
+## Request Payload
 
-The endpoint accepts a JSON body with:
 | Field | Type | Description |
 |---|---|---|
-| `conversation_ids` | list[str] | IDs of conversations to export |
-| `format` | string | `"json"` or `"markdown"` |
+| `conversation_ids` | list[str] | Conversation IDs to export |
+| `format` | string | `"json"`, `"markdown"`, or `"pdf"` |
 | `packaging` | string | `"single"` or `"zip"` |
-
-The server verifies user ownership of each conversation, fetches messages from Cosmos DB, filters for active thread messages, sanitizes internal fields, and returns either a single file or ZIP archive as a binary download.
-
-### Frontend
-- **JS module:** `static/js/chat/chat-export.js`
-- **Modal HTML:** Embedded in `templates/chats.html` (`#export-wizard-modal`)
-- **Global API:** `window.chatExport.openExportWizard(conversationIds, skipSelection)`
-
-The wizard has up to 4 steps:
-1. **Selection Review** — Shows selected conversations with titles (skipped for single-conversation export)
-2. **Format** — Choose between JSON and Markdown via action-type cards
-3. **Packaging** — Choose between single file and ZIP archive
-4. **Download** — Summary and download button
-
-## Entry Points
-
-### Single Conversation Export
-- **Sidebar ellipsis menu** → "Export" item (in `chat-sidebar-conversations.js`)
-- **Left-pane ellipsis menu** → "Export" item (in `chat-conversations.js`)
-- Both call `window.chatExport.openExportWizard([conversationId], true)` — skips the selection step
-
-### Multi-Conversation Export
-- Enter selection mode by clicking "Select" on any conversation
-- Select multiple conversations via checkboxes
-- Click the export button in:
-  - **Left-pane header** — `#export-selected-btn` (btn-info, download icon)
-  - **Sidebar actions bar** — `#sidebar-export-selected-btn`
-- These call `window.chatExport.openExportWizard(selectedIds, false)` — shows all 4 steps
+| `include_summary_intro` | boolean | Whether to generate a per-conversation intro summary |
+| `summary_model_deployment` | string | Optional selected model deployment for the summary intro |
 
 ## Export Formats
 
 ### JSON
-Produces a JSON array where each entry contains:
-```json
-{
-  "conversation": {
-    "id": "...",
-    "title": "...",
-    "last_updated": "...",
-    "chat_type": "...",
-    "tags": [],
-    "is_pinned": false,
-    "context": []
-  },
-  "messages": [
-    {
-      "role": "user",
-      "content": "...",
-      "timestamp": "...",
-      "citations": []
-    }
-  ]
-}
-```
+Each exported conversation entry contains:
+- `conversation` metadata and aggregate counts
+- `summary_intro` status and content (when enabled)
+- `messages` with:
+  - raw `content`
+  - normalized `content_text`
+  - curated `details`
+  - normalized `citations`
+  - raw `legacy_citations`, `hybrid_citations`, `web_search_citations`, and `agent_citations`
+  - nested `thoughts`
+
+This makes the JSON export useful both for readable inspection and downstream processing.
 
 ### Markdown
-Produces a Markdown document with:
-- `# Title` heading
-- Metadata block (last updated, chat type, tags, message count)
-- `### Role` sections per message with timestamps
-- Citation lists where applicable
-- `---` separators between messages and conversations
+Markdown exports are now organized like a lightweight report:
+- conversation title and metadata header
+- optional **Abstract** section generated by the selected model
+- **Transcript** section with clean user/assistant turns only
+- appendix sections for:
+  - conversation metadata
+  - curated message details
+  - references/citations
+  - processing thoughts
+  - supplemental non-transcript messages such as file or system records
+
+This keeps the main body readable while moving verbose reference material to the end of the document.
+
+### PDF
+PDF exports render the conversation as a print-ready document with chat-bubble styling that visually resembles the live chat interface:
+- **User messages** are displayed in blue bubbles (#c8e0fa) aligned to the right
+- **Assistant messages** are displayed in gray bubbles (#f1f0f0) aligned to the left
+- **System and file messages** use distinct background colors
+- Message content is converted from Markdown to HTML for rich formatting
+- The same appendix structure as Markdown is included (metadata, details, references, thoughts, supplemental messages)
+- PDF rendering uses PyMuPDF's Story API on US Letter paper (612 × 792pt) with 0.5-inch margins
+- Multi-page content is handled automatically with page overflow
+
+## Citation Handling
+
+The export supports the same citation categories used in the live chat UI:
+- **Document citations** from `hybrid_citations`
+- **Web citations** from `web_search_citations`
+- **Agent/tool citations** from `agent_citations`
+- **Legacy citations** for backwards-compatible older messages
+
+Normalized citation summaries provide a single export-friendly list, while the raw buckets preserve the original structured data.
+
+## Thoughts Handling
+
+If processing thoughts are enabled and available, they are exported with the assistant message they belong to. Each thought includes:
+- `step_index`
+- `step_type`
+- `content`
+- `detail`
+- `duration_ms`
+- `timestamp`
+
+Markdown exports place these in the **Processing Thoughts** appendix.
 
 ## Output Packaging
 
 ### Single File
-- One file containing all selected conversations
-- JSON: `.json` file
-- Markdown: `.md` file with `---` separators between conversations
+- JSON exports combine all selected conversations into one `.json` file
+- Markdown exports combine all selected conversations into one `.md` file separated by `---`
+- PDF exports combine all selected conversations into one `.pdf` file with visual separators between conversations
 
 ### ZIP Archive
-- One file per conversation inside a `.zip`
-- Filenames: `{sanitized_title}_{id_prefix}.{ext}`
-- Titles are sanitized for filesystem safety (special chars replaced, truncated to 50 chars)
-
-## File Structure
-```
-application/single_app/
-├── route_backend_conversation_export.py   # Backend API endpoint
-├── app.py                                  # Route registration
-├── static/js/chat/
-│   ├── chat-export.js                     # Export wizard module
-│   ├── chat-conversations.js              # Left-pane wiring
-│   └── chat-sidebar-conversations.js      # Sidebar wiring
-├── templates/
-│   ├── chats.html                         # Modal HTML + button + script
-│   ├── _sidebar_nav.html                  # Sidebar export button
-│   └── _sidebar_short_nav.html            # Short sidebar export button
-functional_tests/
-└── test_conversation_export.py            # Functional tests
-```
-
-## Security
-- Endpoint requires `@login_required` and `@user_required` decorators
-- Each conversation is verified for user ownership before export
-- Internal Cosmos DB fields (`_rid`, `_self`, `_etag`, `user_id`, etc.) are stripped from output
-- No sensitive data is included in the export
+- Each conversation is written to its own file in the archive
+- Filenames use `{sanitized_title}_{conversation_id_prefix}.{ext}`
+- Summary intros remain per conversation in both JSON and Markdown archive entries
+
+## Persistent Conversation Summaries (v0.239.030)
+
+Summaries generated during export or on demand are now persisted to the conversation document in Cosmos DB and reused automatically.
+
+### How Caching Works
+- Each summary stores `message_time_start` and `message_time_end` from the messages it was built from.
+- On subsequent exports, `_build_summary_intro` compares the cached `message_time_end` against the latest message timestamp. If no new messages exist, the cached summary is returned instantly.
+- If new messages exist beyond the cached range, a fresh summary is generated, saved, and returned.
+
+### On-Demand Generation
+- The conversation details modal (opened from the sidebar) now includes a **Summary** card.
+- If no summary exists, a **Generate Summary** button with a model selector lets users create one without exporting.
+- If a summary exists, the content, generation date, and model are displayed with a **Regenerate** button.
+- New API endpoint: `POST /api/conversations/<id>/summary` with optional `{ "model_deployment": "..." }` body.
+
+### Reusable Helper
+- `generate_conversation_summary()` (in `route_backend_conversation_export.py`) is the shared LLM call function used by both the export pipeline and the API endpoint.
+- It handles transcript assembly, truncation, model role selection (developer vs system), and Cosmos persistence.
+
+## Security and Data-Shaping Rules
+- Route uses authenticated user checks before export
+- Only user-owned personal conversations are exported
+- Internal Cosmos metadata is not passed through
+- Deleted messages and inactive-thread retries are excluded
+- Exported message details are curated rather than raw metadata dumps
+- Raw settings are not exposed to the browser; the export wizard reuses the existing chat model selector already rendered from sanitized settings
+
+## Files Updated
+- `application/single_app/route_backend_conversation_export.py`
+- `application/single_app/route_backend_conversations.py`
+- `application/single_app/static/js/chat/chat-export.js`
+- `application/single_app/static/js/chat/chat-conversation-details.js`
+- `application/single_app/config.py`
+- `functional_tests/test_conversation_export.py`
+- `functional_tests/test_persistent_conversation_summary.py`
 
 ## Testing and Validation
-- **Functional test:** `functional_tests/test_conversation_export.py`
-- Tests cover:
-  - Conversation sanitization (internal field stripping)
-  - Message sanitization
-  - Markdown generation (headings, metadata, citations)
-  - JSON structure validation
-  - ZIP packaging (correct entries, valid content)
-  - Filename sanitization (special chars, truncation, empty input)
-  - Active thread message filtering
+- **Functional tests:**
+  - `functional_tests/test_conversation_export.py` — export pipeline (8 tests)
+  - `functional_tests/test_persistent_conversation_summary.py` — summary caching (8 tests)
+
+Coverage includes:
+- deleted/inactive message filtering
+- normalized and raw citation export
+- thoughts attached to assistant messages
+- transcript-style Markdown appendices
+- summary-intro metadata shape
+- filename sanitization and ZIP naming
+- content normalization and citation-count helpers
+- PDF HTML body generation with chat-bubble classes
+- PDF byte output validation (%PDF- header)
 
 ## Known Limitations
-- Export is limited to conversations the authenticated user owns
-- Very large conversations (thousands of messages) may take longer to process
-- The wizard fetches conversation titles client-side; if a title lookup fails, it shows the conversation ID instead
+- Export remains limited to user-owned personal conversations
+- Optional intro summaries increase export time because each selected conversation is summarized individually
+- Very large conversations may be truncated for summary generation, though the full export content is still preserved in the output
+- Summary generation is best-effort: if model initialization or generation fails, the export still completes and records the summary error state
+- PDF rendering relies on PyMuPDF’s CSS subset which does not support flexbox, float, or full border-radius; bubble alignment is approximated using margin offsets
diff --git a/docs/explanation/features/v0.239.003/PROCESSING_THOUGHTS.md b/docs/explanation/features/v0.239.003/PROCESSING_THOUGHTS.md
new file mode 100644
index 00000000..5dad56d9
--- /dev/null
+++ b/docs/explanation/features/v0.239.003/PROCESSING_THOUGHTS.md
@@ -0,0 +1,153 @@
+---
+layout: libdoc/page
+title: Processing Thoughts
+order: 100
+category: Features
+---
+
+# Processing Thoughts
+
+## Overview
+The Processing Thoughts feature replaces the generic "AI is typing..." indicator with real-time processing step traces that show users what the system is doing during chat processing. Each step (document search, web search, agent invocation, content safety check, response generation) is persisted in Cosmos DB and can be reviewed later via a per-message collapsible section.
+
+**Version Implemented:** 0.239.003
+
+## Dependencies
+- Flask (backend routes)
+- Azure Cosmos DB (`thoughts` and `archive_thoughts` containers)
+- Bootstrap 5 (collapsible section, badges, icons)
+- ES modules (`chat-thoughts.js`)
+
+## Architecture Overview
+
+### Backend
+
+#### ThoughtTracker (`functions_thoughts.py`)
+Stateful per-request tracker that writes each thought step to Cosmos DB immediately so polling clients can see partial progress.
+
+```
+ThoughtTracker(conversation_id, message_id, thread_id, user_id)
+  .add_thought(step_type, content, detail=None) → thought_id
+  .complete_thought(thought_id, duration_ms)     → updates duration
+  .enabled                                        → checks settings['enable_thoughts']
+```
+
+Design rules:
+- Each `add_thought()` does an immediate `upsert_item()` to Cosmos DB.
+- All writes are wrapped in try/except — thought errors never crash the chat flow.
+- Auto-increments `step_index` per tracker instance.
+- Logs failures via `log_event()` at WARNING level.
+
+#### Thought Document Schema
+```json
+{
+    "id": "uuid",
+    "conversation_id": "str",
+    "message_id": "str (assistant message ID)",
+    "thread_id": "str",
+    "user_id": "str (partition key)",
+    "step_index": 0,
+    "step_type": "search | tabular_analysis | web_search | agent_tool_call | generation | content_safety",
+    "content": "Searching personal workspace documents for 'sales analysis'...",
+    "detail": "Optional technical detail",
+    "duration_ms": null,
+    "timestamp": "ISO-8601"
+}
+```
+
+#### API Endpoints (`route_backend_thoughts.py`)
+
+| Method | Endpoint | Purpose |
+|--------|----------|---------|
+| GET | `/api/conversations/<id>/messages/<id>/thoughts` | Fetch persisted thoughts for a specific assistant message (historical viewing) |
+| GET | `/api/conversations/<id>/thoughts/pending` | Fetch latest in-progress thoughts (polling while waiting for response) |
+
+Both endpoints return `{"thoughts": [...], "enabled": true/false}`. When `enable_thoughts` is off, they return `{"thoughts": [], "enabled": false}`.
+
+#### Instrumentation Points (`route_backend_chats.py`)
+
+| Step Type | Content Example | When |
+|-----------|----------------|------|
+| `content_safety` | "Checking content safety..." | Before content safety check |
+| `search` | "Searching personal documents for 'query'..." | Before hybrid search |
+| `search` | "Found 5 results from 3 documents" | After search results |
+| `tabular_analysis` | "Found tabular data — evaluating analysis..." | When tabular data detected |
+| `web_search` | "Searching the web for 'query'..." | Before web search |
+| `web_search` | "Got 8 web results" | After web search |
+| `agent_tool_call` | "Sending to agent 'Data Analyst'..." | Before agent invocation |
+| `generation` | "Generating response..." | Before GPT call |
+
+### Frontend
+
+#### Streaming Mode
+Thought events are embedded in the SSE stream as `{"type": "thought", ...}` JSON payloads. The streaming handler in `chat-streaming.js` passes these to `handleStreamingThought()` which updates the streaming placeholder badge.
+
+#### Non-Streaming Mode
+A polling mechanism in `chat-thoughts.js` fetches `/thoughts/pending` every 2 seconds while waiting for a response. The loading indicator text is updated with the latest thought step.
+
+#### Per-Message History
+Each assistant message footer includes a lightbulb toggle button (when thoughts exist). Clicking it opens a collapsible section that lazy-loads thoughts from the API. Each step shows an icon, content text, and optional duration.
+
+#### Icon Map
+| Step Type | Bootstrap Icon |
+|-----------|---------------|
+| `search` | `bi-search` |
+| `tabular_analysis` | `bi-table` |
+| `web_search` | `bi-globe` |
+| `agent_tool_call` | `bi-robot` |
+| `generation` | `bi-lightning` |
+| `content_safety` | `bi-shield-check` |
+
+## Configuration
+
+### Admin Settings
+- **Toggle**: `enable_thoughts` (default: `false`)
+- **Location**: Admin Settings > Optional Features tab > "Processing Thoughts" section
+- **Effect**: When disabled, no thoughts are recorded and no UI elements are shown
+
+### Cosmos DB Containers
+| Container | Partition Key | Purpose |
+|-----------|--------------|---------|
+| `thoughts` | `/user_id` | Active thought records |
+| `archive_thoughts` | `/user_id` | Archived thoughts from deleted conversations |
+
+## Archive and Cleanup
+
+When a conversation is deleted:
+- **Archiving enabled**: Thoughts are copied to `archive_thoughts` container, then deleted from `thoughts`
+- **Archiving disabled**: Thoughts are permanently deleted from `thoughts`
+
+This applies to both single conversation delete and bulk delete operations.
+
+## File Structure
+
+### Files Created
+| File | Purpose |
+|------|---------|
+| `functions_thoughts.py` | ThoughtTracker class, Cosmos CRUD helpers |
+| `route_backend_thoughts.py` | API endpoints for fetching thoughts |
+| `static/js/chat/chat-thoughts.js` | Frontend polling, rendering, toggle |
+
+### Files Modified
+| File | Change |
+|------|--------|
+| `config.py` | Added `thoughts` + `archive_thoughts` Cosmos containers, bumped VERSION |
+| `functions_settings.py` | Added `enable_thoughts` default setting |
+| `app.py` | Imported and registered thought routes |
+| `route_backend_chats.py` | Instrumented ~8 thought points per chat path |
+| `route_backend_conversations.py` | Added archive/delete thoughts on conversation delete |
+| `templates/admin_settings.html` | Added Processing Thoughts toggle card |
+| `static/js/admin/admin_settings.js` | Added `enable_thoughts` to settings collection |
+| `static/js/chat/chat-messages.js` | Integrated thoughts toggle in footer, polling start/stop |
+| `static/js/chat/chat-streaming.js` | Handle `type: "thought"` in SSE data |
+| `static/js/chat/chat-loading-indicator.js` | Added `updateLoadingIndicatorText()` for thought display |
+| `static/css/chats.css` | Added thought indicator, toggle, container, and dark mode styles |
+
+## Testing
+
+1. **Enable feature**: Set `enable_thoughts: True` in admin settings
+2. **Non-streaming**: Send a message with document search — verify loading indicator updates with thought steps, lightbulb icon appears after response
+3. **Streaming**: Send a message — verify streaming placeholder shows thought badges, lightbulb available after finalization
+4. **History**: Reload page, open old conversation — click lightbulb to verify lazy-loaded thoughts
+5. **Disabled**: Set `enable_thoughts: False` — verify no thoughts generated, no lightbulb icons
+6. **Archive**: Delete a conversation with archiving enabled — verify thoughts moved to `archive_thoughts`
diff --git a/docs/explanation/features/v0.239.022/CONVERSATION_EXPORT.md b/docs/explanation/features/v0.239.022/CONVERSATION_EXPORT.md
new file mode 100644
index 00000000..edb69c2e
--- /dev/null
+++ b/docs/explanation/features/v0.239.022/CONVERSATION_EXPORT.md
@@ -0,0 +1,58 @@
+# Conversation Export
+
+## Overview
+Snapshot of the Conversation Export feature as implemented in version **0.239.022**.
+
+This version updates export generation so JSON includes modern citation buckets, normalized citation summaries, and processing thoughts, while Markdown becomes a transcript-first report with appendix sections and optional AI-generated intro summaries.
+
+**Version Implemented:** 0.239.022
+**Dependencies:** Flask export route, Azure Cosmos DB conversations/messages/thoughts, Bootstrap modal workflow, chat-export.js, Azure OpenAI/APIM chat models
+
+## Technical Summary
+
+### Backend
+- Filters out deleted messages and inactive-thread retries
+- Reapplies thread-aware ordering to align with the live chat view
+- Includes both normalized and raw citations per message
+- Joins persisted processing thoughts by `message_id`
+- Supports optional per-conversation `summary_intro` generation using a selected model
+
+### Frontend
+- Adds a summary step to the export wizard
+- Lets users enable or disable intro summaries
+- Reuses the existing chat model selector options for summary model choice
+
+## Export Shape
+
+### JSON
+Each conversation entry contains:
+- `conversation`
+- `summary_intro`
+- `messages`
+
+Each message can include:
+- `content`
+- `content_text`
+- `details`
+- `citations`
+- `legacy_citations`
+- `hybrid_citations`
+- `web_search_citations`
+- `agent_citations`
+- `thoughts`
+
+### Markdown
+Markdown exports contain:
+- metadata header
+- optional abstract
+- transcript body
+- appendices for metadata, message details, references, thoughts, and supplemental messages
+
+## Files Updated
+- `application/single_app/route_backend_conversation_export.py`
+- `application/single_app/static/js/chat/chat-export.js`
+- `application/single_app/config.py`
+- `functional_tests/test_conversation_export.py`
+
+## Testing
+Validated by `functional_tests/test_conversation_export.py`.
diff --git a/docs/explanation/fixes/CHAT_WORKSPACE_SELECTION_RESET_FIX.md b/docs/explanation/fixes/CHAT_WORKSPACE_SELECTION_RESET_FIX.md
new file mode 100644
index 00000000..4f2fe8d2
--- /dev/null
+++ b/docs/explanation/fixes/CHAT_WORKSPACE_SELECTION_RESET_FIX.md
@@ -0,0 +1,55 @@
+# Chat Workspace Selection Reset Fix
+
+## Fix Title
+Implicit chat conversation creation now preserves selected workspace scope, tags, and documents.
+
+## Issue Description
+When a user arrived on the chat page with workspace context already selected, either by choosing documents manually or by coming from a workspace link that preselected scope, tags, or documents, clicking into the message input created a conversation and immediately reset the workspace filters back to their defaults.
+
+## Root Cause Analysis
+
+1. The chat bootstrap in `chat-onload.js` auto-created a conversation on first input focus when no conversation ID existed.
+2. `createNewConversation()` in `chat-conversations.js` always called `resetScopeLock()` in full reset mode, which restored the scope dropdown to `All` and reloaded document and tag controls.
+3. That full reset path rebuilt the document and tag UI in `chat-documents.js`, which cleared the preselected workspace context before the user sent the first message.
+
+## Version Implemented
+Fixed in version: **0.239.105**
+
+## Files Modified
+
+| File | Change |
+|------|--------|
+| `application/single_app/static/js/chat/chat-documents.js` | Added a preserve-selection mode to `resetScopeLock()` so lock state can be cleared without rebuilding current scope, tag, and document selections. |
+| `application/single_app/static/js/chat/chat-conversations.js` | Added a `preserveSelections` option to `createNewConversation()` and reused in-flight create requests so implicit creation does not race duplicate conversations. |
+| `application/single_app/static/js/chat/chat-onload.js` | Changed implicit auto-create entry points for input focus, prompt selection, and file selection to preserve current workspace filters. |
+| `application/single_app/static/js/chat/chat-input-actions.js` | Updated file-upload auto-create flows to preserve workspace selections. |
+| `application/single_app/static/js/chat/chat-messages.js` | Updated the first-send auto-create flow to preserve workspace selections. |
+| `functional_tests/test_chat_preserves_workspace_selection_on_auto_create.py` | Added regression coverage for preserve-selection reset logic and implicit conversation creation call sites. |
+| `application/single_app/config.py` | Version bump to `0.239.105`. |
+
+## Code Changes Summary
+
+### Preserve Selection State During Implicit Auto-Create
+- Added an options-based preserve path to the scope reset helper so a new conversation can start unlocked without forcing the workspace picker back to `All`.
+- Updated implicit conversation creation flows to request preserved selections.
+
+### Prevent Duplicate Conversation Creation
+- Reused a single in-flight create-conversation request so focus-triggered creation and an immediate send action do not create duplicate empty conversations.
+
+### Keep Explicit Reset Behavior Intact
+- Left the explicit `New Conversation` button on the default reset path so a deliberate fresh chat still restores default workspace scope.
+
+## Testing Approach
+- Added functional regression coverage in `functional_tests/test_chat_preserves_workspace_selection_on_auto_create.py`.
+- Validates that `resetScopeLock()` supports a preserve-selection path.
+- Validates that `createNewConversation()` preserves selections when requested and reuses a pending create request.
+- Validates that implicit creation call sites in the chat bootstrap, first-send flow, and file upload flow all request preserved selections.
+
+## Impact Analysis
+- Users can now click into the message input and continue with the workspace, tag, and document filters they already chose.
+- Workspace deep links remain stable through the first interaction instead of reverting to the default chat scope.
+- Explicit new chat creation still resets to the default workspace scope, so existing fresh-start behavior remains available.
+
+## Validation
+- Before: first focus in the message box could silently reset workspace-related filters before the user sent a message.
+- After: implicit conversation creation preserves the active workspace context, while explicit new chat creation keeps the full reset behavior.
\ No newline at end of file
diff --git a/docs/explanation/fixes/GLOBAL_ACTION_AUDIT_USER_FALLBACK_FIX.md b/docs/explanation/fixes/GLOBAL_ACTION_AUDIT_USER_FALLBACK_FIX.md
new file mode 100644
index 00000000..011091f2
--- /dev/null
+++ b/docs/explanation/fixes/GLOBAL_ACTION_AUDIT_USER_FALLBACK_FIX.md
@@ -0,0 +1,45 @@
+# Global Action Audit User Fallback Fix
+
+## Fix Title
+Global Action Save Path Defaults Missing Audit User IDs
+
+## Issue Description
+`save_global_action()` accepted an optional `user_id`, but callers that omitted it could persist `created_by` and `modified_by` as `null`. This affected flows such as plugin validation repair, which saves plugin manifests through the global action helper without explicitly passing a user ID.
+
+## Root Cause Analysis
+- `save_global_action()` never mirrored the existing `save_global_agent()` behavior that resolves `user_id` through `get_current_user_id()` when the caller passes `None`.
+- The helper wrote audit fields directly from the unresolved `user_id`, so create operations stored `null` values.
+- Update operations preserved an existing `created_by` value even when it was already `null`, which meant previously corrupted audit data could survive indefinitely.
+
+## Version Implemented
+Fixed in version: **0.239.103**
+
+## Files Modified
+| File | Change |
+|------|--------|
+| `application/single_app/functions_global_actions.py` | Default missing `user_id` from `get_current_user_id()`, fall back to `system`, and repair null `created_by` on update |
+| `functional_tests/test_global_action_user_audit_fallback.py` | Added regression coverage for create and update audit-field fallback behavior |
+| `application/single_app/config.py` | Version bump to 0.239.103 |
+
+## Code Changes Summary
+- Imported `get_current_user_id()` into `functions_global_actions.py`.
+- When `user_id` is `None`, the helper now resolves the current authenticated user.
+- If no authenticated user is available, the helper falls back to `system` so audit fields remain non-null.
+- Existing actions with `created_by=None` are repaired on save by substituting the resolved fallback value.
+
+## Testing Approach
+- Added `functional_tests/test_global_action_user_audit_fallback.py`.
+- The test stubs the config, authentication, and Key Vault dependencies so it can exercise `save_global_action()` directly.
+- Coverage verifies both:
+  - Create flow uses `get_current_user_id()` when `user_id` is omitted.
+  - Update flow repairs a previously null `created_by` and falls back to `system` when no current user exists.
+
+## Impact Analysis
+- Global plugin/action saves now produce stable audit metadata even for internal or repair flows that do not pass a user ID explicitly.
+- Existing global actions with missing `created_by` values are corrected the next time they are saved.
+- No route or payload contract changes were introduced.
+
+## Validation
+- Regression test: `functional_tests/test_global_action_user_audit_fallback.py`
+- Before: `created_by` and `modified_by` could be stored as `null`.
+- After: both fields resolve to the current user ID or `system`.
\ No newline at end of file
diff --git a/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md b/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md
new file mode 100644
index 00000000..9713a62c
--- /dev/null
+++ b/docs/explanation/fixes/SQL_QUERY_PLUGIN_SCHEMA_AWARENESS_FIX.md
@@ -0,0 +1,160 @@
+# SQL Query Plugin Schema Awareness Fix
+
+## Fix Title
+SQL Query Plugin - Schema Awareness, Companion Plugin Auto-Creation, and Workflow Guidance
+
+## Issue Description
+When users asked database-related questions (e.g., "what is user1 licensed to use?"), agents connected to SQL databases would ask for clarification about table/column names instead of querying the database directly. The agent had no citations, meaning it never actually called any database tools.
+
+## Root Cause Analysis
+
+### Original Root Causes (v0.239.014)
+Three interconnected issues caused the initial failure:
+
+1. **Generic `@kernel_function` descriptions**: The SQL Query and SQL Schema plugin function descriptions were terse and generic (e.g., "Execute a SQL query and return results"). They provided no workflow guidance telling the LLM to discover the schema first before writing queries.
+
+2. **No schema context in agent instructions**: Agent instructions were passed through verbatim from configuration with no automatic injection of database schema information.
+
+3. **Independent, disconnected plugins**: The SQL Schema Plugin and SQL Query Plugin operated as completely independent plugins with no linkage.
+
+### Deeper Root Causes Discovered (v0.239.015)
+The v0.239.014 fix improved descriptions but actually made things **worse** because:
+
+4. **No companion schema plugin was ever loaded**: The ESAM agent only had ONE action configured (`sql_query` type). No `sql_schema` action existed in the agent's actions. The `_create_sql_plugin()` method creates exactly what the manifest requests — so only `SQLQueryPlugin` was loaded, never `SQLSchemaPlugin`.
+
+5. **Descriptions demanded non-existent functions**: The v0.239.014 descriptions said "you MUST first call get_database_schema or get_table_list from the SQL Schema plugin" — but those functions didn't exist in the kernel since no schema plugin was loaded. This created an **impossible dependency** that made the LLM ask for clarification instead.
+
+6. **Schema extraction found nothing**: `_extract_sql_schema_for_instructions()` only searched for `SQLSchemaPlugin` instances. Since none existed in the kernel, it returned an empty string, so no schema was injected into agent instructions.
+
+7. **SQLPluginFactory was disconnected**: The `SQLPluginFactory` class was designed to create `(SQLSchemaPlugin, SQLQueryPlugin)` pairs, but was never called by the `LoggedPluginLoader` pipeline.
+
+### Empty Schema Tables from INFORMATION_SCHEMA (v0.239.016)
+After the v0.239.015 fix, the agent could answer simple queries (e.g., "what is user1 licensed to use?" correctly returned Office 365 license data). However, complex multi-table JOIN queries (e.g., "which department is spending the most on licensing?") still failed because:
+
+8. **INFORMATION_SCHEMA views returned empty results on Azure SQL**: The `_get_tables_query()`, `_get_columns_query()`, and `_get_primary_keys_query()` methods used `INFORMATION_SCHEMA.TABLES`, `INFORMATION_SCHEMA.COLUMNS`, and `INFORMATION_SCHEMA.KEY_COLUMN_USAGE` respectively. These views returned **zero rows** in this Azure SQL environment, even though the database contained 5 user tables.
+
+9. **sys.\* catalog views worked correctly**: The `_get_relationships_data()` method used `sys.foreign_keys`, `sys.tables`, and `sys.columns` — and successfully returned 4 foreign key relationships. This proved the database connection and permissions were fine, but `INFORMATION_SCHEMA` access was restricted or misconfigured.
+
+10. **pyodbc.Row type mismatch**: The table iteration code used `isinstance(table, tuple)` to check row types, but `pyodbc.Row` objects may not pass this check depending on the pyodbc version. When `isinstance` returned `False`, the code fell into an `else` branch that assigned the entire Row object as the table name, causing subsequent SQL queries to fail silently in the exception handler.
+
+11. **Result**: `get_database_schema` returned `{'tables': {}, 'relationships': [4 items]}` — the agent had foreign key metadata but no table/column definitions, making it impossible to construct multi-table JOINs.
+
+## Version Implemented
+**Initial fix in version: 0.239.014**
+**Companion plugin fix in version: 0.239.015**
+**Schema catalog views fix in version: 0.239.016**
+
+## Files Modified
+
+| File | Change |
+|------|--------|
+| `application/single_app/semantic_kernel_plugins/sql_schema_plugin.py` | Rewrote all `@kernel_function` descriptions with prescriptive workflow guidance (v0.239.014); migrated all SQL Server queries from INFORMATION_SCHEMA to sys.\* catalog views and fixed pyodbc.Row handling (v0.239.016) |
+| `application/single_app/semantic_kernel_plugins/sql_query_plugin.py` | Rewrote all `@kernel_function` descriptions with resilient conditional guidance (v0.239.015); added `query_database` convenience function (v0.239.014); updated `metadata` property description |
+| `application/single_app/semantic_kernel_loader.py` | Added `_extract_sql_schema_for_instructions()` helper function; auto-injects database schema into agent instructions; added SQLQueryPlugin fallback detection (v0.239.015) |
+| `application/single_app/semantic_kernel_plugins/logged_plugin_loader.py` | Enabled SQL plugin creation path (v0.239.014); added `_auto_create_companion_schema_plugin()` method that auto-creates a SQLSchemaPlugin whenever a SQLQueryPlugin is loaded (v0.239.015) |
+| `application/single_app/config.py` | Version bump to 0.239.016 |
+
+## Code Changes Summary
+
+### v0.239.014 Changes
+
+#### 1. Prescriptive Function Descriptions (sql_schema_plugin.py)
+- `get_database_schema`: Now says "ALWAYS call this function FIRST before executing any SQL queries"
+- `get_table_list`: Now says "Use this function first to discover which tables are available"
+- `get_table_schema`: Now says "Call this after discovering tables via get_database_schema or get_table_list"
+- `get_relationships`: Now says "Use this to understand how tables connect via JOIN conditions"
+
+#### 2. New `query_database` Convenience Function (sql_query_plugin.py)
+- Accepts `question` (natural language) and `query` (SQL) parameters
+- Returns results with the original question context for better LLM response formatting
+
+#### 3. Auto Schema Injection (semantic_kernel_loader.py)
+- New `_extract_sql_schema_for_instructions()` function detects SQL Schema plugins in the kernel
+- Calls `get_database_schema()` at agent load time to fetch full schema
+- Formats schema as markdown tables (table names, columns, types, relationships)
+- Appends schema to agent instructions with directive: "Do NOT ask the user for table or column names"
+
+#### 4. Enabled SQL Plugin Creation Path (logged_plugin_loader.py)
+- Uncommented the `elif plugin_type in ['sql_schema', 'sql_query']` branch
+
+### v0.239.015 Changes (Complete Fix)
+
+#### 5. Auto-Create Companion Schema Plugin (logged_plugin_loader.py)
+- New `_auto_create_companion_schema_plugin()` method
+- When a `sql_query` plugin is loaded, automatically creates a companion `SQLSchemaPlugin` using the same connection details
+- Derives schema plugin name: `enterprise_software_asset_management_query` → `enterprise_software_asset_management_schema`
+- Checks if the companion already exists (idempotent)
+- Enables logging, wraps functions, registers with kernel
+- This is the **critical fix** — ensures schema discovery is always available even when only `sql_query` is configured
+
+#### 6. Resilient Function Descriptions (sql_query_plugin.py)
+- Changed from "you MUST first call get_database_schema" to "If the database schema is provided in your instructions, use those exact table and column names. If no schema is available, call get_database_schema"
+- This dual-path approach works whether schema is injected in instructions OR available via schema plugin functions
+
+#### 7. SQLQueryPlugin Fallback in Schema Extraction (semantic_kernel_loader.py)
+- Added fallback in `_extract_sql_schema_for_instructions()` that also detects `SQLQueryPlugin` instances
+- If no `SQLSchemaPlugin` is found, creates a temporary `SQLSchemaPlugin` from the query plugin's connection config
+- Belt-and-suspenders safety net in case companion auto-creation fails
+- Appends schema to agent instructions with directive: "Do NOT ask the user for table or column names"
+- This ensures the LLM ALWAYS has schema context even if it doesn't call the schema plugin
+
+### 4. Enabled SQL Plugin Creation Path (logged_plugin_loader.py)
+- Uncommented the `elif plugin_type in ['sql_schema', 'sql_query']` branch
+- SQL plugins now use the explicit `_create_sql_plugin()` method instead of generic discovery fallback
+
+### v0.239.016 Changes (Schema Catalog Views Fix)
+
+#### 8. Migrated SQL Server Queries to sys.\* Catalog Views (sql_schema_plugin.py)
+- **`_get_tables_query()`**: Replaced `INFORMATION_SCHEMA.TABLES` with `sys.tables t INNER JOIN sys.schemas s ON t.schema_id = s.schema_id WHERE t.type = 'U'`
+- **`_get_columns_query()`**: Replaced `INFORMATION_SCHEMA.COLUMNS` with `sys.columns c INNER JOIN sys.tables t ... LEFT JOIN sys.default_constraints dc ...` using `TYPE_NAME(c.user_type_id)` for data type resolution
+- **`_get_primary_keys_query()`**: Replaced `INFORMATION_SCHEMA.KEY_COLUMN_USAGE` with `sys.index_columns ic INNER JOIN sys.indexes i ... WHERE i.is_primary_key = 1`
+- This makes all SQL Server schema queries consistent with `_get_relationships_data()`, which already used sys.\* views successfully
+
+#### 9. Robust pyodbc.Row Handling (sql_schema_plugin.py)
+- **`get_database_schema()`**: Replaced `isinstance(table, tuple)` checks with try/except indexing; all row field values cast to `str()` before use as dict keys
+- **`get_table_list()`**: Same robust Row handling pattern applied to table row iteration
+- **`_get_table_schema_data()`**: Primary key list comprehension updated to use `str(pk[0])` without isinstance checks
+- This ensures the code works correctly regardless of whether `pyodbc.Row` inherits from `tuple` in the installed pyodbc version
+
+## Testing Approach
+- Functional test (v0.239.014): `functional_tests/test_sql_query_plugin_schema_awareness.py`
+- Functional test (v0.239.015): `functional_tests/test_sql_auto_schema_companion.py`
+- Validates `_auto_create_companion_schema_plugin` method exists with correct signature
+- Confirms companion creation is triggered in `load_plugin_from_manifest` for `sql_query` type
+- Verifies schema plugin name derivation logic (`_query` → `_schema` suffix swap)
+- Checks `@kernel_function` descriptions are resilient (no hard dependency on non-existent functions)
+- Validates `_extract_sql_schema_for_instructions` has SQLQueryPlugin fallback
+- Confirms version updated to 0.239.015
+- Functional test (v0.239.016): `functional_tests/test_sql_schema_sys_catalog_views.py`
+- Validates all SQL Server queries use `sys.tables`, `sys.columns`, `sys.indexes` instead of `INFORMATION_SCHEMA`
+- Confirms pyodbc.Row-safe iteration (no `isinstance(table, tuple)` checks)
+- Verifies primary key query uses `sys.index_columns` with `is_primary_key = 1`
+- Checks PostgreSQL/MySQL/SQLite queries remain unchanged
+- Confirms version updated to 0.239.016
+
+## Impact Analysis
+- **SQL-connected agents**: Will now automatically have BOTH a query plugin AND a companion schema plugin, even when only `sql_query` is configured. Schema is injected into agent instructions at load time.
+- **Non-SQL agents**: Completely unaffected (companion creation only triggers for `sql_query` type)
+- **LogAnalytics agents**: Unaffected (different plugin type)
+- **Performance**: One-time schema fetch at agent load time adds minimal latency; schema is cached in instructions for the session
+- **Backwards compatible**: If both `sql_query` and `sql_schema` actions are explicitly configured, the companion auto-creation is skipped (checks for existing plugin)
+
+## Before/After Comparison
+
+### Before (v0.239.014)
+- User: "What is user1 licensed to use?"
+- Agent: "I need the exact user identifier... Please provide the identifier..." (no database call, no citations)
+- Root cause: Descriptions demanded calling schema functions that didn't exist in the kernel
+
+### After v0.239.015
+- User: "What is user1 licensed to use?"
+- Agent: Correctly returns Office 365 license data with LicenseID 1, TotalQuantity 52 (simple single-table queries work)
+- User: "Which department is spending the most on licensing?"
+- Agent: Fails — says "I don't see a department dimension in the current schema" and calls `get_database_schema` which returns `{'tables': {}}` (empty)
+- Root cause: INFORMATION_SCHEMA views returned no results on Azure SQL
+
+### After v0.239.016
+- User: "What is user1 licensed to use?"
+- Agent: Correctly returns license data (still works)
+- User: "Which department is spending the most on licensing?"
+- Agent: Has full schema with all 5 tables and their columns → can construct multi-table JOINs (Licenses → Procurements for cost, Usage for department) → returns department-level spending analysis
diff --git a/docs/explanation/fixes/v0.239.008/CHAT_TABULAR_SK_TRIGGER_FIX.md b/docs/explanation/fixes/v0.239.008/CHAT_TABULAR_SK_TRIGGER_FIX.md
new file mode 100644
index 00000000..d525e2d1
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.008/CHAT_TABULAR_SK_TRIGGER_FIX.md
@@ -0,0 +1,67 @@
+# Chat-Uploaded Tabular File SK Mini-Agent Trigger Fix
+
+## Issue Description
+
+When a user uploads a tabular file (CSV, XLSX, XLS, XLSM) directly to a chat conversation and asks a question in model-only mode (no agent selected), the SK mini-agent (`run_tabular_sk_analysis`) did not trigger. The model would see instructions to "use plugin functions" but could not call them without an agent, resulting in the model describing what it would do instead of providing actual analysis results.
+
+The full agent mode worked correctly because the agent has direct access to the `TabularProcessingPlugin` and can call its functions.
+
+## Root Cause
+
+Three gaps prevented the mini SK agent from activating for chat-uploaded tabular files:
+
+1. **Streaming path ignored `file` role messages**: The streaming conversation history loop (`/api/chat/stream`) only processed `user` and `assistant` roles, making chat-uploaded files completely invisible to the model.
+
+2. **Mini SK only triggered from search results**: Both streaming and non-streaming paths only invoked `run_tabular_sk_analysis()` when tabular files appeared in hybrid search results (`combined_documents`). Chat-uploaded files are stored in blob storage as `file` role messages and are not indexed in Azure AI Search, so they never appeared in search results.
+
+3. **Model-only mode can't call plugin functions**: The non-streaming path's file handler injected "Use the tabular_processing plugin functions" as a system message, but in model-only mode the model has no function-calling capability.
+
+## Technical Details
+
+### Files Modified
+
+- `application/single_app/route_backend_chats.py` — All code changes
+- `application/single_app/config.py` — Version bump to 0.239.008
+
+### Code Changes
+
+#### Non-streaming path (`/api/chat`)
+
+1. Added `chat_tabular_files = set()` tracker before the conversation history loop (~line 1896)
+2. Added `chat_tabular_files.add(filename)` inside the `if is_table and file_content_source == 'blob':` block (~line 1936)
+3. After the history loop, added a block that checks `chat_tabular_files` and calls `run_tabular_sk_analysis(source_hint="chat")`, injecting pre-computed results as a system message (~line 2027)
+
+#### Streaming path (`/api/chat/stream`)
+
+4. Replaced the simple 8-line history loop (which only handled `user`/`assistant`) with expanded logic that mirrors the non-streaming path's `file` role handling, including blob tabular file tracking (~line 3687)
+5. Added the same mini SK trigger block after the expanded loop (~line 3751)
+
+### How It Works After Fix
+
+1. User uploads `sales.xlsx` to chat, asks "analyze sales/profit"
+2. During conversation history building, the `file` role message with `is_table=True` and `file_content_source='blob'` is detected
+3. The filename is collected into `chat_tabular_files`
+4. After the history loop, `run_tabular_sk_analysis()` is called with `source_hint="chat"`, which resolves the file from the `personal-chat` blob container
+5. The mini SK agent pre-loads the file schema, calls plugin functions (aggregate, filter, etc.), and returns computed results
+6. Results are injected as a system message so the model can present accurate numbers
+7. Plugin invocation citations are collected for transparency
+
+## Testing
+
+1. Upload a tabular file (xlsx/csv) directly to chat
+2. With no agent selected, send a data analysis question
+3. Verify the response contains actual computed data (not just a description of steps)
+4. Check logs for `[Chat Tabular SK]` entries confirming the mini SK trigger
+5. Verify agent mode still works as before
+
+## Impact
+
+- Enables tabular data analysis in model-only chat mode for chat-uploaded files
+- No changes to existing search-result-based tabular detection
+- No changes to full agent mode behavior
+- Streaming and non-streaming paths both fixed
+
+## Version
+
+- **Version**: 0.239.008
+- **Implemented in**: 0.239.008
diff --git a/docs/explanation/fixes/v0.239.032/TABULAR_WORKSPACE_TRIGGER_AND_THOUGHTS_FIX.md b/docs/explanation/fixes/v0.239.032/TABULAR_WORKSPACE_TRIGGER_AND_THOUGHTS_FIX.md
new file mode 100644
index 00000000..0ab4e9c2
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.032/TABULAR_WORKSPACE_TRIGGER_AND_THOUGHTS_FIX.md
@@ -0,0 +1,65 @@
+# Tabular Workspace Trigger and Thoughts Fix
+
+## Issue Description
+Users could ask multiple questions against the same selected tabular workspace file and see inconsistent behavior. A simple aggregation question could trigger the tabular SK mini-agent, while a later question against the same selected file could fall back to schema-only reasoning. In addition, the Processing Thoughts UI did not show any explicit `tabular_analysis` step even when tabular functions were used.
+
+**Version implemented:** 0.239.032
+
+Fixed/Implemented in version: **0.239.032**
+
+Related `config.py` update: `VERSION` was bumped to `0.239.032`.
+
+## Root Cause Analysis
+1. **Workspace trigger depended too heavily on search results**
+   - The tabular trigger only inspected `combined_documents` returned from hybrid search.
+   - If the selected tabular file produced sparse retrieval output or schema-only chunks, the trigger could miss the explicit workspace selection.
+2. **Mini-agent responses were not hardened against no-tool replies**
+   - For more complex analytical prompts, the mini-agent could return narrative text without actually calling the `TabularProcessingPlugin`.
+   - That produced no tool citations and left the final model with schema-only context.
+3. **Processing thoughts missed tabular work entirely**
+   - The chat flow recorded search, web, and generation steps, but never wrote a `tabular_analysis` thought for workspace or chat tabular runs.
+
+## Technical Details
+### Files Modified
+- `application/single_app/route_backend_chats.py`
+- `application/single_app/config.py`
+- `functional_tests/test_workspace_tabular_trigger_and_thoughts.py`
+
+### Code Changes Summary
+- Added shared helpers to:
+  - detect supported tabular filenames consistently,
+  - resolve explicitly selected workspace tabular documents, and
+  - merge search-result files with selected-document files before triggering analysis.
+- Moved workspace tabular trigger logic so it can run from explicit workspace selection, not just retrieved chunks.
+- Hardened `run_tabular_sk_analysis()` with a retry path that requires actual tabular tool usage before accepting the result.
+- Added `tabular_analysis` thoughts for:
+  - workspace tabular analysis start/completion in non-streaming mode,
+  - workspace tabular analysis start/completion in streaming mode,
+  - chat-uploaded tabular analysis start/completion in non-streaming mode,
+  - chat-uploaded tabular analysis start/completion in streaming mode.
+
+### Testing Approach
+- Added `functional_tests/test_workspace_tabular_trigger_and_thoughts.py` to verify:
+  - explicit workspace-selected tabular files participate in trigger detection,
+  - tabular analysis thoughts are emitted in both chat paths,
+  - the mini-agent prompt now requires tool execution and retries when it answers without tools.
+
+## Impact Analysis
+- Explicitly selected CSV/Excel workspace files now have a more reliable analysis trigger path.
+- Complex tabular prompts are less likely to degrade into schema-only answers.
+- Users can now see tabular analysis activity directly in Processing Thoughts, improving transparency and debugging.
+
+## Validation
+### Before
+- Some workspace-selected tabular questions skipped the SK mini-agent even though the same file was still selected.
+- Thoughts could show search and generation steps without any indication that tabular analysis ran.
+
+### After
+- Workspace tabular analysis considers both retrieved tabular documents and explicitly selected tabular files.
+- Mini-agent retries are stricter when the first response skips tool execution.
+- Processing Thoughts now includes clear `tabular_analysis` steps whenever tabular analysis is attempted.
+
+## Related Validation Assets
+- Functional test: `functional_tests/test_workspace_tabular_trigger_and_thoughts.py`
+- Related feature documentation: `docs/explanation/features/v0.239.003/PROCESSING_THOUGHTS.md`
+- Related earlier fix: `docs/explanation/fixes/v0.239.008/CHAT_TABULAR_SK_TRIGGER_FIX.md`
diff --git a/docs/explanation/fixes/v0.239.033/TABULAR_DATETIME_COMPONENT_ANALYSIS_FIX.md b/docs/explanation/fixes/v0.239.033/TABULAR_DATETIME_COMPONENT_ANALYSIS_FIX.md
new file mode 100644
index 00000000..fafcb2f5
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.033/TABULAR_DATETIME_COMPONENT_ANALYSIS_FIX.md
@@ -0,0 +1,64 @@
+# Tabular Datetime Component Analysis Fix
+
+## Issue Description
+Some tabular questions still fell back to schema-only context with the thought message `Tabular analysis could not compute results; using schema context instead`. This happened most often for time-based questions such as identifying peak hours, busiest weekdays, or monthly patterns from datetime columns.
+
+**Version implemented:** 0.239.033
+
+Fixed/Implemented in version: **0.239.033**
+
+Related `config.py` update: `VERSION` was bumped to `0.239.033`.
+
+## Root Cause Analysis
+1. **The plugin lacked datetime component grouping support**
+   - Existing tabular functions could aggregate and group by existing columns, but they could not directly derive `hour`, `day_of_week`, `month`, or similar components from datetime-like fields.
+   - Questions like “During what hours of the day do departure queues peak?” therefore required a transformation step the plugin did not expose.
+2. **The SK mini-agent could still fail even when the file triggered correctly**
+   - If the model could not find a tool sequence that matched the requested transformation, the tabular analysis flow returned `None` and the chat fell back to schema-only context.
+3. **There was no deterministic recovery path for common time-based questions**
+   - Even when datetime columns and queue/delay metrics were clearly present, the system did not attempt a direct computed fallback.
+
+## Technical Details
+### Files Modified
+- `application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py`
+- `application/single_app/route_backend_chats.py`
+- `application/single_app/config.py`
+- `functional_tests/test_tabular_datetime_component_analysis.py`
+
+### Code Changes Summary
+- Added datetime parsing helpers to `TabularProcessingPlugin` to support:
+  - ISO datetime strings,
+  - time-only strings,
+  - `HHMM` and `HHMMSS` compact time formats.
+- Added a new tabular plugin function: `group_by_datetime_component`
+  - Supports grouping by `year`, `month`, `month_name`, `day`, `date`, `hour`, `minute`, `day_name`, `weekday_number`, `quarter`, and `week`.
+  - Supports `count`, `sum`, `mean`, `min`, `max`, `median`, and `std` aggregations.
+  - Supports optional pre-group filtering with a pandas query expression.
+- Updated the tabular SK prompt and fallback guidance so time-based questions explicitly use `group_by_datetime_component`.
+- Added a direct datetime-aware fallback in `run_tabular_sk_analysis()` so common time-based questions can still return computed results even if the SK mini-agent does not successfully plan the tool sequence.
+
+### Testing Approach
+- Added `functional_tests/test_tabular_datetime_component_analysis.py` to verify:
+  - hour grouping works for ISO datetime strings,
+  - compact `HHMM` values are parsed correctly,
+  - route and plugin integration text references the new datetime grouping capability.
+
+## Impact Analysis
+- Time-based tabular questions now have a dedicated computation path instead of relying on schema-only reasoning.
+- Questions about peak hours, busiest weekdays, and similar datetime-derived trends are much less likely to fall back to the schema preview.
+- The direct fallback keeps user experience resilient even when the mini-agent does not autonomously choose the new function on the first try.
+
+## Validation
+### Before
+- Tabular analysis could trigger correctly but still fail to compute answers for questions requiring datetime-derived grouping.
+- Users saw the thought step `Tabular analysis could not compute results; using schema context instead` for time-based questions.
+
+### After
+- The tabular plugin can directly compute datetime component groupings.
+- The chat route can recover with a deterministic datetime-based fallback for common time-oriented questions.
+- Time-based questions now have a much stronger chance of returning computed results instead of schema-only context.
+
+## Related Validation Assets
+- Functional test: `functional_tests/test_tabular_datetime_component_analysis.py`
+- Related fix: `docs/explanation/fixes/v0.239.032/TABULAR_WORKSPACE_TRIGGER_AND_THOUGHTS_FIX.md`
+- Related thoughts documentation: `docs/explanation/features/v0.239.003/PROCESSING_THOUGHTS.md`
diff --git a/docs/explanation/fixes/v0.239.034/TABULAR_COMPUTED_ANALYSIS_ENFORCEMENT_FIX.md b/docs/explanation/fixes/v0.239.034/TABULAR_COMPUTED_ANALYSIS_ENFORCEMENT_FIX.md
new file mode 100644
index 00000000..58dc263a
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.034/TABULAR_COMPUTED_ANALYSIS_ENFORCEMENT_FIX.md
@@ -0,0 +1,60 @@
+# Tabular Computed Analysis Enforcement Fix
+
+## Issue Description
+Some analytical tabular questions still completed after a schema-only discovery call such as `describe_tabular_file`. That let the model answer from preview rows instead of using computed query, filter, aggregate, or grouped results from the full dataset.
+
+**Version implemented:** 0.239.034
+
+Fixed/Implemented in version: **0.239.034**
+
+Related `config.py` update: `VERSION` was bumped to `0.239.034`.
+
+## Root Cause Analysis
+1. **Any plugin call counted as successful analysis**
+   - `run_tabular_sk_analysis()` accepted the first response as long as any plugin invocation occurred.
+   - Discovery calls such as `describe_tabular_file` therefore counted the same as real analytical operations.
+2. **Schema discovery citations overshadowed computed analysis intent**
+   - When discovery calls happened before analytical calls, citations could emphasize schema inspection rather than the computed operations that actually answered the question.
+3. **Prompt guidance did not explicitly reject discovery-only behavior**
+   - Even with pre-loaded schemas, the mini-agent could still call discovery helpers and stop there.
+
+## Technical Details
+### Files Modified
+- `application/single_app/route_backend_chats.py`
+- `application/single_app/config.py`
+- `functional_tests/test_tabular_analysis_rejects_discovery_only.py`
+
+### Code Changes Summary
+- Added tabular invocation classification helpers in `route_backend_chats.py` to separate:
+  - discovery functions: `list_tabular_files`, `describe_tabular_file`
+  - analytical functions: `aggregate_column`, `filter_rows`, `query_tabular_data`, `group_by_aggregate`, `group_by_datetime_component`
+- Updated `run_tabular_sk_analysis()` so a response is accepted only when at least one analytical tabular function ran.
+- Added retry logging for discovery-only attempts so those paths are visible in diagnostics.
+- Updated tabular prompt guidance to explicitly reject discovery-only tool usage.
+- Filtered tabular citations so discovery-only calls are hidden when analytical tabular calls are present in the same analysis run.
+
+### Testing Approach
+- Added `functional_tests/test_tabular_analysis_rejects_discovery_only.py` to verify:
+  - discovery-only calls are explicitly rejected by prompt and retry guardrails,
+  - citation filtering prefers analytical calls,
+  - retry evaluation can isolate new invocations from the latest attempt.
+
+## Impact Analysis
+- Analytical tabular questions are less likely to be answered from schema previews or sample rows.
+- The mini-agent now has to perform a real computation before its output is trusted.
+- Citations better reflect the actual analytical operations used to answer the question.
+
+## Validation
+### Before
+- A single `describe_tabular_file` call could mark tabular analysis as complete.
+- Users could receive answers based on preview rows with thoughts showing tabular analysis as successful.
+
+### After
+- Discovery-only tool usage triggers a retry instead of being accepted as completed analysis.
+- Successful tabular analysis now requires a computed analytical call.
+- When analytical calls exist, tabular citations focus on those calls instead of schema-only discovery helpers.
+
+## Related Validation Assets
+- Functional test: `functional_tests/test_tabular_analysis_rejects_discovery_only.py`
+- Related fix: `docs/explanation/fixes/v0.239.033/TABULAR_DATETIME_COMPONENT_ANALYSIS_FIX.md`
+- Related fix: `docs/explanation/fixes/v0.239.032/TABULAR_WORKSPACE_TRIGGER_AND_THOUGHTS_FIX.md`
diff --git a/docs/explanation/fixes/v0.239.035/TABULAR_TOOL_CALL_THOUGHTS_FIX.md b/docs/explanation/fixes/v0.239.035/TABULAR_TOOL_CALL_THOUGHTS_FIX.md
new file mode 100644
index 00000000..15481301
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.035/TABULAR_TOOL_CALL_THOUGHTS_FIX.md
@@ -0,0 +1,61 @@
+# Tabular Tool Call Thoughts Fix
+
+## Issue Description
+Tabular analysis thoughts were summarized as generic wrapper messages such as `Running tabular analysis on 1 workspace file(s)` and `Tabular analysis completed using 1 tool call(s)`. That hid which specific tabular tools actually ran, making it harder to understand whether the system queried, filtered, grouped, or only inspected the file.
+
+**Version implemented:** 0.239.035
+
+Fixed/Implemented in version: **0.239.035**
+
+Related `config.py` update: `VERSION` was bumped to `0.239.035`.
+
+## Root Cause Analysis
+1. **Thoughts were recorded at the workflow level instead of the tool level**
+   - The workspace and chat tabular paths emitted only start/completion wrapper thoughts.
+   - Individual plugin invocations were collected for citations but not surfaced as separate tabular thoughts.
+2. **Users could not see what analysis actually happened**
+   - A completion message with a tool count did not reveal whether the mini-agent used `query_tabular_data`, `group_by_datetime_component`, `aggregate_column`, or other functions.
+3. **The agent tool-call pattern already existed elsewhere**
+   - Agent execution paths already emitted one thought per plugin invocation, but the tabular pre-analysis flow had not adopted the same level of detail.
+
+## Technical Details
+### Files Modified
+- `application/single_app/route_backend_chats.py`
+- `application/single_app/config.py`
+- `functional_tests/test_workspace_tabular_trigger_and_thoughts.py`
+
+### Code Changes Summary
+- Added helpers in `route_backend_chats.py` to:
+  - format concise tabular tool thought content,
+  - sanitize thought detail fields,
+  - convert tabular plugin invocations into per-tool thought payloads.
+- Replaced generic workspace and chat tabular wrapper thoughts with one `tabular_analysis` thought per tabular plugin invocation.
+- Preserved failure thoughts when tabular analysis cannot compute results.
+- Kept enhanced citations behavior unchanged while making the thoughts feed more transparent.
+
+### Testing Approach
+- Updated `functional_tests/test_workspace_tabular_trigger_and_thoughts.py` to verify:
+  - per-tool tabular thought helpers exist,
+  - workspace and streaming paths emit tool-level thought payload loops,
+  - generic completion wrapper thoughts are no longer used,
+  - formatted thought payloads contain useful parameters while excluding user and conversation identifiers.
+
+## Impact Analysis
+- Processing Thoughts now shows which tabular tool functions actually ran.
+- Users can distinguish schema inspection, filtering, grouping, and datetime analysis directly from the thoughts timeline.
+- Debugging tabular behavior is easier because the thought feed reflects the real analysis steps instead of only wrapper status messages.
+
+## Validation
+### Before
+- Thoughts showed only generic tabular wrapper messages.
+- Users could not tell which tabular function actually answered the question.
+
+### After
+- Thoughts include individual entries such as the exact tabular function invoked and its key parameters.
+- Generic wrapper completion thoughts are replaced by specific tabular tool-call thoughts.
+- Failure thoughts still appear when tabular analysis cannot compute results.
+
+## Related Validation Assets
+- Functional test: `functional_tests/test_workspace_tabular_trigger_and_thoughts.py`
+- Related fix: `docs/explanation/fixes/v0.239.034/TABULAR_COMPUTED_ANALYSIS_ENFORCEMENT_FIX.md`
+- Related fix: `docs/explanation/fixes/v0.239.033/TABULAR_DATETIME_COMPONENT_ANALYSIS_FIX.md`
diff --git a/docs/explanation/fixes/v0.239.036/TABULAR_GROUPED_PEAK_SUMMARY_FIX.md b/docs/explanation/fixes/v0.239.036/TABULAR_GROUPED_PEAK_SUMMARY_FIX.md
new file mode 100644
index 00000000..5dd1a069
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.036/TABULAR_GROUPED_PEAK_SUMMARY_FIX.md
@@ -0,0 +1,74 @@
+# Tabular Grouped Peak Summary Fix
+
+## Issue Description
+Peak-style analytical questions such as `During what hours of the day do departure queues peak?` still depended too heavily on the model interpreting raw grouped output. The plugin could group by hour, but it did not return explicit highest and lowest group summary fields, and datetime parsing relied too much on generic inference for common US-style timestamps.
+
+**Version implemented:** 0.239.036
+
+Fixed/Implemented in version: **0.239.036**
+
+Related `config.py` update: `VERSION` was bumped to `0.239.036`.
+
+## Root Cause Analysis
+1. **Grouped outputs lacked explicit extremes**
+   - `group_by_datetime_component` and `group_by_aggregate` returned grouped data, but not direct highest and lowest group summaries.
+   - For peak-style questions, the model had to infer the answer from raw JSON instead of using clearly labeled summary fields.
+2. **Common US-style datetime strings were not parsed as explicitly as they should be**
+   - Real data such as the FAA sample file uses values like `5/14/2026 8:31:36 AM`.
+   - The plugin relied on generic fallback parsing too early, which is weaker and noisier than handling common formats directly.
+3. **The tabular prompt did not teach the model to use grouped summary fields**
+   - Even when grouped results were available, the prompt did not explicitly steer peak-style questions toward the strongest summary outputs.
+
+## Technical Details
+### Files Modified
+- `application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py`
+- `application/single_app/route_backend_chats.py`
+- `application/single_app/config.py`
+- `functional_tests/test_tabular_grouped_peak_summary.py`
+
+### Code Changes Summary
+- Improved `_parse_datetime_like_series()` to explicitly handle common date and datetime formats before generic fallback parsing.
+- Added `_build_grouped_summary()` so grouped outputs can expose:
+  - `highest_group`
+  - `highest_value`
+  - `lowest_group`
+  - `lowest_value`
+  - `average_group_value`
+  - `median_group_value`
+  - `second_highest_group`
+  - `second_highest_value`
+- Extended `group_by_aggregate()` to support:
+  - `median`
+  - `std`
+  - `top_n`
+  - `sort_descending`
+  - grouped summary fields and ranked `top_results`
+- Extended `group_by_datetime_component()` to return grouped summary fields alongside `top_results`.
+- Updated the tabular SK prompt so peak-style questions explicitly use the grouped summary fields.
+
+### Testing Approach
+- Added `functional_tests/test_tabular_grouped_peak_summary.py` to verify:
+  - artifact-style `M/D/YYYY h:mm:ss AM/PM` timestamps group correctly by hour,
+  - grouped datetime outputs return highest and lowest summaries,
+  - grouped aggregate outputs return generic peak summaries,
+  - the route prompt mentions the new grouped summary guidance.
+
+## Impact Analysis
+- Peak-style questions are easier for the model to answer correctly because the plugin now returns explicit extremes.
+- Common tabular files with US-style date/time strings are parsed more reliably.
+- The enhancements remain generic and reusable for any grouped categorical or time-based tabular analysis.
+
+## Validation
+### Before
+- The plugin could compute grouped values but forced the model to infer peaks from raw grouped JSON.
+- Timestamp parsing depended more than necessary on generic datetime inference.
+
+### After
+- Grouped tools return explicit highest and lowest summary fields for peak-style interpretation.
+- Artifact-style timestamps like those observed in the FAA CSV are parsed directly by known formats.
+- The route prompt now encourages the model to use the summary fields when answering peak and busiest questions.
+
+## Related Validation Assets
+- Functional test: `functional_tests/test_tabular_grouped_peak_summary.py`
+- Related fix: `docs/explanation/fixes/v0.239.035/TABULAR_TOOL_CALL_THOUGHTS_FIX.md`
+- Related fix: `docs/explanation/fixes/v0.239.033/TABULAR_DATETIME_COMPONENT_ANALYSIS_FIX.md`
diff --git a/docs/explanation/fixes/v0.239.037/TABULAR_TOOL_ERROR_RETRY_AND_THOUGHTS_FIX.md b/docs/explanation/fixes/v0.239.037/TABULAR_TOOL_ERROR_RETRY_AND_THOUGHTS_FIX.md
new file mode 100644
index 00000000..52625425
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.037/TABULAR_TOOL_ERROR_RETRY_AND_THOUGHTS_FIX.md
@@ -0,0 +1,65 @@
+# Tabular Tool Error Retry and Thoughts Fix
+
+## Issue Description
+A failed analytical tabular tool call could still be treated as successful analysis when the plugin returned a JSON error payload rather than raising an exception. This let the chat stop after a single failed tabular tool attempt and produce a weak follow-up answer instead of retrying or falling back. It also left the visible thought feed too thin compared with the internal debug trail.
+
+**Version implemented:** 0.239.037
+
+Fixed/Implemented in version: **0.239.037**
+
+Related `config.py` update: `VERSION` was bumped to `0.239.037`.
+
+## Root Cause Analysis
+1. **Analytical tool presence was treated as success even when the result payload contained an error**
+   - `run_tabular_sk_analysis()` counted analytical function invocations without inspecting whether the returned JSON contained an `error` field.
+   - A single failed call such as `group_by_datetime_component` missing `aggregate_column` could therefore stop the retry flow early.
+2. **Retry attempts did not receive the previous tool error context**
+   - When the first tool call failed, the next SK attempt had no direct feedback about what argument was wrong.
+3. **Thoughts surfaced the tool call but not the recovery path**
+   - The UI could show a failed tool invocation, but not whether the system retried, recovered via fallback, or simply stopped.
+
+## Technical Details
+### Files Modified
+- `application/single_app/route_backend_chats.py`
+- `application/single_app/config.py`
+- `functional_tests/test_tabular_tool_error_retry_and_thoughts.py`
+
+### Code Changes Summary
+- Added helpers to inspect tabular invocation result payloads and extract embedded JSON error messages.
+- Updated analytical invocation classification so tool calls returning JSON errors are treated as failed, not successful.
+- Updated citation filtering so failed analytical tabular calls do not appear as successful tool citations.
+- Fed previous tool error messages back into subsequent SK retry prompts.
+- Added tabular status thoughts for:
+  - recovery after retrying tool errors,
+  - recovery via internal fallback after tool errors,
+  - tool-error state before fallback when computation still fails.
+- Updated tabular tool-call thoughts so JSON error payloads render as failed tool thoughts in the UI.
+
+### Testing Approach
+- Added `functional_tests/test_tabular_tool_error_retry_and_thoughts.py` to verify:
+  - JSON error payloads are classified as failed analytical calls,
+  - failed analytical calls do not become citations,
+  - failed tool thoughts show error details,
+  - recovery thoughts are emitted for internal fallback,
+  - retry prompts include previous tool error feedback.
+
+## Impact Analysis
+- A single failed analytical tabular tool call no longer ends the analysis prematurely.
+- Retry attempts have better context to correct bad tool arguments.
+- The thought feed now better explains the difference between a failed tool call and a recovered final analysis.
+
+## Validation
+### Before
+- The system could stop after one failed analytical tool call.
+- A JSON error payload could still be treated like successful analysis.
+- Thoughts did not clearly show recovery after tool errors.
+
+### After
+- Failed analytical tool calls trigger retry or fallback instead of being accepted as success.
+- Previous tool errors are fed back into the retry prompt.
+- The UI can show failed tool calls and the recovery/fallback status more clearly.
+
+## Related Validation Assets
+- Functional test: `functional_tests/test_tabular_tool_error_retry_and_thoughts.py`
+- Related fix: `docs/explanation/fixes/v0.239.036/TABULAR_GROUPED_PEAK_SUMMARY_FIX.md`
+- Related fix: `docs/explanation/fixes/v0.239.035/TABULAR_TOOL_CALL_THOUGHTS_FIX.md`
diff --git a/docs/explanation/fixes/v0.239.038/TABULAR_YEAR_TREND_AND_SUMMARY_GUARDRAILS_FIX.md b/docs/explanation/fixes/v0.239.038/TABULAR_YEAR_TREND_AND_SUMMARY_GUARDRAILS_FIX.md
new file mode 100644
index 00000000..d92b0384
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.038/TABULAR_YEAR_TREND_AND_SUMMARY_GUARDRAILS_FIX.md
@@ -0,0 +1,65 @@
+# Tabular Year Trend And Summary Guardrails Fix
+
+Fixed in version: **0.239.038**
+
+## Issue Description
+
+Broad tabular questions against workbook data could still produce weak behavior in two places:
+
+1. Year-based trend intent was under-inferred in the direct datetime fallback logic, even though the plugin already supports `year` grouping.
+2. Broad summaries could still mention speculative parser or follow-up analysis failures that were not the main requested outcome.
+
+This showed up with the Superstore workbook where yearly profit analysis should be computable from `Order Date`, but the response still mentioned a parameter parsing issue.
+
+## Root Cause Analysis
+
+- The route-level `infer_datetime_component()` helper recognized hour, weekday, month, quarter, week, and date intent, but not year, yearly, or annual intent.
+- The plugin-level `_try_numeric_conversion()` step converted already-parsed Excel datetime columns into numeric values before the datetime grouping logic ran, which broke valid workbook date columns like `Order Date`.
+- The mini-agent system prompt strongly required computed analysis, but it did not explicitly forbid narrating hypothetical or secondary failures when the user asked for a broad business summary.
+
+## Technical Details
+
+### Files Modified
+
+- `application/single_app/route_backend_chats.py`
+- `application/single_app/semantic_kernel_plugins/tabular_processing_plugin.py`
+- `application/single_app/config.py`
+- `functional_tests/test_tabular_datetime_component_analysis.py`
+
+### Code Changes Summary
+
+- Added `year`, `years`, `yearly`, `annual`, and `annually` keyword inference to the route datetime-component helper.
+- Preserved datetime and timedelta columns during the plugin numeric-conversion pass so Excel date columns remain usable for datetime grouping.
+- Expanded the tabular tool prompt text to frame `group_by_datetime_component` as the trend-analysis tool for year, quarter, month, week, day, and hour groupings.
+- Added a stronger prompt guardrail telling the tabular mini-agent not to mention hypothetical follow-up analyses, parser errors, or failed attempts unless the user explicitly asks about failures and real tool error output exists.
+- Extended the existing datetime regression test to cover yearly grouping behavior and the new prompt guidance.
+
+### Testing Approach
+
+- Reused the existing datetime component functional test file.
+- Added an in-memory yearly grouping scenario modeled on workbook-style date columns like `Order Date`.
+- Verified route prompt text contains the new yearly trend guidance and speculative-failure guardrail.
+
+## Impact Analysis
+
+- Improves reliability for yearly or annual time-series questions on CSV and Excel files.
+- Reduces the chance of broad tabular summaries surfacing distracting parser-error commentary when the user did not ask for failure details.
+- Keeps the tabular tool behavior generic rather than special-casing the Superstore workbook.
+
+## Validation
+
+### Test Results
+
+- `functional_tests/test_tabular_datetime_component_analysis.py`
+
+### Before
+
+- Year intent was not part of the direct datetime inference keywords.
+- Excel datetime columns could be converted away from datetime dtype before grouping.
+- The prompt left more room for broad summaries to mention hypothetical or secondary parsing failures.
+
+### After
+
+- Yearly and annual phrasing now map to `year` grouping intent.
+- Excel datetime columns remain intact for yearly and other datetime-component grouping.
+- The prompt explicitly steers the model toward computed findings only, without narrating unrelated failed attempts.
\ No newline at end of file
diff --git a/docs/explanation/fixes/v0.239.112/AGENT_AUDIT_METADATA_VALIDATION_FIX.md b/docs/explanation/fixes/v0.239.112/AGENT_AUDIT_METADATA_VALIDATION_FIX.md
new file mode 100644
index 00000000..0672c25d
--- /dev/null
+++ b/docs/explanation/fixes/v0.239.112/AGENT_AUDIT_METADATA_VALIDATION_FIX.md
@@ -0,0 +1,34 @@
+# Agent Audit Metadata Validation Fix (v0.239.112)
+
+## Issue Description
+Saving an existing agent could fail with `Agent validation failed: Additional properties are not allowed ('created_at', 'created_by', 'modified_at', 'modified_by' were unexpected)` when the browser sent back a round-tripped agent object that included server-managed audit metadata.
+
+## Root Cause Analysis
+The backend sanitized user-editable agent fields, but it did not strip server-managed audit or Cosmos metadata before schema validation. As a result, valid agent edits could be rejected purely because the payload still contained fields previously added by the backend.
+
+## Version Implemented
+Fixed/Implemented in version: **0.239.112**
+
+## Technical Details
+### Files Modified
+- application/single_app/functions_agent_payload.py
+- application/single_app/config.py
+- functional_tests/test_agent_audit_metadata_validation_fix.py
+
+### Code Changes Summary
+- Strip server-managed agent metadata such as `created_at`, `created_by`, `modified_at`, `modified_by`, `updated_at`, `last_updated`, `user_id`, `group_id`, and Cosmos system fields during payload sanitization.
+- Preserve existing save behavior where the backend rehydrates authoritative audit fields before persistence.
+- Add a regression test that validates a round-tripped agent payload can still pass schema validation.
+
+### Testing Approach
+- Run the functional test `functional_tests/test_agent_audit_metadata_validation_fix.py`.
+
+## Impact Analysis
+- Editing and saving existing agents no longer fails when the client includes backend-managed metadata.
+- The backend now treats audit metadata as authoritative server state rather than client-provided input.
+
+## Validation
+- Functional test: functional_tests/test_agent_audit_metadata_validation_fix.py
+
+## Reference to Config Version Update
+- Version updated in application/single_app/config.py to **0.239.112**.
\ No newline at end of file
diff --git a/docs/explanation/release_notes.md b/docs/explanation/release_notes.md
index 478242a6..70c9d144 100644
--- a/docs/explanation/release_notes.md
+++ b/docs/explanation/release_notes.md
@@ -2,6 +2,250 @@
 
 # Feature Release
 
+### **(v0.239.112)**
+
+#### Bug Fixes
+
+*   **Agent Save Validation for Round-Tripped Metadata**
+    *   Fixed agent saves failing when an existing personal, group, or global agent was edited and the browser sent back backend-managed audit fields such as `created_at`, `created_by`, `modified_at`, and `modified_by`.
+    *   Agent payload sanitization now strips backend-managed audit and Cosmos metadata before schema validation, while preserving server-side tracking during persistence.
+    *   (Ref: `functions_agent_payload.py`, `route_backend_agents.py`, agent schema validation, functional test coverage)
+
+### **(v0.239.104)**
+
+#### Bug Fixes
+
+*   **Multi-Sheet Workbook Tabular Analysis**
+    *   Fixed multi-sheet Excel workbooks being analyzed from the wrong worksheet during tabular chat responses. Questions that clearly target a specific tab, such as asset values in a workbook with `Assets`, `Balance`, and `Income` sheets, no longer silently default to the first sheet.
+    *   Tabular runtime analysis now requires explicit `sheet_name` or `sheet_index` selection for analytical calls on multi-sheet workbooks, and the SK mini-agent preload now includes workbook sheet inventory and per-sheet schemas so the model can choose the correct worksheet before computing results.
+    *   Enhanced citations and tabular previews now preserve worksheet context, using `Sheet: <name>` for sheet-specific references and `Location: Workbook Schema` for workbook-level schema citations instead of generic `Page 1` labels. The tabular preview modal also supports switching between workbook sheets.
+    *   (Ref: `tabular_processing_plugin.py`, `route_backend_chats.py`, `route_enhanced_citations.py`, `chat-enhanced-citations.js`, `chat-citations.js`, `chat-messages.js`)
+
+### **(v0.239.102)**
+
+#### Bug Fixes
+
+*   **Tabular Citation Conversation Ownership Check**
+    *   Fixed an IDOR vulnerability on `/api/enhanced_citations/tabular` where any authenticated user who could guess a `conversation_id` and `file_id` could download another user's chat-uploaded tabular files.
+    *   The endpoint now reads the conversation document from Cosmos DB and verifies that `conversation.user_id` matches the current user before serving the blob. Returns 403 Forbidden on mismatch and 404 if the conversation does not exist.
+    *   (Ref: `route_enhanced_citations.py`, `cosmos_conversations_container`)
+
+*   **Tabular Preview `max_rows` Parameter Validation**
+    *   The `max_rows` query parameter on `/api/enhanced_citations/tabular_preview` was parsed with bare `int()`, causing a 500 error on non-integer input. Switched to Flask's `request.args.get(..., type=int)` which silently falls back to the default on invalid input, matching the pattern used by other endpoints.
+    *   (Ref: `route_enhanced_citations.py`)
+
+#### New Features
+
+*   **Configurable Tabular Preview Blob Size Limit**
+    *   Added an admin-configurable maximum blob size for tabular file previews, replacing the previous hardcoded limit. Default is 200 MB.
+    *   New **Tabular Preview Limits** card in the Enhanced Citations section of Admin Settings (Citations tab) lets admins increase or decrease the limit based on their compute resources and user population.
+    *   Setting is stored as `tabular_preview_max_blob_size_mb` and accepts values from 1 to 1024 MB.
+    *   (Ref: `route_enhanced_citations.py`, `functions_settings.py`, `admin_settings.html`)
+
+*   **Tabular Preview Memory Optimization**
+    *   The `/api/enhanced_citations/tabular_preview` endpoint no longer loads entire files into a DataFrame. It now uses `nrows` limits in `pandas.read_csv`/`read_excel` to read only the rows needed for the preview, and checks blob size before downloading to reject oversized files early.
+    *   (Ref: `route_enhanced_citations.py`)
+
+### **(v0.239.031)**
+
+#### New Features
+
+*   **Persistent Conversation Summaries**
+    *   Summaries generated during conversation export are now saved to the conversation document in Cosmos DB for future reuse.
+    *   Cached summaries include `message_time_start` and `message_time_end` — when a conversation has new messages beyond the cached range, a fresh summary is generated automatically.
+    *   The conversation details modal now shows a **Summary** card at the top. If a summary exists it displays the content, generation date, and model used. If no summary exists a **Generate Summary** button with model selector lets users create one on demand.
+    *   A **Regenerate** button is available on existing summaries to force a refresh with the currently selected model.
+    *   New `POST /api/conversations/<id>/summary` endpoint accepts an optional `model_deployment` and returns the generated summary.
+    *   The `GET /api/conversations/<id>/metadata` response now includes a `summary` field.
+    *   Extracted `generate_conversation_summary()` as a shared helper used by both the export pipeline and the new API endpoint.
+    *   (Ref: `route_backend_conversation_export.py`, `route_backend_conversations.py`, `chat-conversation-details.js`, `functions_conversation_metadata.py`)
+
+*   **PDF Conversation Export**
+    *   Added PDF as a third export format option alongside JSON and Markdown, giving users a print-ready, visually styled conversation archive.
+    *   PDF output renders chat messages with colored bubbles that mirror the live chat UI: blue for user messages, gray for assistant messages, green for file messages, and amber for system messages.
+    *   Message content is converted from Markdown to HTML for rich formatting (bold, italic, code blocks, lists, tables) inside the PDF.
+    *   Full appendix structure is included (metadata, message details, references, processing thoughts, supplemental messages), matching the Markdown export layout.
+    *   Rendering uses PyMuPDF's Story API on US Letter paper with 0.5-inch margins and automatic multi-page overflow.
+    *   Works with both single-file and ZIP packaging; intro summaries are supported in PDF as well.
+    *   Frontend format step updated to a 3-column card grid with a new PDF card using the `bi-filetype-pdf` icon.
+    *   (Ref: `route_backend_conversation_export.py`, `chat-export.js`, PyMuPDF Story API, conversation export workflow)
+
+*   **Conversation Export Intro Summaries**
+    *   Added an optional AI-generated intro summary step to the conversation export workflow, so each exported chat can begin with a short abstract before the full transcript.
+    *   Summary model selection now reuses the same model list shown in the chat composer, keeping the export flow aligned with the main chat experience.
+    *   Works for both JSON and Markdown exports, including ZIP exports where each conversation keeps its own summary metadata.
+    *   (Ref: `route_backend_conversation_export.py`, `chat-export.js`, conversation export workflow)
+
+*   **Agent & Action User Tracking (created_by / modified_by)**
+    *   All agent and action documents (personal, group, and global) now include `created_by`, `created_at`, `modified_by`, and `modified_at` fields that track which user created or last modified the entity.
+    *   On updates, the original `created_by` and `created_at` values are preserved while `modified_by` and `modified_at` are refreshed with the current user and timestamp.
+    *   New optional `user_id` parameter added to `save_group_agent`, `save_global_agent`, `save_group_action`, and `save_global_action` for caller-supplied user tracking (backward-compatible, defaults to `None`).
+    *   (Ref: `functions_personal_agents.py`, `functions_group_agents.py`, `functions_global_agents.py`, `functions_personal_actions.py`, `functions_group_actions.py`, `functions_global_actions.py`)
+
+*   **Activity Logging for Agent & Action CRUD Operations**
+    *   Every create, update, and delete operation on agents and actions now generates an activity log record in the `activity_logs` Cosmos DB container and Application Insights.
+    *   Six new logging functions: `log_agent_creation`, `log_agent_update`, `log_agent_deletion`, `log_action_creation`, `log_action_update`, `log_action_deletion`.
+    *   Activity records include: `user_id`, `activity_type`, `entity_type` (agent/action), `operation` (create/update/delete), `workspace_type` (personal/group/global), and `workspace_context` (group_id when applicable).
+    *   Logging is fire-and-forget — failures never break the CRUD operation.
+    *   All personal, group, and admin routes for both agents and actions are wired up.
+    *   (Ref: `functions_activity_logging.py`, `route_backend_agents.py`, `route_backend_plugins.py`)
+
+*   **Tabular Data Analysis — SK Mini-Agent for Normal Chat**
+    *   Tabular files (CSV, XLSX, XLS, XLSM) detected in search results now trigger a lightweight Semantic Kernel mini-agent that pre-computes data analysis before the main LLM response. This brings the same analytical depth previously only available in full agent mode to every normal chat conversation.
+    *   **Automatic Detection**: When AI Search results include tabular files from any workspace (personal, group, or public) or chat-uploaded documents, the system automatically identifies them via the `TABULAR_EXTENSIONS` configuration and routes the query through the SK mini-agent pipeline.
+    *   **Unified Workspace and Chat Handling**: Tabular files are processed identically regardless of their storage location. The plugin resolves blob paths across all four container types (`user-documents`, `group-documents`, `public-documents`, `personal-chat`) with automatic fallback resolution if the primary source lookup fails. A user asking about an Excel file in their personal workspace gets the same analytical treatment as one asking about a CSV uploaded directly to a chat.
+    *   **Six Data Analysis Functions**: The `TabularProcessingPlugin` exposes `describe_tabular_file`, `aggregate_column` (sum, mean, count, min, max, median, std, nunique, value_counts), `filter_rows` (==, !=, >, <, >=, <=, contains, startswith, endswith), `query_tabular_data` (pandas query syntax), `group_by_aggregate`, and `list_tabular_files` — all registered as Semantic Kernel functions that the mini-agent orchestrates autonomously.
+    *   **Pre-Computed Results Injected as Context**: The mini-agent's computed analysis (exact numerical results, aggregations, filtered data) is injected into the main LLM's system context so it can present accurate, citation-backed answers without hallucinating numbers.
+    *   **Graceful Degradation**: If the mini-agent analysis fails for any reason, the system falls back to instructing the main LLM to use the tabular processing plugin functions directly, preserving full functionality.
+    *   **Non-Streaming and Streaming Support**: Both chat modes are supported. The mini-agent runs synchronously before the main LLM call in both paths.
+    *   **Requires Enhanced Citations**: The tabular processing plugin depends on the blob storage client initialized by the enhanced citations system. The `enable_enhanced_citations` admin setting must be enabled for tabular data analysis to activate.
+    *   (Ref: `run_tabular_sk_analysis()`, `TabularProcessingPlugin`, `collect_tabular_sk_citations()`, `TABULAR_EXTENSIONS`)
+
+*   **Tabular Tool Execution Citations**
+    *   Every tool call made by the SK mini-agent during tabular analysis is captured and surfaced as an agent citation, providing full transparency into the data analysis pipeline.
+    *   **Automatic Capture**: The existing `@plugin_function_logger` decorator on all `TabularProcessingPlugin` functions records each invocation including function name, input parameters, returned results, execution duration, and success/failure status.
+    *   **Citation Format**: Tool execution citations appear in the same "Agent Tool Execution" modal used by full agent mode, showing `tool_name` (e.g., `TabularProcessingPlugin.aggregate_column`), `function_arguments` (the exact parameters passed), and `function_result` (the computed data returned).
+    *   **End-to-End Auditability**: Users can verify exactly which aggregations, filters, or queries were run against their data, what parameters were used, and what raw results were returned — before the LLM summarized them into the final response.
+    *   (Ref: `collect_tabular_sk_citations()`, `plugin_invocation_logger.py`)
+
+*   **SK Mini-Agent Performance Optimization**
+    *   Reduced typical tabular analysis time from ~74 seconds to an estimated ~30-33 seconds (55-60% reduction) through three complementary optimizations.
+    *   **DataFrame Caching**: Per-request in-memory cache eliminates redundant blob downloads. Previously, each of the ~8 tool calls in a typical analysis downloaded and parsed the same file independently. Now the file is downloaded once and subsequent calls read from cache. Cache is automatically scoped to the request (new plugin instance per analysis) and garbage-collected afterward.
+    *   **Pre-Dispatch Schema Injection**: File schemas (columns, data types, row counts, and a 3-row preview) are pre-loaded and injected into the SK mini-agent's system prompt before execution begins. This eliminates 2 LLM round-trips that were previously spent on file discovery (`list_tabular_files`) and schema inspection (`describe_tabular_file`), allowing the model to jump directly to analysis tool calls.
+    *   **Async Plugin Functions**: All six `@kernel_function` methods converted to `async def` using `asyncio.to_thread()`. This enables Semantic Kernel's built-in `asyncio.gather()` to truly parallelize batched tool calls (e.g., 3 simultaneous `aggregate_column` calls) instead of executing them serially on the event loop.
+    *   **Batching Instructions**: The system prompt now instructs the model to batch multiple independent function calls in a single response, reducing LLM round-trips further.
+    *   (Ref: `_df_cache`, `asyncio.to_thread`, pre-dispatch schema injection in `run_tabular_sk_analysis()`)
+
+*   **SQL Test Connection Button**
+    *   Added a "Test Connection" button to the SQL Database Configuration section (Step 3) of the action wizard, allowing users to validate database connectivity before saving.
+    *   Supports all database types: SQL Server, Azure SQL (with managed identity), PostgreSQL, MySQL, and SQLite.
+    *   Shows inline success/failure alerts with a 15-second timeout cap and sanitized error messages.
+    *   New backend endpoint: `POST /api/plugins/test-sql-connection`.
+    *   (Ref: `route_backend_plugins.py`, `plugin_modal_stepper.js`, `_plugin_modal.html`)
+
+#### Bug Fixes
+
+*   **On-Demand Summary Generation — Content Normalization Fix**
+    *   Fixed the `POST /api/conversations/<id>/summary` endpoint failing with an error when generating summaries from the conversation details modal.
+    *   Root cause: message `content` in Cosmos DB can be a list of content parts (e.g., `[{type: "text", text: "..."}]`) rather than a plain string. The endpoint was passing the raw list as `content_text`, which either stringified incorrectly or produced empty transcript text.
+    *   Now uses `_normalize_content()` to properly flatten list/dict content into plain text, matching the export pipeline's behavior.
+    *   (Ref: `route_backend_conversations.py`, `_normalize_content`, `generate_conversation_summary`)
+
+*   **Export Summary Reasoning-Model Compatibility**
+    *   Fixed export intro summary generation failing or returning empty content with reasoning-series models (gpt-5, o1, o3) through a series of incremental fixes: using `developer` role instead of `system` for instruction messages, removing all `max_tokens` / `max_completion_tokens` caps so the model decides output length naturally, and adding null-safe content extraction for `None` responses.
+    *   Summary now includes ALL messages (user, assistant, system, file, image analysis) for full context, with a simplified prompt producing 1-2 factual paragraphs.
+    *   Added detailed debug logging showing message count, character count, model name, role, and finish reason.
+    *   (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, `generate_conversation_summary`)
+
+*   **Conversation Export Schema and Markdown Refresh**
+    *   Fixed conversation exports lagging behind the live chat schema. JSON exports now include processing thoughts, normalized citations, and the raw document/web/tool citation buckets stored with assistant messages.
+    *   Fixed Markdown exports being too flat and text-heavy by reorganizing them into a transcript-first layout with appendices for metadata, message details, references, thoughts, and supplemental records.
+    *   Fixed exported conversations including content that no longer matched the visible chat by filtering deleted messages and inactive-thread retries, then reapplying thread-aware ordering before export.
+    *   (Ref: `route_backend_conversation_export.py`, `test_conversation_export.py`, conversation export rendering)
+
+*   **Export Tag/Classification Rendering Fix**
+    *   Fixed conversation tags and classifications rendering as raw Python dicts (e.g., `{'category': 'model', 'value': 'gpt-5'}`) in both Markdown and PDF exports.
+    *   Tags now display as readable `category: value` strings, with smart handling for participant names, document titles, and generic category/value pairs.
+    *   (Ref: `route_backend_conversation_export.py`, `_format_tag` helper, Markdown/PDF metadata rendering)
+
+*   **Export Summary Error Visibility**
+    *   Added `debug_print` and `log_event` logging to all summary generation error paths, including the empty-response path that previously failed silently.
+    *   The actual error detail is now shown in both Markdown and PDF exports when summary generation fails, replacing the generic "could not be generated" message.
+    *   (Ref: `route_backend_conversation_export.py`, `_build_summary_intro`, export error rendering)
+
+*   **Content Safety for Streaming Chat Path**
+    *   Added full Azure AI Content Safety checking to the streaming (`/api/chat/stream`) SSE path, matching the existing non-streaming (`/api/chat`) implementation.
+    *   Previously, only the non-streaming path performed content safety analysis; streaming conversations bypassed safety checks entirely.
+    *   Implementation includes: `AnalyzeTextOptions` analysis, severity threshold checking (severity ≥ 4 blocks the message), blocklist matching, persistence of blocked messages to `cosmos_safety_container`, creation of safety-role message documents, and proper SSE event delivery of blocked status to the client.
+    *   On block, the streaming generator yields the safety message and `[DONE]` event, then stops — preventing any further LLM invocation.
+    *   Errors in the content safety call are caught and logged without breaking the chat flow, consistent with the non-streaming behavior.
+    *   (Ref: `route_backend_chats.py`, streaming SSE generator, `AnalyzeTextOptions`, `cosmos_safety_container`)
+
+*   **SQL Schema Plugin — Eliminate Redundant Schema Calls**
+    *   Fixed agent calling `get_database_schema` twice per query even though the full schema was already injected into the agent's instructions at load time.
+    *   Root cause: The `@kernel_function` descriptions in `sql_schema_plugin.py` said "ALWAYS call this function FIRST," which overrode the schema context already available in the instructions.
+    *   Updated all four function descriptions (`get_database_schema`, `get_table_schema`, `get_table_list`, `get_relationships`) to use the resilient pattern: "If the database schema is already provided in your instructions, use that directly and do NOT call this function."
+    *   This eliminates ~400ms+ of unnecessary database round trips per query and aligns with the same pattern already used in `sql_query_plugin.py`.
+    *   (Ref: `sql_schema_plugin.py`, `@kernel_function` descriptions, schema injection)
+
+*   **SQL Schema Plugin — Empty Tables from INFORMATION_SCHEMA**
+    *   Fixed `get_database_schema` returning `'tables': {}` (empty) despite the database having tables, while relationships were returned correctly.
+    *   Root cause: SQL Server table/column enumeration used `INFORMATION_SCHEMA.TABLES` and `INFORMATION_SCHEMA.COLUMNS` views, which returned empty results in the Azure SQL environment. Meanwhile, the relationships query used `sys.foreign_keys`/`sys.tables`/`sys.columns` catalog views which worked perfectly.
+    *   Migrated all SQL Server schema queries to use `sys.*` catalog views consistently: `sys.tables`/`sys.schemas` for table enumeration, `sys.columns` with `TYPE_NAME()` for column details, and `sys.indexes`/`sys.index_columns` for primary key detection.
+    *   Fixed `pyodbc.Row` handling throughout the plugin — removed all `isinstance(table, tuple)` checks that could fail with pyodbc Row objects, replaced with robust try/except indexing.
+    *   This enables the full schema (tables, columns, types, PKs, FKs) to be injected into agent instructions, allowing agents to construct complex multi-table JOINs for analytical queries.
+    *   (Ref: `sql_schema_plugin.py`, `sys.tables`, `sys.columns`, `sys.indexes`, pyodbc.Row handling)
+
+*   **SQL Query Plugin — Auto-Create Companion Schema Plugin**
+    *   Fixed the remaining issue where SQL-connected agents still asked for clarification instead of querying the database, even after description improvements.
+    *   Root cause: Agents configured with only a `sql_query` action never had a `SQLSchemaPlugin` loaded in the kernel. The descriptions demanded calling `get_database_schema` — a function that didn't exist — creating an impossible dependency that caused the LLM to ask for clarification.
+    *   `LoggedPluginLoader` now automatically creates a companion `SQLSchemaPlugin` whenever a `SQLQueryPlugin` is loaded, using the same connection details. This ensures schema discovery is always available.
+    *   Updated `@kernel_function` descriptions to be resilient: "If the database schema is provided in your instructions, use it directly. Otherwise, call get_database_schema." This dual-path approach works whether schema is injected via instructions or available via plugin functions.
+    *   Added fallback in `_extract_sql_schema_for_instructions()` to also detect `SQLQueryPlugin` instances and create a temporary schema extractor if no `SQLSchemaPlugin` is found.
+    *   (Ref: `logged_plugin_loader.py`, `sql_query_plugin.py`, `semantic_kernel_loader.py`)
+
+*   **SQL Query Plugin Schema Awareness**
+    *   Fixed agents connected to SQL databases asking users for clarification about table/column names instead of querying the database directly.
+    *   Root cause: SQL Query and SQL Schema plugin `@kernel_function` descriptions were generic with no workflow guidance, agent instructions had no database schema context, and the two plugins operated independently with no linkage.
+    *   Rewrote all `@kernel_function` descriptions in both SQL plugins to be prescriptive workflow guides (modeled after the working LogAnalyticsPlugin), explicitly instructing the LLM to discover schema first before generating queries.
+    *   Added auto-injection of database schema into agent instructions at load time — when SQL Schema plugins are detected, the full schema (tables, columns, types, relationships) is fetched and appended to the agent's system prompt.
+    *   Added new `query_database(question, query)` convenience function to `SQLQueryPlugin` for intent-aligned tool calling.
+    *   Enabled the SQL-specific plugin creation path in `logged_plugin_loader.py` (was previously commented out).
+    *   (Ref: `sql_query_plugin.py`, `sql_schema_plugin.py`, `semantic_kernel_loader.py`, `logged_plugin_loader.py`)
+
+*   **Chat-Uploaded Tabular Files Now Trigger SK Mini-Agent in Model-Only Mode**
+    *   Fixed an issue where tabular files (CSV, XLSX, XLS, XLSM) uploaded directly to a chat conversation were not analyzed by the SK mini-agent when no agent was selected. The model would describe what analysis it would perform instead of returning actual computed results.
+    *   **Root Cause**: The mini SK agent only triggered from search results, but chat-uploaded files are stored in blob storage and not indexed in Azure AI Search. Additionally, the streaming path completely ignored `file` role messages in conversation history.
+    *   **Fix**: Both streaming and non-streaming chat paths now detect chat-uploaded tabular files during conversation history building and trigger `run_tabular_sk_analysis(source_hint="chat")` to pre-compute results. The streaming path also now properly handles `file` role messages (tabular and non-tabular) matching the non-streaming path's behavior.
+    *   (Ref: `route_backend_chats.py`, `run_tabular_sk_analysis()`, `collect_tabular_sk_citations()`)
+
+*   **Group SQL Action/Plugin Save Failure**
+    *   Fixed group SQL actions (sql_query and sql_schema types) failing to save correctly due to missing endpoint placeholder. Group routes now apply the same `sql://sql_query` / `sql://sql_schema` endpoint logic as personal action routes.
+    *   Fixed Step 4 (Advanced) dynamic fields overwriting Step 3 (Configuration) SQL values with empty strings during form data collection. SQL types now skip the dynamic field merge entirely since Step 3 already provides all necessary configuration.
+    *   Fixed auth type definition schemas (`sql_query.definition.json`, `sql_schema.definition.json`) only allowing `connection_string` auth type, blocking `user`, `identity`, and `servicePrincipal` types that the UI and runtime support.
+    *   Fixed `__Secret` key suffix mismatch in additional settings schemas where `connection_string__Secret` and `password__Secret` didn't match the runtime's expected `connection_string` and `password` field names. Also removed duplicate `azuresql` enum value.
+    *   (Ref: `route_backend_plugins.py`, `plugin_modal_stepper.js`, `sql_query.definition.json`, `sql_schema.definition.json`, `sql_query_plugin.additional_settings.schema.json`, `sql_schema_plugin.additional_settings.schema.json`)
+
+#### User Interface Enhancements
+
+*   **Agent Responded Thought — Seconds & Total Duration**
+    *   The "responded" thought now shows time in **seconds** instead of milliseconds, and clarifies it is the total time from the initial user message (e.g., `'gpt-5-nano' responded (16.3s from initial message)`).
+    *   A `request_start_time` is now captured at the top of both the non-streaming and streaming chat handlers, so the duration reflects the full request lifecycle — including content safety, hybrid search, and agent invocation — not just the model response time.
+    *   Applies to all three agent paths: local SK agents (non-streaming), Azure AI Foundry agents, and streaming SK agents.
+    *   (Ref: `route_backend_chats.py`, `request_start_time`, agent responded thoughts)
+
+*   **Enhanced Agent Execution Thoughts**
+    *   Added detailed model-level status messages during agent execution, giving users full visibility into each stage of the AI pipeline.
+    *   **Model Identification**: A new "Sending to '{deployment_name}'" thought appears immediately after "Sending to agent", showing the exact model deployment being used (e.g., `gpt-5-nano`).
+    *   **Generating Response**: A "Generating response..." thought now appears before the agent begins its invocation loop, matching the existing behavior for non-agent GPT calls.
+    *   **Model Responded with Duration**: A "'{deployment_name}' responded ({duration}ms)" thought appears after the agent completes, showing total wall-clock execution time.
+    *   Applies to all three agent paths: local SK agents (streaming and non-streaming) and Azure AI Foundry agents.
+    *   Uses the existing `generation` step type (lightning bolt icon) — no frontend changes required.
+    *   (Ref: `route_backend_chats.py`, `ThoughtTracker`, agent execution pipeline)
+
+*   **List/Grid View Toggle for Agents and Actions**
+    *   Added a list/grid view toggle to all four workspace areas: personal agents, personal actions, group agents, and group actions.
+    *   **Grid View**: Large cards with type icon, humanized name, truncated description, and action buttons (Chat, View, Edit, Delete as applicable).
+    *   **List View**: Improved table layout with fixed column widths (28%/47%/25%), humanized display names, and truncated descriptions with hover tooltips for full text.
+    *   **View Button**: New eye-icon button on every agent and action that opens a read-only detail modal with gradient-header summary cards (Basic Information, Model Configuration, Instructions for agents; Basic Information, Configuration for actions).
+    *   **Name Humanization**: Display names are now automatically parsed — underscores and camelCase/PascalCase boundaries are converted to properly spaced, title-cased words (e.g., `myCustomAgent` → `My Custom Agent`).
+    *   **Persistent Preference**: View mode selection (list/grid) is saved per area in localStorage and restored on page load.
+    *   New shared utility module `view-utils.js` provides reusable functions for all four workspace areas.
+    *   (Ref: `view-utils.js`, `workspace_agents.js`, `workspace_plugins.js`, `plugin_common.js`, `group_agents.js`, `group_plugins.js`, `workspace.html`, `group_workspaces.html`, `styles.css`)
+
+*   **Chat with Agent Button for Group Agents**
+    *   Added a "Chat" button to each group agent row, allowing users to quickly select a group agent and navigate to the chat page.
+    *   (Ref: `group_agents.js`, `group_workspaces.html`)
+
+*   **Hidden Deprecated Action Types**
+    *   Deprecated action types (`sql_schema`, `ui_test`, `queue_storage`, `blob_storage`, `embedding_model`) are now hidden from the action creation wizard type selector. Existing actions of these types remain functional.
+    *   (Ref: `plugin_modal_stepper.js`)
+
+*   **Advanced Settings Collapse Toggle**
+    *   Step 4 (Advanced) content is now hidden behind a collapsible toggle button ("Show Advanced Settings") instead of being displayed by default. Reduces visual noise for most users.
+    *   For SQL action types, the redundant additional fields UI in Step 4 is hidden entirely since all SQL configuration is already handled in Step 3.
+    *   Step 5 (Summary) no longer shows the raw additional fields JSON dump for SQL types, since that data is already shown in the SQL Database Configuration summary card.
+    *   (Ref: `_plugin_modal.html`, `plugin_modal_stepper.js`)
 ### **(v0.239.005)**
 
 #### New Features
@@ -139,19 +383,6 @@
     *   **Files Modified**: `chat-documents.js`, `chat-messages.js`, `functions_search.py`, `route_backend_chats.py`, `chats.html`.
     *   (Ref: Multi-document selection, tag filtering, OData search integration, `CHAT_DOCUMENT_AND_TAG_FILTERING.md`)
 
-#### New Features
-
-*   **Conversation Export**
-    *   Export one or multiple conversations from the Chat page in JSON or Markdown format.
-    *   **Single Export**: Use the ellipsis menu on any conversation to quickly export it.
-    *   **Multi-Export**: Enter selection mode, check the conversations you want, and click the export button.
-    *   A guided 4-step wizard walks you through selection review, format choice, packaging options (single file or ZIP archive), and download.
-    *   Sensitive internal metadata is automatically stripped from exported data for security.
-
-*   **Retention Policy UI for Groups and Public Workspaces**
-    *   Can now configure conversation and document retention periods directly from the workspace and group management page.
-    *   Choose from preset retention periods ranging from 7 days to 10 years, use the organization default, or disable automatic deletion entirely.
-
 #### Bug Fixes
 
 *   **Citation Parsing Bug Fix**
diff --git a/functional_tests/test_agent_audit_metadata_validation_fix.py b/functional_tests/test_agent_audit_metadata_validation_fix.py
new file mode 100644
index 00000000..25a58e3b
--- /dev/null
+++ b/functional_tests/test_agent_audit_metadata_validation_fix.py
@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+# test_agent_audit_metadata_validation_fix.py
+"""
+Functional test for agent audit metadata validation fix.
+Version: 0.239.112
+Implemented in: 0.239.112
+
+This test ensures that round-tripped agent payloads containing server-managed
+audit and Cosmos metadata can still be sanitized and validated before save.
+"""
+
+import os
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app'))
+
+from functions_agent_payload import sanitize_agent_payload
+from json_schema_validation import validate_agent
+
+
+def test_round_tripped_agent_metadata_is_ignored():
+    """Audit and Cosmos metadata should not block agent validation."""
+    print("🔍 Testing round-tripped agent payload sanitization...")
+
+    payload = {
+        "id": "123e4567-e89b-42d3-a456-426614174000",
+        "name": "metadata_safe_agent",
+        "display_name": "Metadata Safe Agent",
+        "description": "Validates metadata stripping before agent save.",
+        "instructions": "Be helpful.",
+        "azure_openai_gpt_endpoint": "",
+        "azure_openai_gpt_key": "",
+        "azure_openai_gpt_deployment": "deployment-a",
+        "azure_openai_gpt_api_version": "2024-05-01-preview",
+        "azure_agent_apim_gpt_endpoint": "",
+        "azure_agent_apim_gpt_subscription_key": "",
+        "azure_agent_apim_gpt_deployment": "",
+        "azure_agent_apim_gpt_api_version": "",
+        "enable_agent_gpt_apim": False,
+        "is_global": False,
+        "is_group": False,
+        "agent_type": "local",
+        "actions_to_load": [],
+        "other_settings": {},
+        "max_completion_tokens": 4096,
+        "created_at": "2026-03-13T10:00:00Z",
+        "created_by": "user-1",
+        "modified_at": "2026-03-13T10:05:00Z",
+        "modified_by": "user-2",
+        "updated_at": "2026-03-13T10:05:00Z",
+        "last_updated": "2026-03-13T10:05:00Z",
+        "user_id": "user-1",
+        "group_id": "group-1",
+        "_etag": "etag-value",
+        "_rid": "rid-value",
+        "_self": "self-value",
+        "_ts": 1234567890
+    }
+
+    cleaned = sanitize_agent_payload(payload)
+    validation_error = validate_agent(cleaned)
+
+    unexpected_fields = [
+        "created_at",
+        "created_by",
+        "modified_at",
+        "modified_by",
+        "updated_at",
+        "last_updated",
+        "user_id",
+        "group_id",
+        "_etag",
+        "_rid",
+        "_self",
+        "_ts",
+    ]
+
+    for field in unexpected_fields:
+        if field in cleaned:
+            print(f"❌ Field should have been stripped during sanitization: {field}")
+            return False
+
+    if validation_error:
+        print(f"❌ Validation failed after sanitization: {validation_error}")
+        return False
+
+    print("✅ Agent payload validation ignores server-managed metadata.")
+    return True
+
+
+if __name__ == "__main__":
+    print("🧪 Testing Agent Audit Metadata Validation Fix...")
+    print("=" * 70)
+
+    tests = [
+        test_round_tripped_agent_metadata_is_ignored,
+    ]
+
+    results = []
+
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+
+    success = all(results)
+    print(f"\n📊 Results: {sum(results)}/{len(tests)} tests passed")
+
+    if success:
+        print("✅ All agent audit metadata validation tests passed!")
+    else:
+        print("❌ Some tests failed. Please review the agent metadata sanitization changes.")
+
+    sys.exit(0 if success else 1)
\ No newline at end of file
diff --git a/functional_tests/test_chat_preserves_workspace_selection_on_auto_create.py b/functional_tests/test_chat_preserves_workspace_selection_on_auto_create.py
new file mode 100644
index 00000000..ecbe5482
--- /dev/null
+++ b/functional_tests/test_chat_preserves_workspace_selection_on_auto_create.py
@@ -0,0 +1,178 @@
+#!/usr/bin/env python3
+# test_chat_preserves_workspace_selection_on_auto_create.py
+"""
+Functional test for chat workspace selection preservation on implicit conversation creation.
+Version: 0.239.105
+Implemented in: 0.239.105
+
+This test ensures that auto-creating a conversation from the chat input,
+prompt picker, send flow, or file upload flow does not reset selected
+workspace scope, tags, or documents back to the default state.
+"""
+
+import os
+import sys
+
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+CHAT_DOCUMENTS_FILE = os.path.join(
+    ROOT_DIR,
+    'application',
+    'single_app',
+    'static',
+    'js',
+    'chat',
+    'chat-documents.js',
+)
+CHAT_CONVERSATIONS_FILE = os.path.join(
+    ROOT_DIR,
+    'application',
+    'single_app',
+    'static',
+    'js',
+    'chat',
+    'chat-conversations.js',
+)
+CHAT_ONLOAD_FILE = os.path.join(
+    ROOT_DIR,
+    'application',
+    'single_app',
+    'static',
+    'js',
+    'chat',
+    'chat-onload.js',
+)
+CHAT_INPUT_ACTIONS_FILE = os.path.join(
+    ROOT_DIR,
+    'application',
+    'single_app',
+    'static',
+    'js',
+    'chat',
+    'chat-input-actions.js',
+)
+CHAT_MESSAGES_FILE = os.path.join(
+    ROOT_DIR,
+    'application',
+    'single_app',
+    'static',
+    'js',
+    'chat',
+    'chat-messages.js',
+)
+
+
+def read_file(path):
+    with open(path, 'r', encoding='utf-8') as file_handle:
+        return file_handle.read()
+
+
+def test_scope_reset_supports_preserving_current_selections():
+    """Verify scope reset can clear lock state without wiping active selections."""
+    print('🔍 Testing resetScopeLock preserveSelections support...')
+
+    try:
+        content = read_file(CHAT_DOCUMENTS_FILE)
+
+        required_snippets = [
+            'export function resetScopeLock(options = {})',
+            'const { preserveSelections = false } = options;',
+            'if (preserveSelections) {',
+            'buildScopeDropdown();',
+            'updateScopeLockIcon();',
+            'updateHeaderLockIcon();',
+            'return;',
+        ]
+
+        missing = [snippet for snippet in required_snippets if snippet not in content]
+        assert not missing, f'Missing preserveSelections reset support: {missing}'
+
+        print('✅ resetScopeLock preserveSelections support passed')
+        return True
+
+    except Exception as exc:
+        print(f'❌ Test failed: {exc}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_create_new_conversation_reuses_pending_request_and_respects_preserve_flag():
+    """Verify createNewConversation preserves selections when requested and reuses in-flight creation."""
+    print('🔍 Testing createNewConversation preserve path and in-flight reuse...')
+
+    try:
+        content = read_file(CHAT_CONVERSATIONS_FILE)
+
+        required_snippets = [
+            'let pendingConversationCreation = null;',
+            'if (pendingConversationCreation) {',
+            'await pendingConversationCreation;',
+            'const { preserveSelections = false } = options;',
+            'resetScopeLock({ preserveSelections });',
+        ]
+
+        missing = [snippet for snippet in required_snippets if snippet not in content]
+        assert not missing, f'Missing createNewConversation preservation logic: {missing}'
+
+        # Explicit New Conversation should still use the default full reset path.
+        assert 'createNewConversation();' in content, (
+            'Expected explicit New Conversation button flow to remain unchanged.'
+        )
+
+        print('✅ createNewConversation preserve path and in-flight reuse passed')
+        return True
+
+    except Exception as exc:
+        print(f'❌ Test failed: {exc}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_implicit_conversation_creation_call_sites_preserve_workspace_filters():
+    """Verify implicit create-conversation flows request preserved selections."""
+    print('🔍 Testing implicit conversation creation call sites...')
+
+    try:
+        onload_content = read_file(CHAT_ONLOAD_FILE)
+        input_actions_content = read_file(CHAT_INPUT_ACTIONS_FILE)
+        messages_content = read_file(CHAT_MESSAGES_FILE)
+
+        checks = {
+            'input focus preserves selections': 'createNewConversation(null, { preserveSelections: true });' in onload_content,
+            'prompt button preserves selections': onload_content.count('createNewConversation(null, { preserveSelections: true });') >= 2,
+            'file button preserves selections': onload_content.count('createNewConversation(null, { preserveSelections: true });') >= 3,
+            'file upload auto-create preserves selections': input_actions_content.count('}, { preserveSelections: true });') >= 2,
+            'send flow preserves selections': '}, { preserveSelections: true });' in messages_content,
+        }
+
+        failed_checks = [name for name, passed in checks.items() if not passed]
+        assert not failed_checks, f'Missing preserve-selection implicit creation paths: {failed_checks}'
+
+        print('✅ Implicit conversation creation call sites passed')
+        return True
+
+    except Exception as exc:
+        print(f'❌ Test failed: {exc}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == '__main__':
+    tests = [
+        test_scope_reset_supports_preserving_current_selections,
+        test_create_new_conversation_reuses_pending_request_and_respects_preserve_flag,
+        test_implicit_conversation_creation_call_sites_preserve_workspace_filters,
+    ]
+
+    results = []
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+
+    success = all(results)
+    print(f"\n📊 Results: {sum(results)}/{len(results)} tests passed")
+    sys.exit(0 if success else 1)
\ No newline at end of file
diff --git a/functional_tests/test_chat_tabular_sk_trigger.py b/functional_tests/test_chat_tabular_sk_trigger.py
new file mode 100644
index 00000000..9de91b39
--- /dev/null
+++ b/functional_tests/test_chat_tabular_sk_trigger.py
@@ -0,0 +1,319 @@
+#!/usr/bin/env python3
+# test_chat_tabular_sk_trigger.py
+"""
+Functional test for chat-uploaded tabular file SK mini-agent trigger fix.
+Version: 0.239.008
+Implemented in: 0.239.008
+
+This test ensures that when tabular data files (CSV, XLSX) are uploaded directly
+to a chat conversation, the SK mini-agent (`run_tabular_sk_analysis`) is properly
+triggered in model-only mode (no agent selected) to pre-compute analysis results.
+
+Previously, the mini SK agent only triggered from search results, missing
+chat-uploaded files entirely. The streaming path also ignored file role messages.
+
+This test validates:
+1. Chat-uploaded tabular files are detected in conversation history (file role)
+2. Filenames are collected into chat_tabular_files set for both blob and inline cases
+3. The streaming path properly handles file role messages (not just user/assistant)
+4. The mini SK trigger block activates when chat_tabular_files is non-empty
+"""
+
+import sys
+import os
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'application', 'single_app'))
+
+
+def test_chat_tabular_file_detection():
+    """Test that chat-uploaded tabular files are detected from file role messages."""
+    print("Testing chat-uploaded tabular file detection...")
+
+    try:
+        # Simulate conversation messages as they would appear from Cosmos DB
+        recent_messages = [
+            {
+                'role': 'file',
+                'filename': 'Sample_-_Superstore_1.xlsx',
+                'file_content': '',
+                'is_table': True,
+                'file_content_source': 'blob',
+                'metadata': {},
+            },
+            {
+                'role': 'user',
+                'content': 'analyze sales/profit',
+                'metadata': {},
+            },
+        ]
+
+        # Simulate the conversation history building logic (streaming path)
+        conversation_history_for_api = []
+        allowed_roles_in_history = ['user', 'assistant']
+        max_file_content_length_in_history = 50000
+        max_tabular_content_length_in_history = 50000
+        chat_tabular_files = set()
+
+        for message in recent_messages:
+            role = message.get('role')
+            content = message.get('content', '')
+
+            if role in allowed_roles_in_history:
+                conversation_history_for_api.append({
+                    'role': role,
+                    'content': content
+                })
+            elif role == 'file':
+                filename = message.get('filename', 'uploaded_file')
+                file_content = message.get('file_content', '')
+                is_table = message.get('is_table', False)
+                file_content_source = message.get('file_content_source', '')
+
+                if is_table and file_content_source == 'blob':
+                    chat_tabular_files.add(filename)
+                    conversation_history_for_api.append({
+                        'role': 'system',
+                        'content': (
+                            f"[User uploaded a tabular data file named '{filename}'. "
+                            f"The file is stored in blob storage and available for analysis. "
+                            f"The file source is 'chat'.]"
+                        )
+                    })
+
+        # Verify file was detected
+        assert 'Sample_-_Superstore_1.xlsx' in chat_tabular_files, \
+            f"Expected xlsx file in chat_tabular_files, got: {chat_tabular_files}"
+
+        # Verify system message was added for the file
+        system_msgs = [m for m in conversation_history_for_api if m['role'] == 'system']
+        assert len(system_msgs) == 1, f"Expected 1 system message, got {len(system_msgs)}"
+        assert 'Sample_-_Superstore_1.xlsx' in system_msgs[0]['content'], \
+            "System message should reference the uploaded file"
+
+        # Verify user message was included
+        user_msgs = [m for m in conversation_history_for_api if m['role'] == 'user']
+        assert len(user_msgs) == 1, f"Expected 1 user message, got {len(user_msgs)}"
+        assert user_msgs[0]['content'] == 'analyze sales/profit', \
+            "User message content should be preserved"
+
+        print("Test passed!")
+        return True
+
+    except Exception as e:
+        print(f"Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_non_tabular_file_handling():
+    """Test that non-tabular files are handled with content preview."""
+    print("Testing non-tabular file handling in streaming path...")
+
+    try:
+        recent_messages = [
+            {
+                'role': 'file',
+                'filename': 'report.txt',
+                'file_content': 'This is the report content.',
+                'is_table': False,
+                'file_content_source': 'inline',
+                'metadata': {},
+            },
+            {
+                'role': 'user',
+                'content': 'summarize the report',
+                'metadata': {},
+            },
+        ]
+
+        conversation_history_for_api = []
+        allowed_roles_in_history = ['user', 'assistant']
+        max_file_content_length_in_history = 50000
+        max_tabular_content_length_in_history = 50000
+        chat_tabular_files = set()
+
+        for message in recent_messages:
+            role = message.get('role')
+            content = message.get('content', '')
+
+            if role in allowed_roles_in_history:
+                conversation_history_for_api.append({
+                    'role': role,
+                    'content': content
+                })
+            elif role == 'file':
+                filename = message.get('filename', 'uploaded_file')
+                file_content = message.get('file_content', '')
+                is_table = message.get('is_table', False)
+                file_content_source = message.get('file_content_source', '')
+
+                if is_table and file_content_source == 'blob':
+                    chat_tabular_files.add(filename)
+                    conversation_history_for_api.append({
+                        'role': 'system',
+                        'content': f"[User uploaded a tabular data file named '{filename}'.]"
+                    })
+                else:
+                    content_limit = (
+                        max_tabular_content_length_in_history if is_table
+                        else max_file_content_length_in_history
+                    )
+                    display_content = file_content[:content_limit]
+                    if len(file_content) > content_limit:
+                        display_content += "..."
+
+                    conversation_history_for_api.append({
+                        'role': 'system',
+                        'content': (
+                            f"[User uploaded a file named '{filename}'. "
+                            f"Content preview:\n{display_content}]\n"
+                            f"Use this file context if relevant."
+                        )
+                    })
+
+        # Non-tabular files should NOT be in chat_tabular_files
+        assert len(chat_tabular_files) == 0, \
+            f"Non-tabular files should not be tracked, got: {chat_tabular_files}"
+
+        # System message should contain file content preview
+        system_msgs = [m for m in conversation_history_for_api if m['role'] == 'system']
+        assert len(system_msgs) == 1, f"Expected 1 system message, got {len(system_msgs)}"
+        assert 'report.txt' in system_msgs[0]['content'], \
+            "System message should reference the file"
+        assert 'This is the report content.' in system_msgs[0]['content'], \
+            "System message should include file content preview"
+
+        print("Test passed!")
+        return True
+
+    except Exception as e:
+        print(f"Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_multiple_tabular_files():
+    """Test detection of multiple tabular files uploaded to chat."""
+    print("Testing multiple tabular file detection...")
+
+    try:
+        recent_messages = [
+            {
+                'role': 'file',
+                'filename': 'sales_2024.xlsx',
+                'file_content': '',
+                'is_table': True,
+                'file_content_source': 'blob',
+                'metadata': {},
+            },
+            {
+                'role': 'file',
+                'filename': 'inventory.csv',
+                'file_content': 'item,count\nwidget,100',
+                'is_table': True,
+                'file_content_source': 'blob',
+                'metadata': {},
+            },
+            {
+                'role': 'user',
+                'content': 'compare sales to inventory',
+                'metadata': {},
+            },
+        ]
+
+        chat_tabular_files = set()
+        conversation_history_for_api = []
+        allowed_roles_in_history = ['user', 'assistant']
+
+        for message in recent_messages:
+            role = message.get('role')
+            content = message.get('content', '')
+
+            if role in allowed_roles_in_history:
+                conversation_history_for_api.append({'role': role, 'content': content})
+            elif role == 'file':
+                filename = message.get('filename', 'uploaded_file')
+                is_table = message.get('is_table', False)
+                file_content_source = message.get('file_content_source', '')
+
+                if is_table and file_content_source == 'blob':
+                    chat_tabular_files.add(filename)
+                    conversation_history_for_api.append({
+                        'role': 'system',
+                        'content': f"[Tabular file '{filename}' available for analysis.]"
+                    })
+
+        assert len(chat_tabular_files) == 2, \
+            f"Expected 2 tabular files, got {len(chat_tabular_files)}"
+        assert 'sales_2024.xlsx' in chat_tabular_files, "Missing sales_2024.xlsx"
+        assert 'inventory.csv' in chat_tabular_files, "Missing inventory.csv"
+
+        print("Test passed!")
+        return True
+
+    except Exception as e:
+        print(f"Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_inline_tabular_not_tracked_for_sk():
+    """Test that inline tabular files (not blob) are NOT tracked for SK analysis."""
+    print("Testing inline tabular files are not tracked for SK analysis...")
+
+    try:
+        recent_messages = [
+            {
+                'role': 'file',
+                'filename': 'data.csv',
+                'file_content': 'col1,col2\n1,2\n3,4',
+                'is_table': True,
+                'file_content_source': 'inline',  # Not blob
+                'metadata': {},
+            },
+        ]
+
+        chat_tabular_files = set()
+
+        for message in recent_messages:
+            role = message.get('role')
+            if role == 'file':
+                is_table = message.get('is_table', False)
+                file_content_source = message.get('file_content_source', '')
+                filename = message.get('filename', '')
+
+                if is_table and file_content_source == 'blob':
+                    chat_tabular_files.add(filename)
+
+        assert len(chat_tabular_files) == 0, \
+            f"Inline tabular files should not be tracked for SK, got: {chat_tabular_files}"
+
+        print("Test passed!")
+        return True
+
+    except Exception as e:
+        print(f"Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == "__main__":
+    results = []
+    results.append(test_chat_tabular_file_detection())
+    results.append(test_non_tabular_file_handling())
+    results.append(test_multiple_tabular_files())
+    results.append(test_inline_tabular_not_tracked_for_sk())
+
+    print(f"\n{'='*50}")
+    print(f"Results: {sum(results)}/{len(results)} tests passed")
+    if all(results):
+        print("All tests passed!")
+        sys.exit(0)
+    else:
+        print("Some tests failed!")
+        sys.exit(1)
diff --git a/functional_tests/test_conversation_export.py b/functional_tests/test_conversation_export.py
index cb8e56d0..654059b1 100644
--- a/functional_tests/test_conversation_export.py
+++ b/functional_tests/test_conversation_export.py
@@ -1,365 +1,545 @@
 #!/usr/bin/env python3
 # test_conversation_export.py
 """
-Functional test for conversation export feature.
-Version: 0.237.050
-Implemented in: 0.237.050
-
-This test validates the conversation export backend endpoint
-and ensures JSON/Markdown formats and single/ZIP packaging work correctly.
+Functional test for conversation export enhancements.
+Version: 0.239.029
+Implemented in: 0.239.022 (base), 0.239.023 (PDF export), 0.239.025 (tag formatting fix), 0.239.028 (summary token budget fix)
+
+This test ensures conversation export now includes normalized and raw citations,
+processing thoughts, transcript-style Markdown appendices, deleted-message filtering,
+optional summary-intro metadata, PDF export with chat-bubble styling, and properly
+formatted tag/classification rendering.
 """
 
-import sys
-import os
+import io
 import json
+import os
+import sys
 import zipfile
-import io
 
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app'))
 
+from route_backend_conversation_export import (  # noqa: E402
+    _build_citation_counts,
+    _build_pdf_html_body,
+    _collect_raw_citation_buckets,
+    _conversation_to_markdown,
+    _conversation_to_pdf_bytes,
+    _filter_messages_for_export,
+    _format_tag,
+    _normalize_citations,
+    _normalize_content,
+    _pdf_bubble_class,
+    _safe_filename,
+    _sanitize_conversation,
+    _sanitize_message,
+    _truncate_for_summary,
+)
+
+
+def test_filter_messages_for_export():
+    """Test that deleted and inactive-thread messages are excluded."""
+    print("🔍 Testing export message filtering...")
 
-def test_sanitize_conversation():
-    """Test that _sanitize_conversation strips internal fields."""
-    print("🔍 Testing _sanitize_conversation...")
-
-    raw_conversation = {
-        'id': 'conv-123',
-        'title': 'Test Conversation',
-        'last_updated': '2025-01-01T00:00:00Z',
-        'chat_type': 'personal',
-        'tags': ['test'],
-        'is_pinned': False,
-        'context': [],
-        'user_id': 'secret-user-id',
-        '_rid': 'cosmos-internal-rid',
-        '_self': 'cosmos-self-link',
-        '_etag': 'some-etag',
-        '_attachments': 'attachments',
-        '_ts': 1234567890,
-        'partition_key': 'should-not-appear'
-    }
+    messages = [
+        {'id': 'm1', 'role': 'user', 'content': 'Keep me', 'metadata': {}},
+        {'id': 'm2', 'role': 'assistant', 'content': 'Delete me', 'metadata': {'is_deleted': True}},
+        {'id': 'm3', 'role': 'assistant', 'content': 'Inactive retry', 'metadata': {'thread_info': {'active_thread': False}}},
+        {'id': 'm4', 'role': 'assistant', 'content': 'Active reply', 'metadata': {'thread_info': {'active_thread': True}}},
+        {'id': 'm5', 'role': 'user', 'content': 'Legacy message', 'metadata': {'thread_info': {}}},
+    ]
 
-    # Import after path setup — may fail if dependencies aren't installed
-    try:
-        from route_backend_conversation_export import register_route_backend_conversation_export
-        print("  Module imported successfully (dependencies available)")
-    except ImportError as ie:
-        print(f"  Skipping import test (missing dependency: {ie})")
-        print("  Verifying sanitization logic inline instead...")
-
-    # We test the logic manually since inner functions are not directly accessible
-    sanitized = {
-        'id': raw_conversation.get('id'),
-        'title': raw_conversation.get('title', 'Untitled'),
-        'last_updated': raw_conversation.get('last_updated', ''),
-        'chat_type': raw_conversation.get('chat_type', 'personal'),
-        'tags': raw_conversation.get('tags', []),
-        'is_pinned': raw_conversation.get('is_pinned', False),
-        'context': raw_conversation.get('context', [])
-    }
+    filtered = _filter_messages_for_export(messages)
+    filtered_ids = [message['id'] for message in filtered]
 
-    assert 'id' in sanitized, "Should retain id"
-    assert 'title' in sanitized, "Should retain title"
-    assert 'user_id' not in sanitized, "Should strip user_id"
-    assert '_rid' not in sanitized, "Should strip Cosmos internal fields"
-    assert '_etag' not in sanitized, "Should strip _etag"
-    assert 'partition_key' not in sanitized, "Should strip partition_key"
+    assert filtered_ids == ['m1', 'm4', 'm5'], f"Unexpected filtered IDs: {filtered_ids}"
 
-    print("✅ _sanitize_conversation test passed!")
+    print("✅ Export message filtering test passed!")
     return True
 
 
-def test_sanitize_message():
-    """Test that _sanitize_message strips internal fields."""
-    print("🔍 Testing _sanitize_message...")
+def test_sanitize_message_with_citations_and_thoughts():
+    """Test that sanitized messages retain normalized/raw citations and thoughts."""
+    print("🔍 Testing message sanitization with citations and thoughts...")
 
-    raw_message = {
-        'id': 'msg-456',
+    assistant_message = {
+        'id': 'assistant-1',
         'role': 'assistant',
-        'content': 'Hello, how can I help?',
-        'timestamp': '2025-01-01T00:00:01Z',
-        'citations': [{'title': 'Doc1', 'url': 'https://example.com'}],
-        'conversation_id': 'conv-123',
-        'user_id': 'secret-user-id',
-        '_rid': 'cosmos-internal',
-        'metadata': {'thread_info': {'active_thread': True}},
+        'content': [{'type': 'text', 'text': 'Answer with evidence.'}],
+        'timestamp': '2026-03-06T12:00:02Z',
+        'augmented': True,
+        'hybrid_citations': [
+            {
+                'file_name': 'paper.pdf',
+                'page_number': 4,
+                'citation_id': 'doc-1_4',
+                'chunk_id': 'chunk-1',
+                'metadata_type': 'abstract',
+                'metadata_content': 'Study abstract text.'
+            }
+        ],
+        'web_search_citations': [
+            {'title': 'Example Source', 'url': 'https://example.com/source'}
+        ],
+        'agent_citations': [
+            {
+                'tool_name': 'web_lookup',
+                'function_name': 'azure_ai_foundry_web_search',
+                'plugin_name': 'azure_ai_foundry',
+                'function_arguments': {'query': 'test'},
+                'function_result': {'answer': 'done'},
+                'timestamp': '2026-03-06T12:00:01Z',
+                'success': True
+            }
+        ],
+        'metadata': {
+            'reasoning_effort': 'medium',
+            'token_usage': {
+                'prompt_tokens': 10,
+                'completion_tokens': 20,
+                'total_tokens': 30
+            }
+        },
+        'model_deployment_name': 'gpt-5-mini',
+        'hybridsearch_query': 'refined search query'
     }
+    thoughts = [
+        {
+            'step_index': 0,
+            'step_type': 'search',
+            'content': 'Searching documents...',
+            'detail': 'query=refined search query',
+            'duration_ms': 123,
+            'timestamp': '2026-03-06T12:00:01Z'
+        }
+    ]
 
-    result = {
-        'role': raw_message.get('role', ''),
-        'content': raw_message.get('content', ''),
-        'timestamp': raw_message.get('timestamp', ''),
-    }
-    if raw_message.get('citations'):
-        result['citations'] = raw_message['citations']
-
-    assert result['role'] == 'assistant', "Should retain role"
-    assert result['content'] == 'Hello, how can I help?', "Should retain content"
-    assert 'citations' in result, "Should retain citations"
-    assert 'user_id' not in result, "Should strip user_id"
-    assert '_rid' not in result, "Should strip Cosmos internal fields"
-    assert 'conversation_id' not in result, "Should strip conversation_id"
-    assert 'metadata' not in result, "Should strip metadata"
-
-    print("✅ _sanitize_message test passed!")
+    sanitized = _sanitize_message(assistant_message, sequence_index=2, transcript_index=2, thoughts=thoughts)
+
+    assert sanitized['content_text'] == 'Answer with evidence.', 'Content should normalize list-based text.'
+    assert sanitized['label'] == 'Turn 2', 'Transcript messages should use turn labels.'
+    assert sanitized['citation_counts']['document'] == 1, 'Document citation count should be preserved.'
+    assert sanitized['citation_counts']['web'] == 1, 'Web citation count should be preserved.'
+    assert sanitized['citation_counts']['agent_tool'] == 1, 'Agent citation count should be preserved.'
+    assert len(sanitized['citations']) == 3, 'Normalized citations should include all raw buckets.'
+    assert sanitized['hybrid_citations'][0]['file_name'] == 'paper.pdf', 'Raw hybrid citations should be preserved.'
+    assert sanitized['thoughts'][0]['step_type'] == 'search', 'Thoughts should be attached to the message.'
+    assert sanitized['details']['generation']['model_deployment'] == 'gpt-5-mini', 'Generation details should include the model.'
+
+    print("✅ Message sanitization test passed!")
+    return True
+
+
+def test_conversation_metadata_and_json_shape():
+    """Test that conversation-level metadata captures counts for export JSON."""
+    print("🔍 Testing conversation metadata and JSON shape...")
+
+    messages = [
+        {
+            'id': 'u1',
+            'role': 'user',
+            'is_transcript_message': True,
+            'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+            'thoughts': []
+        },
+        {
+            'id': 'a1',
+            'role': 'assistant',
+            'is_transcript_message': True,
+            'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 0, 'legacy': 0, 'total': 2},
+            'thoughts': [{'step_type': 'search'}]
+        },
+        {
+            'id': 'f1',
+            'role': 'file',
+            'is_transcript_message': False,
+            'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+            'thoughts': []
+        }
+    ]
+
+    conversation = _sanitize_conversation(
+        {
+            'id': 'conv-123',
+            'title': 'Export Test',
+            'last_updated': '2026-03-06T12:00:00Z',
+            'chat_type': 'personal',
+            'tags': ['export', 'thoughts'],
+            'context': ['workspace-a'],
+            'classification': ['research'],
+            'strict': False,
+            'is_pinned': True,
+            'scope_locked': True,
+            'locked_contexts': ['workspace-a']
+        },
+        messages=messages,
+        role_counts={'user': 1, 'assistant': 1, 'file': 1},
+        citation_counts={'document': 1, 'web': 1, 'agent_tool': 0, 'legacy': 0, 'total': 2},
+        thought_count=1
+    )
+
+    exported = [{'conversation': conversation, 'summary_intro': {'enabled': False, 'generated': False}, 'messages': messages}]
+    parsed = json.loads(json.dumps(exported, indent=2, ensure_ascii=False, default=str))
+
+    assert parsed[0]['conversation']['transcript_message_count'] == 2, 'Transcript count should exclude supplemental messages.'
+    assert parsed[0]['conversation']['citation_counts']['total'] == 2, 'Conversation citation counts should be included.'
+    assert parsed[0]['summary_intro']['enabled'] is False, 'Summary intro status should be included in JSON.'
+
+    print("✅ Conversation metadata and JSON shape test passed!")
     return True
 
 
-def test_conversation_to_markdown():
-    """Test markdown generation from a conversation entry."""
-    print("🔍 Testing markdown generation...")
+def test_markdown_export_structure():
+    """Test transcript-style Markdown with appendices, citations, and thoughts."""
+    print("🔍 Testing Markdown export structure...")
 
     entry = {
         'conversation': {
             'id': 'conv-123',
-            'title': 'My Test Chat',
-            'last_updated': '2025-01-01T12:00:00Z',
+            'title': 'My Exported Chat',
+            'last_updated': '2026-03-06T12:00:00Z',
             'chat_type': 'personal',
-            'tags': ['important', 'test'],
+            'tags': ['science'],
+            'classification': ['research'],
+            'context': ['workspace-a'],
+            'strict': False,
             'is_pinned': False,
-            'context': []
+            'scope_locked': True,
+            'locked_contexts': ['workspace-a'],
+            'message_count': 3,
+            'message_counts_by_role': {'user': 1, 'assistant': 1, 'file': 1},
+            'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 0, 'total': 3},
+            'thought_count': 1
+        },
+        'summary_intro': {
+            'enabled': True,
+            'generated': True,
+            'model_deployment': 'gpt-5-mini',
+            'generated_at': '2026-03-06T12:05:00Z',
+            'content': 'A concise abstract.\n\n- Key point one\n- Key point two',
+            'error': None
         },
         'messages': [
             {
+                'id': 'u1',
                 'role': 'user',
-                'content': 'Hello!',
-                'timestamp': '2025-01-01T12:00:01Z'
+                'speaker_label': 'User',
+                'label': 'Turn 1',
+                'sequence_index': 1,
+                'transcript_index': 1,
+                'is_transcript_message': True,
+                'timestamp': '2026-03-06T12:00:01Z',
+                'content': 'What did the paper conclude?',
+                'content_text': 'What did the paper conclude?',
+                'details': {'interaction_mode': {'workspace_search': {'search_enabled': True, 'document_scope': 'personal'}}},
+                'citations': [],
+                'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+                'thoughts': [],
+                'legacy_citations': [],
+                'hybrid_citations': [],
+                'web_search_citations': [],
+                'agent_citations': []
             },
             {
+                'id': 'a1',
                 'role': 'assistant',
-                'content': 'Hi there! How can I help you?',
-                'timestamp': '2025-01-01T12:00:02Z',
-                'citations': [{'title': 'Doc1'}]
+                'speaker_label': 'Assistant',
+                'label': 'Turn 2',
+                'sequence_index': 2,
+                'transcript_index': 2,
+                'is_transcript_message': True,
+                'timestamp': '2026-03-06T12:00:02Z',
+                'content': 'The paper concluded the intervention improved outcomes.',
+                'content_text': 'The paper concluded the intervention improved outcomes.',
+                'details': {'generation': {'model_deployment': 'gpt-5-mini', 'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 0, 'total': 3}}},
+                'citations': [
+                    {'citation_type': 'document', 'label': 'paper.pdf — Page 4', 'citation_id': 'doc-1_4', 'page_number': 4, 'classification': 'research', 'metadata_type': 'abstract', 'metadata_content': 'Study abstract text.'},
+                    {'citation_type': 'web', 'label': 'Example Source', 'title': 'Example Source', 'url': 'https://example.com/source'},
+                    {'citation_type': 'agent_tool', 'label': 'web_lookup'}
+                ],
+                'citation_counts': {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 0, 'total': 3},
+                'thoughts': [{'step_type': 'search', 'content': 'Searching documents...', 'detail': 'query=paper outcome', 'duration_ms': 88, 'timestamp': '2026-03-06T12:00:01Z'}],
+                'legacy_citations': [],
+                'hybrid_citations': [{'file_name': 'paper.pdf'}],
+                'web_search_citations': [{'title': 'Example Source', 'url': 'https://example.com/source'}],
+                'agent_citations': [{'tool_name': 'web_lookup', 'function_name': 'azure_ai_foundry_web_search', 'plugin_name': 'azure_ai_foundry', 'function_arguments': {'query': 'paper outcome'}, 'function_result': {'answer': 'done'}, 'timestamp': '2026-03-06T12:00:01Z', 'success': True}]
+            },
+            {
+                'id': 'f1',
+                'role': 'file',
+                'speaker_label': 'File',
+                'label': 'Message 3',
+                'sequence_index': 3,
+                'transcript_index': None,
+                'is_transcript_message': False,
+                'timestamp': '2026-03-06T12:00:00Z',
+                'content': 'paper.pdf',
+                'content_text': 'paper.pdf',
+                'details': {'message_context': {'filename': 'paper.pdf'}},
+                'citations': [],
+                'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+                'thoughts': [],
+                'legacy_citations': [],
+                'hybrid_citations': [],
+                'web_search_citations': [],
+                'agent_citations': []
             }
         ]
     }
 
-    # Replicate the markdown conversion logic
-    conv = entry['conversation']
-    messages = entry['messages']
-    lines = []
-    lines.append(f"# {conv['title']}")
-    lines.append('')
-    lines.append(f"**Last Updated:** {conv['last_updated']}  ")
-    lines.append(f"**Chat Type:** {conv['chat_type']}  ")
-    if conv.get('tags'):
-        lines.append(f"**Tags:** {', '.join(conv['tags'])}  ")
-    lines.append(f"**Messages:** {len(messages)}  ")
-    lines.append('')
-    lines.append('---')
-    lines.append('')
-
-    for msg in messages:
-        role = msg.get('role', 'unknown')
-        role_label = role.capitalize()
-        if role == 'assistant':
-            role_label = 'Assistant'
-        elif role == 'user':
-            role_label = 'User'
-        lines.append(f"### {role_label}")
-        if msg.get('timestamp'):
-            lines.append(f"*{msg['timestamp']}*")
-        lines.append('')
-        lines.append(msg.get('content', ''))
-        lines.append('')
-        if msg.get('citations'):
-            lines.append('**Citations:**')
-            for cit in msg['citations']:
-                if isinstance(cit, dict):
-                    source = cit.get('title') or cit.get('filepath') or cit.get('url', 'Unknown')
-                    lines.append(f"- {source}")
-            lines.append('')
-        lines.append('---')
-        lines.append('')
-
-    markdown = '\n'.join(lines)
-
-    assert '# My Test Chat' in markdown, "Should have title as H1"
-    assert '**Last Updated:**' in markdown, "Should have last updated"
-    assert '**Tags:** important, test' in markdown, "Should list tags"
-    assert '### User' in markdown, "Should have user heading"
-    assert '### Assistant' in markdown, "Should have assistant heading"
-    assert 'Hello!' in markdown, "Should contain user message"
-    assert 'Hi there! How can I help you?' in markdown, "Should contain assistant reply"
-    assert '**Citations:**' in markdown, "Should include citations section"
-    assert '- Doc1' in markdown, "Should list citation title"
-
-    print("✅ Markdown generation test passed!")
+    markdown = _conversation_to_markdown(entry)
+
+    assert '# My Exported Chat' in markdown, 'Markdown should include the title heading.'
+    assert '## Abstract' in markdown, 'Markdown should include the abstract section.'
+    assert '## Transcript' in markdown, 'Markdown should include the transcript section.'
+    assert '## Appendix B — Message Details' in markdown, 'Markdown should include the message-details appendix.'
+    assert '## Appendix C — References' in markdown, 'Markdown should include the references appendix.'
+    assert '## Appendix D — Processing Thoughts' in markdown, 'Markdown should include the thoughts appendix.'
+    assert '## Appendix E — Supplemental Messages' in markdown, 'Markdown should include supplemental messages.'
+    assert 'paper.pdf — Page 4' in markdown, 'Document citation labels should appear in references.'
+    assert 'Example Source' in markdown, 'Web citations should appear in references.'
+    assert 'Searching documents...' in markdown, 'Thought content should appear in the appendix.'
+
+    print("✅ Markdown export structure test passed!")
     return True
 
 
-def test_json_export_structure():
-    """Test that JSON export produces the expected structure."""
-    print("🔍 Testing JSON export structure...")
+def test_safe_filename_and_zip_packaging():
+    """Test filename sanitization and ZIP naming consistency."""
+    print("🔍 Testing safe filename and ZIP packaging...")
+
+    assert _safe_filename('Normal Title') == 'Normal_Title', 'Spaces should become underscores.'
+    assert _safe_filename('File/With:Bad*Chars') == 'File_With_Bad_Chars', 'Unsafe characters should be replaced.'
+    assert _safe_filename('A' * 100) == 'A' * 50, 'Long names should be truncated.'
+    assert _safe_filename('') == 'Untitled', 'Empty titles should use Untitled.'
 
     exported = [
-        {
-            'conversation': {
-                'id': 'conv-abc',
-                'title': 'Test Convo',
-                'last_updated': '2025-01-01T00:00:00Z',
-                'chat_type': 'personal',
-                'tags': [],
-                'is_pinned': False,
-                'context': []
-            },
-            'messages': [
-                {'role': 'user', 'content': 'Hello', 'timestamp': '2025-01-01T00:00:01Z'},
-                {'role': 'assistant', 'content': 'World', 'timestamp': '2025-01-01T00:00:02Z'}
-            ]
-        }
+        {'conversation': {'id': 'conv-001-extra', 'title': 'First Chat'}, 'messages': []},
+        {'conversation': {'id': 'conv-002-extra', 'title': 'Second Chat'}, 'messages': []}
     ]
 
-    content = json.dumps(exported, indent=2, ensure_ascii=False, default=str)
-    parsed = json.loads(content)
+    buffer = io.BytesIO()
+    with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as archive:
+        for entry in exported:
+            conversation = entry['conversation']
+            safe_title = _safe_filename(conversation.get('title', 'Untitled'))
+            conversation_id_short = conversation.get('id', 'unknown')[:8]
+            archive.writestr(f"{safe_title}_{conversation_id_short}.json", json.dumps(entry))
 
-    assert isinstance(parsed, list), "Export should be a list"
-    assert len(parsed) == 1, "Should have one conversation"
-    assert 'conversation' in parsed[0], "Each entry should have conversation"
-    assert 'messages' in parsed[0], "Each entry should have messages"
-    assert len(parsed[0]['messages']) == 2, "Should have 2 messages"
-    assert parsed[0]['conversation']['title'] == 'Test Convo', "Title should match"
+    buffer.seek(0)
+    with zipfile.ZipFile(buffer, 'r') as archive:
+        names = archive.namelist()
+        assert 'First_Chat_conv-001.json' in names, f"Unexpected ZIP entries: {names}"
+        assert 'Second_Chat_conv-002.json' in names, f"Unexpected ZIP entries: {names}"
 
-    print("✅ JSON export structure test passed!")
+    print("✅ Safe filename and ZIP packaging test passed!")
     return True
 
 
-def test_zip_packaging():
-    """Test that ZIP packaging creates valid archive with correct entries."""
-    print("🔍 Testing ZIP packaging...")
+def test_content_and_citation_helpers():
+    """Test content normalization, citation normalization, and summary truncation helpers."""
+    print("🔍 Testing helper utilities...")
 
-    exported = [
-        {
-            'conversation': {
-                'id': 'conv-001-abc-def',
-                'title': 'First Chat',
-                'last_updated': '2025-01-01',
-                'chat_type': 'personal',
-                'tags': [],
-                'is_pinned': False,
-                'context': []
-            },
-            'messages': [
-                {'role': 'user', 'content': 'Hello', 'timestamp': '2025-01-01'}
-            ]
-        },
-        {
-            'conversation': {
-                'id': 'conv-002-xyz-ghi',
-                'title': 'Second Chat',
-                'last_updated': '2025-01-02',
-                'chat_type': 'personal',
-                'tags': [],
-                'is_pinned': False,
-                'context': []
-            },
-            'messages': [
-                {'role': 'user', 'content': 'Goodbye', 'timestamp': '2025-01-02'}
-            ]
-        }
-    ]
+    content_text = _normalize_content([
+        {'type': 'text', 'text': 'Line one'},
+        {'type': 'image_url', 'image_url': {'url': 'https://example.com/image.png'}},
+        {'type': 'text', 'text': 'Line two'}
+    ])
+    assert content_text == 'Line one\n[Image]\nLine two', 'Content normalization should flatten list-based content.'
 
-    import re
+    raw_buckets = _collect_raw_citation_buckets({
+        'citations': [{'title': 'Legacy'}],
+        'hybrid_citations': [{'file_name': 'doc.pdf', 'page_number': 2}],
+        'web_search_citations': [{'title': 'Web', 'url': 'https://example.com'}],
+        'agent_citations': [{'tool_name': 'lookup'}]
+    })
+    normalized = _normalize_citations(raw_buckets)
+    counts = _build_citation_counts(normalized)
 
-    def safe_filename(title):
-        safe = re.sub(r'[<>:"/\\|?*]', '_', title)
-        safe = re.sub(r'\s+', '_', safe)
-        safe = safe.strip('_. ')
-        if len(safe) > 50:
-            safe = safe[:50]
-        return safe or 'Untitled'
+    assert counts == {'document': 1, 'web': 1, 'agent_tool': 1, 'legacy': 1, 'total': 4}, f"Unexpected citation counts: {counts}"
 
-    buffer = io.BytesIO()
-    with zipfile.ZipFile(buffer, 'w', zipfile.ZIP_DEFLATED) as zf:
-        for entry in exported:
-            conv = entry['conversation']
-            safe_title = safe_filename(conv.get('title', 'Untitled'))
-            conv_id_short = conv.get('id', 'unknown')[:8]
-            file_content = json.dumps(entry, indent=2, ensure_ascii=False, default=str)
-            file_name = f"{safe_title}_{conv_id_short}.json"
-            zf.writestr(file_name, file_content)
+    truncated = _truncate_for_summary('A' * 70000)
+    assert '[... transcript truncated for export summary generation ...]' in truncated, 'Summary truncation marker should be inserted for long transcripts.'
+    assert len(truncated) < 70000, 'Truncated summary source should be shorter than the original transcript.'
 
-    buffer.seek(0)
+    print("✅ Helper utilities test passed!")
+    return True
 
-    with zipfile.ZipFile(buffer, 'r') as zf:
-        names = zf.namelist()
-        assert len(names) == 2, f"ZIP should have 2 files, got {len(names)}"
-        assert 'First_Chat_conv-001.json' in names, f"Expected First_Chat_conv-001.json, got {names}"
-        assert 'Second_Chat_conv-002.json' in names, f"Expected Second_Chat_conv-002.json, got {names}"
 
-        # Verify content
-        first_content = json.loads(zf.read('First_Chat_conv-001.json'))
-        assert first_content['conversation']['title'] == 'First Chat'
-        assert len(first_content['messages']) == 1
+def test_pdf_export_generation():
+    """Test PDF export generates valid HTML body and PDF bytes."""
+    print("🔍 Testing PDF export generation...")
 
-    print("✅ ZIP packaging test passed!")
+    entry = {
+        'conversation': {
+            'id': 'conv-pdf-001',
+            'title': 'PDF Export Test',
+            'last_updated': '2026-03-06T14:00:00Z',
+            'chat_type': 'personal',
+            'tags': ['pdf', 'test'],
+            'classification': [],
+            'context': [],
+            'strict': False,
+            'is_pinned': False,
+            'scope_locked': False,
+            'locked_contexts': [],
+            'message_count': 2,
+            'message_counts_by_role': {'user': 1, 'assistant': 1},
+            'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+            'thought_count': 0
+        },
+        'summary_intro': {
+            'enabled': False,
+            'generated': False,
+            'model_deployment': None,
+            'generated_at': None,
+            'content': '',
+            'error': None
+        },
+        'messages': [
+            {
+                'id': 'u1',
+                'role': 'user',
+                'speaker_label': 'User',
+                'label': 'Turn 1',
+                'sequence_index': 1,
+                'transcript_index': 1,
+                'is_transcript_message': True,
+                'timestamp': '2026-03-06T14:00:01Z',
+                'content': 'Hello, can you help me?',
+                'content_text': 'Hello, can you help me?',
+                'details': {},
+                'citations': [],
+                'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+                'thoughts': [],
+                'legacy_citations': [],
+                'hybrid_citations': [],
+                'web_search_citations': [],
+                'agent_citations': []
+            },
+            {
+                'id': 'a1',
+                'role': 'assistant',
+                'speaker_label': 'Assistant',
+                'label': 'Turn 2',
+                'sequence_index': 2,
+                'transcript_index': 2,
+                'is_transcript_message': True,
+                'timestamp': '2026-03-06T14:00:02Z',
+                'content': 'Of course! How can I assist you today?',
+                'content_text': 'Of course! How can I assist you today?',
+                'details': {},
+                'citations': [],
+                'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+                'thoughts': [],
+                'legacy_citations': [],
+                'hybrid_citations': [],
+                'web_search_citations': [],
+                'agent_citations': []
+            }
+        ]
+    }
+
+    # Test HTML body generation
+    html_body = _build_pdf_html_body(entry)
+    assert '<h1>PDF Export Test</h1>' in html_body, 'HTML should contain the conversation title.'
+    assert 'user-bubble' in html_body, 'HTML should contain user bubble CSS class.'
+    assert 'assistant-bubble' in html_body, 'HTML should contain assistant bubble CSS class.'
+    assert 'Transcript' in html_body, 'HTML should contain Transcript section.'
+    assert 'Appendix A' in html_body, 'HTML should contain Appendix A.'
+    assert 'Hello, can you help me?' in html_body, 'HTML should contain user message content.'
+    assert 'Of course! How can I assist you today?' in html_body, 'HTML should contain assistant message content.'
+
+    # Test bubble class helper
+    assert _pdf_bubble_class('user') == 'user-bubble', 'User role should map to user-bubble.'
+    assert _pdf_bubble_class('assistant') == 'assistant-bubble', 'Assistant role should map to assistant-bubble.'
+    assert _pdf_bubble_class('system') == 'system-bubble', 'System role should map to system-bubble.'
+    assert _pdf_bubble_class('file') == 'file-bubble', 'File role should map to file-bubble.'
+    assert _pdf_bubble_class('unknown') == 'other-bubble', 'Unknown roles should map to other-bubble.'
+
+    # Test PDF bytes generation
+    pdf_bytes = _conversation_to_pdf_bytes(entry)
+    assert isinstance(pdf_bytes, bytes), 'PDF output should be bytes.'
+    assert pdf_bytes[:5] == b'%PDF-', f'PDF should start with %PDF- header, got: {pdf_bytes[:10]}'
+    assert len(pdf_bytes) > 100, 'PDF should have a reasonable size.'
+
+    print("✅ PDF export generation test passed!")
     return True
 
 
-def test_safe_filename():
-    """Test filename sanitization."""
-    print("🔍 Testing safe filename generation...")
+def test_tag_formatting():
+    """Test that dict-style tags and classifications render as readable strings."""
+    print("🔍 Testing tag/classification formatting...")
 
-    import re
+    # Category/value tag
+    assert _format_tag({'category': 'model', 'value': 'gpt-5'}) == 'model: gpt-5'
 
-    def safe_filename(title):
-        safe = re.sub(r'[<>:"/\\|?*]', '_', title)
-        safe = re.sub(r'\s+', '_', safe)
-        safe = safe.strip('_. ')
-        if len(safe) > 50:
-            safe = safe[:50]
-        return safe or 'Untitled'
+    # Participant tag with name
+    assert _format_tag({'category': 'participant', 'name': 'Alice', 'user_id': 'u1'}) == 'participant: Alice'
 
-    assert safe_filename('Normal Title') == 'Normal_Title', "Spaces should become underscores"
-    assert safe_filename('File/With:Bad*Chars') == 'File_With_Bad_Chars', "Bad chars should be replaced"
-    assert safe_filename('A' * 100) == 'A' * 50, "Long names should be truncated"
-    assert safe_filename('') == 'Untitled', "Empty should become Untitled"
-    assert safe_filename('   ') == 'Untitled', "Whitespace-only should become Untitled"
+    # Participant tag with email fallback
+    assert _format_tag({'category': 'participant', 'email': 'bob@test.com'}) == 'participant: bob@test.com'
 
-    print("✅ Safe filename test passed!")
-    return True
+    # Document tag with title
+    assert _format_tag({'category': 'document', 'title': 'Study.pdf', 'document_id': 'd1'}) == 'document: Study.pdf'
 
+    # Document tag with only document_id
+    assert _format_tag({'category': 'document', 'document_id': 'd1'}) == 'document: d1'
 
-def test_active_thread_filter():
-    """Test that only active thread messages are included."""
-    print("🔍 Testing active thread message filtering...")
+    # Plain string tag (older data)
+    assert _format_tag('science') == 'science'
 
-    messages = [
-        {'role': 'user', 'content': 'Hello', 'metadata': {}},
-        {'role': 'assistant', 'content': 'Reply 1', 'metadata': {'thread_info': {'active_thread': True}}},
-        {'role': 'assistant', 'content': 'Reply 2 (inactive)', 'metadata': {'thread_info': {'active_thread': False}}},
-        {'role': 'user', 'content': 'Follow up', 'metadata': {'thread_info': {}}},
-        {'role': 'assistant', 'content': 'Final', 'metadata': {'thread_info': {'active_thread': None}}},
-    ]
+    # Category only (no value)
+    assert _format_tag({'category': 'semantic'}) == 'semantic'
+
+    # Verify Markdown export uses formatted tags
+    entry = {
+        'conversation': {
+            'id': 'conv-tags-001',
+            'title': 'Tag Format Test',
+            'last_updated': '2026-03-08T00:00:00Z',
+            'chat_type': 'personal',
+            'tags': [
+                {'category': 'model', 'value': 'gpt-5'},
+                {'category': 'semantic', 'value': 'cubesats'}
+            ],
+            'classification': [],
+            'context': [],
+            'strict': False,
+            'is_pinned': False,
+            'scope_locked': False,
+            'locked_contexts': [],
+            'message_count': 0,
+            'message_counts_by_role': {},
+            'citation_counts': {'document': 0, 'web': 0, 'agent_tool': 0, 'legacy': 0, 'total': 0},
+            'thought_count': 0
+        },
+        'summary_intro': {'enabled': False, 'generated': False},
+        'messages': []
+    }
+
+    md = _conversation_to_markdown(entry)
+    assert 'model: gpt-5' in md, f'Markdown should contain formatted tag, got: {md[:500]}'
+    assert "{'category'" not in md, 'Markdown should not contain raw dict strings'
+
+    html = _build_pdf_html_body(entry)
+    assert 'model: gpt-5' in html, f'PDF HTML should contain formatted tag'
+    assert "{'category'" not in html, 'PDF HTML should not contain raw dict strings'
 
-    filtered = []
-    for msg in messages:
-        thread_info = msg.get('metadata', {}).get('thread_info', {})
-        active = thread_info.get('active_thread')
-        if active is True or active is None or 'active_thread' not in thread_info:
-            filtered.append(msg)
-
-    assert len(filtered) == 4, f"Expected 4 active messages, got {len(filtered)}"
-    contents = [m['content'] for m in filtered]
-    assert 'Reply 2 (inactive)' not in contents, "Inactive thread message should be excluded"
-    assert 'Hello' in contents, "Message without thread info should be included"
-    assert 'Reply 1' in contents, "Active=True message should be included"
-    assert 'Follow up' in contents, "Message with empty thread_info should be included"
-    assert 'Final' in contents, "Message with active_thread=None should be included"
-
-    print("✅ Active thread filter test passed!")
+    print("✅ Tag formatting test passed!")
     return True
 
 
 if __name__ == "__main__":
     tests = [
-        test_sanitize_conversation,
-        test_sanitize_message,
-        test_conversation_to_markdown,
-        test_json_export_structure,
-        test_zip_packaging,
-        test_safe_filename,
-        test_active_thread_filter
+        test_filter_messages_for_export,
+        test_sanitize_message_with_citations_and_thoughts,
+        test_conversation_metadata_and_json_shape,
+        test_markdown_export_structure,
+        test_safe_filename_and_zip_packaging,
+        test_content_and_citation_helpers,
+        test_pdf_export_generation,
+        test_tag_formatting,
     ]
     results = []
 
@@ -367,8 +547,8 @@ def test_active_thread_filter():
         print(f"\n🧪 Running {test.__name__}...")
         try:
             results.append(test())
-        except Exception as e:
-            print(f"❌ {test.__name__} failed: {e}")
+        except Exception as exc:
+            print(f"❌ {test.__name__} failed: {exc}")
             import traceback
             traceback.print_exc()
             results.append(False)
diff --git a/functional_tests/test_global_action_user_audit_fallback.py b/functional_tests/test_global_action_user_audit_fallback.py
new file mode 100644
index 00000000..b2129432
--- /dev/null
+++ b/functional_tests/test_global_action_user_audit_fallback.py
@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+# test_global_action_user_audit_fallback.py
+"""
+Functional test for global action audit user fallback.
+Version: 0.239.103
+Implemented in: 0.239.103
+
+This test ensures that save_global_action() resolves a missing user ID from
+get_current_user_id() and falls back to a non-null system audit value.
+"""
+
+# pyright: reportMissingImports=false
+
+import importlib
+import os
+import sys
+import types
+
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app'))
+
+
+class FakeContainer:
+    def __init__(self, existing_action=None):
+        self.existing_action = existing_action
+        self.upserted_body = None
+
+    def read_item(self, item, partition_key):
+        if self.existing_action is None:
+            raise Exception('not found')
+        return dict(self.existing_action)
+
+    def upsert_item(self, body):
+        self.upserted_body = dict(body)
+        self.existing_action = dict(body)
+        return dict(body)
+
+
+def _load_functions_global_actions(fake_container, current_user_id):
+    config_stub = types.ModuleType('config')
+    config_stub.cosmos_global_actions_container = fake_container
+
+    auth_stub = types.ModuleType('functions_authentication')
+    auth_stub.get_current_user_id = lambda: current_user_id
+
+    keyvault_stub = types.ModuleType('functions_keyvault')
+
+    class SecretReturnType:
+        TRIGGER = 'trigger'
+
+    keyvault_stub.SecretReturnType = SecretReturnType
+    keyvault_stub.keyvault_plugin_save_helper = lambda action_data, scope_value, scope: action_data
+    keyvault_stub.keyvault_plugin_get_helper = (
+        lambda action_data, scope_value, scope, return_type: action_data
+    )
+    keyvault_stub.keyvault_plugin_delete_helper = lambda action, scope_value, scope: None
+
+    original_modules = {}
+    for module_name, module_stub in {
+        'config': config_stub,
+        'functions_authentication': auth_stub,
+        'functions_keyvault': keyvault_stub,
+    }.items():
+        original_modules[module_name] = sys.modules.get(module_name)
+        sys.modules[module_name] = module_stub
+
+    original_modules['functions_global_actions'] = sys.modules.get('functions_global_actions')
+    sys.modules.pop('functions_global_actions', None)
+
+    module = importlib.import_module('functions_global_actions')
+    return module, original_modules
+
+
+def _restore_modules(original_modules):
+    for module_name, original_module in original_modules.items():
+        if original_module is None:
+            sys.modules.pop(module_name, None)
+        else:
+            sys.modules[module_name] = original_module
+
+
+def test_save_global_action_uses_current_user_when_missing():
+    """Validate missing user_id is filled from get_current_user_id."""
+    print('🔍 Testing global action audit fallback to current user...')
+
+    fake_container = FakeContainer()
+    module, original_modules = _load_functions_global_actions(fake_container, 'user-123')
+
+    try:
+        result = module.save_global_action(
+            {
+                'name': 'test_plugin',
+                'displayName': 'Test Plugin',
+                'type': 'custom',
+                'description': 'Regression coverage for missing user audit fields',
+                'endpoint': 'https://example.com/plugin',
+                'auth': {'type': 'identity'},
+                'metadata': {},
+                'additionalFields': {},
+            }
+        )
+
+        assert result is not None, 'save_global_action should return the saved action'
+        assert result['created_by'] == 'user-123', 'created_by should use current user ID'
+        assert result['modified_by'] == 'user-123', 'modified_by should use current user ID'
+        assert fake_container.upserted_body['created_by'] == 'user-123'
+        assert fake_container.upserted_body['modified_by'] == 'user-123'
+
+        print('✅ Missing user_id now resolves to get_current_user_id()')
+        return True
+    finally:
+        _restore_modules(original_modules)
+
+
+def test_save_global_action_falls_back_to_system_and_repairs_null_creator():
+    """Validate update flow repairs null created_by and never persists null audit values."""
+    print('🔍 Testing global action audit fallback to system...')
+
+    existing_action = {
+        'id': 'action-1',
+        'name': 'test_plugin',
+        'created_by': None,
+        'created_at': '2026-03-01T12:00:00',
+    }
+    fake_container = FakeContainer(existing_action=existing_action)
+    module, original_modules = _load_functions_global_actions(fake_container, None)
+
+    try:
+        result = module.save_global_action(
+            {
+                'id': 'action-1',
+                'name': 'test_plugin',
+                'displayName': 'Test Plugin',
+                'type': 'custom',
+                'description': 'Regression coverage for null audit repair',
+                'endpoint': 'https://example.com/plugin',
+                'auth': {'type': 'identity'},
+                'metadata': {},
+                'additionalFields': {},
+            },
+            user_id=None,
+        )
+
+        assert result is not None, 'save_global_action should return the saved action'
+        assert result['created_by'] == 'system', 'created_by should fall back to system'
+        assert result['modified_by'] == 'system', 'modified_by should fall back to system'
+        assert result['created_at'] == '2026-03-01T12:00:00', 'created_at should be preserved'
+        assert fake_container.upserted_body['created_by'] == 'system'
+        assert fake_container.upserted_body['modified_by'] == 'system'
+
+        print('✅ Null audit values are repaired to a non-null system value')
+        return True
+    finally:
+        _restore_modules(original_modules)
+
+
+if __name__ == '__main__':
+    tests = [
+        test_save_global_action_uses_current_user_when_missing,
+        test_save_global_action_falls_back_to_system_and_repairs_null_creator,
+    ]
+
+    results = []
+    for test in tests:
+        print(f'\n🧪 Running {test.__name__}...')
+        try:
+            results.append(test())
+        except Exception as exc:
+            print(f'❌ {test.__name__} failed: {exc}')
+            import traceback
+            traceback.print_exc()
+            results.append(False)
+
+    success = all(results)
+    print(f'\n📊 Results: {sum(bool(result) for result in results)}/{len(results)} tests passed')
+    sys.exit(0 if success else 1)
\ No newline at end of file
diff --git a/functional_tests/test_persistent_conversation_summary.py b/functional_tests/test_persistent_conversation_summary.py
new file mode 100644
index 00000000..e8d95ffa
--- /dev/null
+++ b/functional_tests/test_persistent_conversation_summary.py
@@ -0,0 +1,279 @@
+#!/usr/bin/env python3
+# test_persistent_conversation_summary.py
+"""
+Functional test for persistent conversation summaries.
+Version: 0.239.030
+Implemented in: 0.239.030
+
+This test ensures that:
+1. generate_conversation_summary() returns properly structured summary data
+2. _build_summary_intro() uses cached summary when message_time_end hasn't changed
+3. _build_summary_intro() regenerates when messages are newer than cached summary
+4. Summary data includes message_time_start and message_time_end fields
+5. The truncation helper works correctly
+"""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app'))
+
+from route_backend_conversation_export import (  # noqa: E402
+    _build_summary_intro,
+    _truncate_for_summary,
+    SUMMARY_SOURCE_CHAR_LIMIT,
+)
+
+
+def test_summary_intro_cache_hit():
+    """Test that _build_summary_intro returns cached summary when message_time_end matches."""
+    print("🔍 Testing summary cache hit (no new messages)...")
+
+    existing_summary = {
+        'content': 'Cached summary text.',
+        'model_deployment': 'gpt-4o',
+        'generated_at': '2026-01-15T10:00:00',
+        'message_time_start': '2026-01-15T09:00:00',
+        'message_time_end': '2026-01-15T09:55:00'
+    }
+
+    conversation = {
+        'id': 'conv-test-1',
+        'title': 'Test Conversation',
+        'summary': existing_summary
+    }
+
+    messages = [
+        {'role': 'user', 'content_text': 'Hello', 'speaker_label': 'USER'},
+        {'role': 'assistant', 'content_text': 'Hi there', 'speaker_label': 'ASSISTANT'}
+    ]
+
+    sanitized_conversation = {'title': 'Test Conversation'}
+
+    result = _build_summary_intro(
+        messages=messages,
+        conversation=conversation,
+        sanitized_conversation=sanitized_conversation,
+        settings={},
+        enabled=True,
+        summary_model_deployment='gpt-4o',
+        message_time_start='2026-01-15T09:00:00',
+        message_time_end='2026-01-15T09:55:00'  # Same as cached
+    )
+
+    assert result['enabled'] is True, f"Expected enabled=True, got {result['enabled']}"
+    assert result['generated'] is True, f"Expected generated=True, got {result['generated']}"
+    assert result['content'] == 'Cached summary text.', f"Expected cached content, got: {result['content']}"
+    assert result['model_deployment'] == 'gpt-4o', f"Expected gpt-4o, got: {result['model_deployment']}"
+    assert result['generated_at'] == '2026-01-15T10:00:00', f"Expected cached timestamp, got: {result['generated_at']}"
+    assert result['error'] is None, f"Expected no error, got: {result['error']}"
+
+    print("✅ Summary cache hit test passed!")
+    return True
+
+
+def test_summary_intro_cache_hit_older_message():
+    """Test cache hit when cached message_time_end is NEWER than current (edge case)."""
+    print("🔍 Testing summary cache hit with older message_time_end...")
+
+    existing_summary = {
+        'content': 'Summary covers more messages.',
+        'model_deployment': 'gpt-4o',
+        'generated_at': '2026-01-15T12:00:00',
+        'message_time_start': '2026-01-15T09:00:00',
+        'message_time_end': '2026-01-15T11:55:00'
+    }
+
+    conversation = {
+        'id': 'conv-test-2',
+        'summary': existing_summary
+    }
+
+    sanitized_conversation = {'title': 'Test'}
+
+    result = _build_summary_intro(
+        messages=[{'role': 'user', 'content_text': 'Hi', 'speaker_label': 'USER'}],
+        conversation=conversation,
+        sanitized_conversation=sanitized_conversation,
+        settings={},
+        enabled=True,
+        summary_model_deployment='gpt-4o',
+        message_time_start='2026-01-15T09:00:00',
+        message_time_end='2026-01-15T10:00:00'  # Older than cached
+    )
+
+    assert result['generated'] is True, "Should use cache when cached end >= current end"
+    assert result['content'] == 'Summary covers more messages.', "Should return cached content"
+
+    print("✅ Summary cache hit (older message) test passed!")
+    return True
+
+
+def test_summary_intro_disabled():
+    """Test that disabled summary returns immediately."""
+    print("🔍 Testing summary disabled state...")
+
+    result = _build_summary_intro(
+        messages=[],
+        conversation={'id': 'conv-disabled'},
+        sanitized_conversation={'title': 'Test'},
+        settings={},
+        enabled=False,
+        summary_model_deployment=''
+    )
+
+    assert result['enabled'] is False, f"Expected enabled=False, got {result['enabled']}"
+    assert result['generated'] is False, f"Expected generated=False, got {result['generated']}"
+    assert result['content'] == '', f"Expected empty content, got: {result['content']}"
+
+    print("✅ Summary disabled state test passed!")
+    return True
+
+
+def test_summary_intro_stale_cache():
+    """Test that a stale cache (newer messages exist) triggers regeneration attempt."""
+    print("🔍 Testing summary stale cache detection...")
+
+    existing_summary = {
+        'content': 'Old cached summary.',
+        'model_deployment': 'gpt-4o',
+        'generated_at': '2026-01-15T10:00:00',
+        'message_time_start': '2026-01-15T09:00:00',
+        'message_time_end': '2026-01-15T09:55:00'
+    }
+
+    conversation = {
+        'id': 'conv-stale',
+        'summary': existing_summary
+    }
+
+    sanitized_conversation = {'title': 'Test'}
+
+    # message_time_end is NEWER than cached — should try to regenerate
+    # Since we don't have a real OpenAI client, this will fail with an error,
+    # which confirms the cache was NOT used.
+    result = _build_summary_intro(
+        messages=[
+            {'role': 'user', 'content_text': 'New message', 'speaker_label': 'USER'}
+        ],
+        conversation=conversation,
+        sanitized_conversation=sanitized_conversation,
+        settings={},
+        enabled=True,
+        summary_model_deployment='gpt-4o',
+        message_time_start='2026-01-15T09:00:00',
+        message_time_end='2026-01-15T10:30:00'  # NEWER than cached 09:55
+    )
+
+    # Without a real GPT client, generation should fail
+    assert result['generated'] is False, "Without GPT client, generation should fail"
+    assert result['error'] is not None, "Should have an error when no GPT client available"
+    # The key assertion: it DID NOT return the cached content
+    assert result['content'] != 'Old cached summary.', "Should not use stale cached summary"
+
+    print("✅ Summary stale cache detection test passed!")
+    return True
+
+
+def test_summary_data_structure():
+    """Test that the summary_intro structure contains all expected fields."""
+    print("🔍 Testing summary data structure...")
+
+    result = _build_summary_intro(
+        messages=[],
+        conversation={'id': 'conv-struct'},
+        sanitized_conversation={'title': 'Test'},
+        settings={},
+        enabled=True,
+        summary_model_deployment='gpt-4o',
+        message_time_start=None,
+        message_time_end=None
+    )
+
+    expected_keys = {'enabled', 'generated', 'model_deployment', 'generated_at', 'content', 'error'}
+    actual_keys = set(result.keys())
+    assert expected_keys.issubset(actual_keys), f"Missing keys: {expected_keys - actual_keys}"
+
+    print("✅ Summary data structure test passed!")
+    return True
+
+
+def test_truncation_short_text():
+    """Test that short text passes through unchanged."""
+    print("🔍 Testing truncation with short text...")
+
+    short_text = "This is a short transcript."
+    result = _truncate_for_summary(short_text)
+    assert result == short_text, "Short text should pass through unchanged"
+
+    print("✅ Truncation short text test passed!")
+    return True
+
+
+def test_truncation_long_text():
+    """Test that long text gets truncated with marker."""
+    print("🔍 Testing truncation with long text...")
+
+    long_text = "A" * (SUMMARY_SOURCE_CHAR_LIMIT + 1000)
+    result = _truncate_for_summary(long_text)
+
+    assert len(result) < len(long_text), "Truncated text should be shorter"
+    assert "transcript truncated" in result, "Should contain truncation marker"
+
+    print("✅ Truncation long text test passed!")
+    return True
+
+
+def test_summary_no_content():
+    """Test that summary with no message content returns an error."""
+    print("🔍 Testing summary with empty messages...")
+
+    result = _build_summary_intro(
+        messages=[
+            {'role': 'user', 'content_text': '', 'speaker_label': 'USER'},
+            {'role': 'assistant', 'content_text': '', 'speaker_label': 'ASSISTANT'}
+        ],
+        conversation={'id': 'conv-empty'},
+        sanitized_conversation={'title': 'Test'},
+        settings={},
+        enabled=True,
+        summary_model_deployment='gpt-4o',
+        message_time_start=None,
+        message_time_end=None
+    )
+
+    assert result['generated'] is False, "Should not generate with empty content"
+    assert result['error'] is not None, "Should have an error message"
+    assert 'No message content' in result['error'], f"Unexpected error: {result['error']}"
+
+    print("✅ Summary with empty messages test passed!")
+    return True
+
+
+if __name__ == "__main__":
+    tests = [
+        test_summary_intro_cache_hit,
+        test_summary_intro_cache_hit_older_message,
+        test_summary_intro_disabled,
+        test_summary_intro_stale_cache,
+        test_summary_data_structure,
+        test_truncation_short_text,
+        test_truncation_long_text,
+        test_summary_no_content,
+    ]
+    results = []
+
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        try:
+            results.append(test())
+        except Exception as e:
+            print(f"❌ {test.__name__} failed with exception: {e}")
+            import traceback
+            traceback.print_exc()
+            results.append(False)
+
+    passed = sum(1 for r in results if r)
+    total = len(results)
+    print(f"\n📊 Results: {passed}/{total} tests passed")
+    sys.exit(0 if all(results) else 1)
diff --git a/functional_tests/test_sql_auto_schema_companion.py b/functional_tests/test_sql_auto_schema_companion.py
new file mode 100644
index 00000000..2f488c97
--- /dev/null
+++ b/functional_tests/test_sql_auto_schema_companion.py
@@ -0,0 +1,222 @@
+#!/usr/bin/env python3
+"""
+Functional test for SQL Auto Schema Companion Plugin creation.
+Version: 0.239.015
+Implemented in: 0.239.015
+
+This test ensures that when a SQL Query plugin is loaded via LoggedPluginLoader,
+a companion SQL Schema plugin is automatically created and registered in the kernel.
+This fixes the issue where agents with only a sql_query action configured would have
+no schema awareness, causing the LLM to ask for clarification instead of querying.
+
+Additionally validates that:
+- SQLQueryPlugin @kernel_function descriptions are resilient (don't demand non-existent functions)
+- The _extract_sql_schema_for_instructions fallback detects SQLQueryPlugin instances
+"""
+
+import sys
+import os
+import inspect
+
+# Add the application directory to the path for imports
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app'))
+
+
+def test_companion_schema_plugin_method_exists():
+    """Test that LoggedPluginLoader has the _auto_create_companion_schema_plugin method."""
+    print("🔍 Testing _auto_create_companion_schema_plugin method exists...")
+    
+    try:
+        from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader
+        
+        assert hasattr(LoggedPluginLoader, '_auto_create_companion_schema_plugin'), \
+            "LoggedPluginLoader must have _auto_create_companion_schema_plugin method"
+        
+        method = getattr(LoggedPluginLoader, '_auto_create_companion_schema_plugin')
+        assert callable(method), "_auto_create_companion_schema_plugin must be callable"
+        
+        # Verify method signature includes query_manifest and query_plugin_name
+        sig = inspect.signature(method)
+        param_names = list(sig.parameters.keys())
+        assert 'query_manifest' in param_names, "Method must accept query_manifest parameter"
+        assert 'query_plugin_name' in param_names, "Method must accept query_plugin_name parameter"
+        
+        print("✅ _auto_create_companion_schema_plugin method exists with correct signature!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_companion_creation_triggered_for_sql_query():
+    """Test that load_plugin_from_manifest triggers companion creation for sql_query type."""
+    print("🔍 Testing companion creation trigger in load_plugin_from_manifest...")
+    
+    try:
+        from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader
+        
+        source = inspect.getsource(LoggedPluginLoader.load_plugin_from_manifest)
+        
+        # The method should check for sql_query type and call companion creation
+        assert "plugin_type == 'sql_query'" in source, \
+            "load_plugin_from_manifest must check for sql_query plugin type"
+        assert "_auto_create_companion_schema_plugin" in source, \
+            "load_plugin_from_manifest must call _auto_create_companion_schema_plugin"
+        
+        print("✅ load_plugin_from_manifest triggers companion creation for sql_query!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_schema_plugin_name_derivation():
+    """Test that the companion schema plugin name is correctly derived."""
+    print("🔍 Testing schema plugin name derivation logic...")
+    
+    try:
+        from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader
+        
+        source = inspect.getsource(LoggedPluginLoader._auto_create_companion_schema_plugin)
+        
+        # Should handle _query suffix replacement
+        assert "endswith('_query')" in source, \
+            "Method should check for _query suffix"
+        assert "'_schema'" in source, \
+            "Method should derive _schema suffix"
+        
+        # Should check if schema plugin already exists
+        assert "self.kernel.plugins" in source, \
+            "Method should check kernel.plugins for existing schema plugin"
+        
+        # Should create SQLSchemaPlugin
+        assert "SQLSchemaPlugin" in source, \
+            "Method should create a SQLSchemaPlugin instance"
+        
+        # Should register with kernel
+        assert "_register_plugin_with_kernel" in source, \
+            "Method should register the companion plugin with the kernel"
+        
+        print("✅ Schema plugin name derivation and creation logic is correct!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_resilient_query_plugin_descriptions():
+    """Test that SQLQueryPlugin descriptions don't demand non-existent schema functions."""
+    print("🔍 Testing SQLQueryPlugin @kernel_function descriptions are resilient...")
+    
+    try:
+        from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin
+        
+        source = inspect.getsource(SQLQueryPlugin)
+        
+        # Descriptions should NOT contain "you MUST first call" which creates a hard dependency
+        assert "you MUST first call" not in source, \
+            "Descriptions should not contain 'you MUST first call' (creates hard dependency on non-existent functions)"
+        assert "You MUST first discover" not in source, \
+            "Descriptions should not contain 'You MUST first discover' (creates hard dependency)"
+        
+        # Descriptions SHOULD contain resilient language
+        assert "If the database schema is provided in your instructions" in source, \
+            "Descriptions should reference schema from instructions as primary source"
+        
+        # Should still mention the schema plugin as a fallback
+        assert "get_database_schema" in source, \
+            "Descriptions should still reference get_database_schema as a fallback option"
+        
+        print("✅ SQLQueryPlugin descriptions are resilient and don't demand non-existent functions!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_schema_extraction_fallback():
+    """Test that _extract_sql_schema_for_instructions has a fallback for SQLQueryPlugin."""
+    print("🔍 Testing _extract_sql_schema_for_instructions fallback...")
+    
+    try:
+        from semantic_kernel_loader import _extract_sql_schema_for_instructions
+        
+        source = inspect.getsource(_extract_sql_schema_for_instructions)
+        
+        # Should have fallback logic
+        assert "Fallback" in source or "fallback" in source, \
+            "Function should contain fallback logic"
+        
+        # Should check for SQLQueryPlugin
+        assert "SQLQueryPlugin" in source, \
+            "Fallback should check for SQLQueryPlugin instances"
+        
+        # Should create temporary schema extractor
+        assert "temp_manifest" in source or "temp_schema" in source, \
+            "Fallback should create a temporary schema extractor"
+        
+        print("✅ _extract_sql_schema_for_instructions has SQLQueryPlugin fallback!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_version_updated():
+    """Test that the version has been updated for this fix."""
+    print("🔍 Testing version update...")
+    
+    try:
+        # Read config.py directly to check version
+        config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 
+                                   '..', 'application', 'single_app', 'config.py')
+        with open(config_path, 'r') as f:
+            config_content = f.read()
+        
+        assert 'VERSION = "0.239.015"' in config_content, \
+            f"config.py should contain VERSION = \"0.239.015\""
+        
+        print("✅ Version correctly updated to 0.239.015!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == "__main__":
+    tests = [
+        test_companion_schema_plugin_method_exists,
+        test_companion_creation_triggered_for_sql_query,
+        test_schema_plugin_name_derivation,
+        test_resilient_query_plugin_descriptions,
+        test_schema_extraction_fallback,
+        test_version_updated,
+    ]
+    results = []
+    
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+    
+    success = all(results)
+    passed = sum(results)
+    total = len(results)
+    print(f"\n📊 Results: {passed}/{total} tests passed")
+    
+    if not success:
+        failed_tests = [t.__name__ for t, r in zip(tests, results) if not r]
+        print(f"❌ Failed tests: {', '.join(failed_tests)}")
+    
+    sys.exit(0 if success else 1)
diff --git a/functional_tests/test_sql_query_plugin_schema_awareness.py b/functional_tests/test_sql_query_plugin_schema_awareness.py
new file mode 100644
index 00000000..b16f73bd
--- /dev/null
+++ b/functional_tests/test_sql_query_plugin_schema_awareness.py
@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+"""
+Functional test for SQL Query Plugin Schema Awareness improvements.
+Version: 0.239.014
+Implemented in: 0.239.014
+
+This test ensures that the SQL Query and SQL Schema plugins have proper
+workflow-guiding descriptions in their @kernel_function decorators, metadata
+properties, and that the new query_database convenience function exists.
+These improvements ensure agents discover database schema before attempting
+to generate and execute SQL queries.
+"""
+
+import sys
+import os
+
+# Add the application directory to the path for imports
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app'))
+
+
+def test_sql_schema_plugin_descriptions():
+    """Test that SQL Schema Plugin has prescriptive workflow descriptions."""
+    print("🔍 Testing SQL Schema Plugin @kernel_function descriptions...")
+    
+    try:
+        from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+        import inspect
+        
+        source = inspect.getsource(SQLSchemaPlugin)
+        
+        # Check get_database_schema has workflow guidance
+        assert "ALWAYS call this function FIRST" in source, \
+            "get_database_schema description should contain 'ALWAYS call this function FIRST'"
+        
+        assert "before executing any SQL queries" in source, \
+            "get_database_schema description should guide calling before queries"
+        
+        # Check get_table_list has workflow guidance
+        assert "Use this function first to discover which tables are available" in source, \
+            "get_table_list description should guide discovery workflow"
+        
+        # Check get_table_schema has workflow guidance
+        assert "Call this after discovering tables" in source, \
+            "get_table_schema description should reference discovery step"
+        
+        # Check get_relationships has workflow guidance
+        assert "JOIN conditions" in source, \
+            "get_relationships description should mention JOIN conditions"
+        
+        print("✅ SQL Schema Plugin descriptions contain workflow guidance!")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_sql_query_plugin_descriptions():
+    """Test that SQL Query Plugin has prescriptive workflow descriptions."""
+    print("🔍 Testing SQL Query Plugin @kernel_function descriptions...")
+    
+    try:
+        from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin
+        import inspect
+        
+        source = inspect.getsource(SQLQueryPlugin)
+        
+        # Check execute_query has schema requirement
+        assert "you MUST first call get_database_schema or get_table_list" in source, \
+            "execute_query description should require schema discovery first"
+        
+        assert "fully qualified table names" in source, \
+            "execute_query description should mention fully qualified table names"
+        
+        # Check execute_scalar has schema requirement
+        assert "MUST first discover the database schema" in source, \
+            "execute_scalar description should require schema discovery"
+        
+        # Check validate_query has useful guidance
+        assert "pre-check complex queries" in source, \
+            "validate_query description should guide pre-checking"
+        
+        print("✅ SQL Query Plugin descriptions contain workflow guidance!")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_query_database_function_exists():
+    """Test that the new query_database convenience function exists."""
+    print("🔍 Testing query_database function existence...")
+    
+    try:
+        from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin
+        
+        # Check the function exists on the class
+        assert hasattr(SQLQueryPlugin, 'query_database'), \
+            "SQLQueryPlugin should have a query_database method"
+        
+        # Check it's in get_functions list
+        # We need to create a minimal instance to test, but we can check the class method
+        import inspect
+        source = inspect.getsource(SQLQueryPlugin.query_database)
+        
+        assert "question" in source, \
+            "query_database should accept a 'question' parameter"
+        
+        assert "query" in source, \
+            "query_database should accept a 'query' parameter"
+        
+        assert "@kernel_function" in inspect.getsource(SQLQueryPlugin), \
+            "query_database should be decorated with @kernel_function"
+        
+        print("✅ query_database function exists with correct parameters!")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_metadata_descriptions_updated():
+    """Test that metadata property descriptions include workflow guidance."""
+    print("🔍 Testing metadata property descriptions...")
+    
+    try:
+        from semantic_kernel_plugins.sql_query_plugin import SQLQueryPlugin
+        from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+        import inspect
+        
+        # Check SQL Query Plugin metadata
+        query_source = inspect.getsource(SQLQueryPlugin)
+        assert "WORKFLOW: Before executing any query" in query_source, \
+            "SQL Query Plugin metadata should contain workflow guidance"
+        
+        # Check SQL Schema Plugin metadata
+        schema_source = inspect.getsource(SQLSchemaPlugin)
+        assert "ALWAYS call get_database_schema" in schema_source, \
+            "SQL Schema Plugin metadata should contain workflow guidance"
+        
+        print("✅ Metadata descriptions include workflow guidance!")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_sql_plugin_creation_path_enabled():
+    """Test that the SQL plugin creation path is enabled in logged_plugin_loader."""
+    print("🔍 Testing SQL plugin creation path is enabled...")
+    
+    try:
+        from semantic_kernel_plugins.logged_plugin_loader import LoggedPluginLoader
+        import inspect
+        
+        source = inspect.getsource(LoggedPluginLoader._create_plugin_instance)
+        
+        # Check the SQL path is NOT commented out
+        assert "elif plugin_type in ['sql_schema', 'sql_query']:" in source, \
+            "SQL plugin creation path should be enabled (not commented out)"
+        
+        assert "return self._create_sql_plugin(manifest)" in source, \
+            "SQL plugin creation should call _create_sql_plugin"
+        
+        # Make sure it's not still commented
+        lines = source.split('\n')
+        for line in lines:
+            stripped = line.strip()
+            if "sql_schema" in stripped and "sql_query" in stripped and "elif" in stripped:
+                assert not stripped.startswith('#'), \
+                    "SQL plugin creation path should NOT be commented out"
+                break
+        
+        print("✅ SQL plugin creation path is enabled!")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_schema_injection_function_exists():
+    """Test that the schema injection helper function exists in semantic_kernel_loader."""
+    print("🔍 Testing schema injection function exists...")
+    
+    try:
+        # Read the file and check for the function
+        loader_path = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), 
+            '..', 'application', 'single_app', 'semantic_kernel_loader.py'
+        )
+        
+        with open(loader_path, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        assert "def _extract_sql_schema_for_instructions(kernel)" in content, \
+            "_extract_sql_schema_for_instructions function should exist in semantic_kernel_loader.py"
+        
+        assert "sql_schema_summary = _extract_sql_schema_for_instructions(kernel)" in content, \
+            "Schema injection should be called in the agent loading flow"
+        
+        assert "Available Database Schema" in content, \
+            "Schema injection should add 'Available Database Schema' header to instructions"
+        
+        assert "Do NOT ask the user for table or column names" in content, \
+            "Schema injection should instruct agent not to ask user for schema info"
+        
+        print("✅ Schema injection function exists and is integrated!")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == "__main__":
+    tests = [
+        test_sql_schema_plugin_descriptions,
+        test_sql_query_plugin_descriptions,
+        test_query_database_function_exists,
+        test_metadata_descriptions_updated,
+        test_sql_plugin_creation_path_enabled,
+        test_schema_injection_function_exists,
+    ]
+    results = []
+    
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+    
+    success = all(results)
+    print(f"\n📊 Results: {sum(results)}/{len(results)} tests passed")
+    sys.exit(0 if success else 1)
diff --git a/functional_tests/test_sql_schema_sys_catalog_views.py b/functional_tests/test_sql_schema_sys_catalog_views.py
new file mode 100644
index 00000000..dd1c5f05
--- /dev/null
+++ b/functional_tests/test_sql_schema_sys_catalog_views.py
@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+"""
+Functional test for SQL Schema Plugin sys.* catalog view migration.
+Version: 0.239.016
+Implemented in: 0.239.016
+
+This test ensures that the SQL Schema Plugin uses sys.tables, sys.columns,
+sys.indexes (catalog views) instead of INFORMATION_SCHEMA views for SQL Server,
+fixing the issue where get_database_schema returned empty tables dict ('tables': {})
+despite the database having tables. The sys.* views are proven to work since the
+relationships query (which uses sys.foreign_keys) always returned valid data.
+
+Also validates robust pyodbc.Row handling (no isinstance(tuple) checks).
+"""
+
+import sys
+import os
+import inspect
+
+# Add the application directory to the path for imports
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'application', 'single_app'))
+
+
+def test_tables_query_uses_sys_views():
+    """Test that SQL Server tables query uses sys.tables instead of INFORMATION_SCHEMA."""
+    print("🔍 Testing SQL Server tables query uses sys.tables...")
+
+    try:
+        from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+
+        source = inspect.getsource(SQLSchemaPlugin._get_tables_query)
+
+        # Should use sys.tables
+        assert "sys.tables" in source, \
+            "_get_tables_query should reference sys.tables"
+        assert "sys.schemas" in source, \
+            "_get_tables_query should reference sys.schemas"
+
+        # Should NOT use INFORMATION_SCHEMA.TABLES for sqlserver
+        # (other DB types still use their own patterns)
+        assert "INFORMATION_SCHEMA.TABLES" not in source, \
+            "_get_tables_query should NOT use INFORMATION_SCHEMA.TABLES (use sys.tables instead)"
+
+        print("✅ Tables query uses sys.tables!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_columns_query_uses_sys_views():
+    """Test that SQL Server columns query uses sys.columns instead of INFORMATION_SCHEMA."""
+    print("🔍 Testing SQL Server columns query uses sys.columns...")
+
+    try:
+        from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+
+        source = inspect.getsource(SQLSchemaPlugin._get_columns_query)
+
+        # Should use sys.columns
+        assert "sys.columns" in source, \
+            "_get_columns_query should reference sys.columns"
+        assert "TYPE_NAME" in source, \
+            "_get_columns_query should use TYPE_NAME() for data type resolution"
+
+        # Should NOT use INFORMATION_SCHEMA.COLUMNS for sqlserver
+        assert "INFORMATION_SCHEMA.COLUMNS" not in source, \
+            "_get_columns_query should NOT use INFORMATION_SCHEMA.COLUMNS (use sys.columns instead)"
+
+        print("✅ Columns query uses sys.columns!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_primary_keys_query_uses_sys_views():
+    """Test that SQL Server PK query uses sys.indexes instead of INFORMATION_SCHEMA."""
+    print("🔍 Testing SQL Server primary keys query uses sys.indexes...")
+
+    try:
+        from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+
+        source = inspect.getsource(SQLSchemaPlugin._get_primary_keys_query)
+
+        # Should use sys.indexes with is_primary_key
+        assert "sys.indexes" in source, \
+            "_get_primary_keys_query should reference sys.indexes"
+        assert "is_primary_key" in source, \
+            "_get_primary_keys_query should filter on is_primary_key"
+        assert "sys.index_columns" in source, \
+            "_get_primary_keys_query should reference sys.index_columns"
+
+        # Should NOT use INFORMATION_SCHEMA.KEY_COLUMN_USAGE for sqlserver section
+        # Note: PostgreSQL/MySQL sections may still use INFORMATION_SCHEMA (that's fine)
+        # We check that the sqlserver branch uses sys.* by verifying sys.indexes is present
+        assert "sys.index_columns" in source, \
+            "_get_primary_keys_query should reference sys.index_columns for SQL Server"
+
+        print("✅ Primary keys query uses sys.indexes!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_robust_row_handling_no_isinstance_tuple():
+    """Test that table row parsing doesn't rely on isinstance(tuple) checks."""
+    print("🔍 Testing robust pyodbc.Row handling...")
+
+    try:
+        from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+
+        # Check get_database_schema table iteration
+        schema_source = inspect.getsource(SQLSchemaPlugin.get_database_schema)
+
+        # Should NOT use isinstance(table, tuple) pattern
+        assert "isinstance(table, tuple)" not in schema_source, \
+            "get_database_schema should not use isinstance(table, tuple) — pyodbc.Row may not be a tuple"
+
+        # Should use try/except for robust indexing
+        assert "except (TypeError, IndexError)" in schema_source, \
+            "get_database_schema should handle TypeError/IndexError for row parsing"
+
+        # Check get_table_list too
+        list_source = inspect.getsource(SQLSchemaPlugin.get_table_list)
+        assert "isinstance(table_row, (list, tuple))" not in list_source, \
+            "get_table_list should not use isinstance tuple check"
+
+        # Check primary keys list comprehension
+        schema_data_source = inspect.getsource(SQLSchemaPlugin._get_table_schema_data)
+        assert "isinstance(pk, (list, tuple))" not in schema_data_source, \
+            "Primary key parsing should not use isinstance tuple check"
+
+        print("✅ Robust Row handling — no isinstance(tuple) dependencies!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_consistency_with_relationships_query():
+    """Test that all SQL Server queries use sys.* views consistently (like relationships)."""
+    print("🔍 Testing consistency: all SQL Server queries use sys.* views...")
+
+    try:
+        from semantic_kernel_plugins.sql_schema_plugin import SQLSchemaPlugin
+
+        # The relationships query already used sys.* — verify it still does
+        rel_source = inspect.getsource(SQLSchemaPlugin._get_relationships_data)
+        assert "sys.foreign_keys" in rel_source, \
+            "Relationships query should use sys.foreign_keys"
+        assert "sys.tables" in rel_source, \
+            "Relationships query should reference sys.tables"
+
+        # Tables query should also use sys.*
+        tables_source = inspect.getsource(SQLSchemaPlugin._get_tables_query)
+        assert "sys.tables" in tables_source
+
+        # Columns query should also use sys.*
+        cols_source = inspect.getsource(SQLSchemaPlugin._get_columns_query)
+        assert "sys.columns" in cols_source
+
+        # PK query should also use sys.*
+        pk_source = inspect.getsource(SQLSchemaPlugin._get_primary_keys_query)
+        assert "sys.indexes" in pk_source
+
+        print("✅ All SQL Server queries consistently use sys.* catalog views!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_version_updated():
+    """Test that the version has been updated for this fix."""
+    print("🔍 Testing version update...")
+
+    try:
+        config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                                   '..', 'application', 'single_app', 'config.py')
+        with open(config_path, 'r') as f:
+            config_content = f.read()
+
+        assert 'VERSION = "0.239.016"' in config_content, \
+            f"config.py should contain VERSION = \"0.239.016\""
+
+        print("✅ Version correctly updated to 0.239.016!")
+        return True
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == "__main__":
+    tests = [
+        test_tables_query_uses_sys_views,
+        test_columns_query_uses_sys_views,
+        test_primary_keys_query_uses_sys_views,
+        test_robust_row_handling_no_isinstance_tuple,
+        test_consistency_with_relationships_query,
+        test_version_updated,
+    ]
+    results = []
+
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+
+    success = all(results)
+    passed = sum(results)
+    total = len(results)
+    print(f"\n📊 Results: {passed}/{total} tests passed")
+
+    if not success:
+        failed_tests = [t.__name__ for t, r in zip(tests, results) if not r]
+        print(f"❌ Failed tests: {', '.join(failed_tests)}")
+
+    sys.exit(0 if success else 1)
diff --git a/functional_tests/test_tabular_analysis_rejects_discovery_only.py b/functional_tests/test_tabular_analysis_rejects_discovery_only.py
new file mode 100644
index 00000000..36f45dbf
--- /dev/null
+++ b/functional_tests/test_tabular_analysis_rejects_discovery_only.py
@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+# test_tabular_analysis_rejects_discovery_only.py
+"""
+Functional test for tabular analytical-only orchestration fix.
+Version: 0.239.111
+Implemented in: 0.239.111
+
+This test ensures that discovery-only tabular tool calls are not accepted as
+completed analysis for analytical questions, and that the tabular SK pass is
+restricted to analytical tools on retry attempts.
+"""
+
+import ast
+import os
+import sys
+from types import SimpleNamespace
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(ROOT_DIR)
+
+ROUTE_FILE = os.path.join(ROOT_DIR, 'application', 'single_app', 'route_backend_chats.py')
+TARGET_ASSIGNMENTS = {
+    'TABULAR_DISCOVERY_FUNCTION_NAMES',
+    'TABULAR_ANALYSIS_FUNCTION_NAMES',
+}
+TARGET_FUNCTIONS = {
+    'get_new_plugin_invocations',
+    'get_tabular_invocation_result_payload',
+    'get_tabular_invocation_error_message',
+    'split_tabular_analysis_invocations',
+    'split_tabular_plugin_invocations',
+    'filter_tabular_citation_invocations',
+}
+
+
+def load_tabular_route_helpers():
+    """Load selected constants and helpers from the chat route source."""
+    with open(ROUTE_FILE, 'r', encoding='utf-8') as file_handle:
+        route_content = file_handle.read()
+
+    parsed = ast.parse(route_content, filename=ROUTE_FILE)
+    selected_nodes = []
+
+    for node in parsed.body:
+        if isinstance(node, ast.Assign):
+            target_names = {
+                target.id for target in node.targets if isinstance(target, ast.Name)
+            }
+            if target_names & TARGET_ASSIGNMENTS:
+                selected_nodes.append(node)
+        elif isinstance(node, ast.FunctionDef) and node.name in TARGET_FUNCTIONS:
+            selected_nodes.append(node)
+
+    module = ast.Module(body=selected_nodes, type_ignores=[])
+    namespace = {}
+    exec(compile(module, ROUTE_FILE, 'exec'), namespace)
+    return namespace, route_content
+
+
+def test_discovery_only_calls_trigger_retry_guardrails():
+    """Verify discovery-only tool use is explicitly rejected in the route logic."""
+    print("🔍 Testing discovery-only retry guardrails...")
+
+    try:
+        _, route_content = load_tabular_route_helpers()
+
+        checks = {
+            'prompt disables discovery tools in analysis pass': 'Discovery functions are not available in this analysis run because schema context is already pre-loaded.' in route_content,
+            'analysis run filters callable functions': 'included_functions' in route_content,
+            'retry path requires analytical tools': 'FunctionChoiceBehavior.Required(' in route_content,
+            'retry logging mentions discovery tools': 'used only discovery tool(s)' in route_content,
+            'success logging counts successful analytical tools': 'Analysis complete via {len(successful_analytical_invocations)} analytical tool call(s)' in route_content,
+        }
+
+        failed_checks = [name for name, passed in checks.items() if not passed]
+        assert not failed_checks, f"Missing expected discovery-only guardrails: {failed_checks}"
+
+        print("✅ Discovery-only retry guardrails passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_citation_filter_prefers_analytical_calls():
+    """Verify discovery citations are removed when analytical calls also exist."""
+    print("🔍 Testing analytical citation filtering...")
+
+    try:
+        helpers, _ = load_tabular_route_helpers()
+        filter_invocations = helpers['filter_tabular_citation_invocations']
+        split_invocations = helpers['split_tabular_plugin_invocations']
+
+        invocations = [
+            SimpleNamespace(function_name='describe_tabular_file'),
+            SimpleNamespace(function_name='lookup_value'),
+            SimpleNamespace(function_name='group_by_datetime_component'),
+            SimpleNamespace(function_name='query_tabular_data'),
+        ]
+
+        discovery_invocations, analytical_invocations, other_invocations = split_invocations(invocations)
+        filtered_invocations = filter_invocations(invocations)
+        filtered_function_names = [invocation.function_name for invocation in filtered_invocations]
+
+        assert len(discovery_invocations) == 1, 'Expected one discovery invocation.'
+        assert len(analytical_invocations) == 3, 'Expected three analytical invocations.'
+        assert len(other_invocations) == 0, 'Did not expect other invocation types.'
+        assert filtered_function_names == ['lookup_value', 'group_by_datetime_component', 'query_tabular_data'], (
+            f"Expected only analytical citations, got: {filtered_function_names}"
+        )
+
+        print("✅ Analytical citation filtering passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_new_invocation_slicing_supports_attempt_retries():
+    """Verify retry evaluation can isolate only the invocations from the latest attempt."""
+    print("🔍 Testing new invocation slicing...")
+
+    try:
+        helpers, _ = load_tabular_route_helpers()
+        get_new_invocations = helpers['get_new_plugin_invocations']
+
+        invocations = [
+            SimpleNamespace(function_name='describe_tabular_file'),
+            SimpleNamespace(function_name='group_by_datetime_component'),
+            SimpleNamespace(function_name='aggregate_column'),
+        ]
+
+        sliced_invocations = get_new_invocations(invocations, baseline_count=1)
+        sliced_function_names = [invocation.function_name for invocation in sliced_invocations]
+
+        assert sliced_function_names == ['group_by_datetime_component', 'aggregate_column'], (
+            f"Unexpected invocation slice: {sliced_function_names}"
+        )
+
+        print("✅ New invocation slicing passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == '__main__':
+    tests = [
+        test_discovery_only_calls_trigger_retry_guardrails,
+        test_citation_filter_prefers_analytical_calls,
+        test_new_invocation_slicing_supports_attempt_retries,
+    ]
+
+    results = []
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+
+    success = all(results)
+    print(f"\n📊 Results: {sum(results)}/{len(results)} tests passed")
+    sys.exit(0 if success else 1)
diff --git a/functional_tests/test_tabular_multisheet_workbook_support.py b/functional_tests/test_tabular_multisheet_workbook_support.py
new file mode 100644
index 00000000..35a109cb
--- /dev/null
+++ b/functional_tests/test_tabular_multisheet_workbook_support.py
@@ -0,0 +1,263 @@
+#!/usr/bin/env python3
+# test_tabular_multisheet_workbook_support.py
+"""
+Functional test for multi-sheet workbook analytical orchestration fix.
+Version: 0.239.111
+Implemented in: 0.239.111
+
+This test ensures that multi-sheet workbook analysis can select the likely
+worksheet, use lookup_value for row-and-column retrieval, and constrain the
+tabular SK pass to analytical tools.
+"""
+
+import ast
+import asyncio
+import importlib.util
+import json
+import os
+import re
+import sys
+
+import pandas as pd
+
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(ROOT_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'application', 'single_app'))
+
+ROUTE_FILE = os.path.join(ROOT_DIR, 'application', 'single_app', 'route_backend_chats.py')
+PLUGIN_FILE = os.path.join(
+    ROOT_DIR,
+    'application',
+    'single_app',
+    'semantic_kernel_plugins',
+    'tabular_processing_plugin.py',
+)
+
+TARGET_ASSIGNMENTS = {
+    'TABULAR_ANALYSIS_FUNCTION_NAMES',
+}
+TARGET_FUNCTIONS = {
+    '_normalize_tabular_sheet_token',
+    '_tokenize_tabular_sheet_text',
+    '_select_likely_workbook_sheet',
+}
+
+
+def load_tabular_route_helpers():
+    """Load selected constants and helpers from the chat route source."""
+    with open(ROUTE_FILE, 'r', encoding='utf-8') as file_handle:
+        route_content = file_handle.read()
+
+    parsed = ast.parse(route_content, filename=ROUTE_FILE)
+    selected_nodes = []
+
+    for node in parsed.body:
+        if isinstance(node, ast.Assign):
+            target_names = {
+                target.id for target in node.targets if isinstance(target, ast.Name)
+            }
+            if target_names & TARGET_ASSIGNMENTS:
+                selected_nodes.append(node)
+        elif isinstance(node, ast.FunctionDef) and node.name in TARGET_FUNCTIONS:
+            selected_nodes.append(node)
+
+    module = ast.Module(body=selected_nodes, type_ignores=[])
+    namespace = {'re': re}
+    exec(compile(module, ROUTE_FILE, 'exec'), namespace)
+    return namespace, route_content
+
+
+PLUGIN_SPEC = importlib.util.spec_from_file_location('tabular_processing_plugin', PLUGIN_FILE)
+PLUGIN_MODULE = importlib.util.module_from_spec(PLUGIN_SPEC)
+PLUGIN_SPEC.loader.exec_module(PLUGIN_MODULE)
+TabularProcessingPlugin = PLUGIN_MODULE.TabularProcessingPlugin
+
+
+def build_mock_workbook_plugin():
+    """Create a plugin backed by in-memory workbook frames."""
+    plugin = TabularProcessingPlugin()
+    container_name = 'mock-container'
+    blob_name = 'Family Finances.xlsx'
+    workbook_frames = {
+        'Balance': pd.DataFrame({
+            'Accounts': ['Cash', 'Total Monthly Expenses'],
+            'Nov-25': ['2500.00', '7400.12'],
+        }),
+        'Assets': pd.DataFrame({
+            'Accounts': ['Checking', 'Total Assets'],
+            'Nov-25': ['1500.00', '481225.18'],
+        }),
+    }
+    workbook_metadata = {
+        'is_workbook': True,
+        'sheet_names': ['Balance', 'Assets'],
+        'sheet_count': 2,
+        'default_sheet': 'Balance',
+    }
+
+    plugin._resolve_blob_location_with_fallback = lambda *args, **kwargs: (container_name, blob_name)
+    plugin._get_workbook_metadata = lambda *args, **kwargs: workbook_metadata.copy()
+
+    def read_dataframe(container, blob, sheet_name=None, sheet_index=None, require_explicit_sheet=False):
+        selected_sheet, _ = plugin._resolve_sheet_selection(
+            container,
+            blob,
+            sheet_name=sheet_name,
+            sheet_index=sheet_index,
+            require_explicit_sheet=require_explicit_sheet,
+        )
+        return workbook_frames[selected_sheet].copy()
+
+    plugin._read_tabular_blob_to_dataframe = read_dataframe
+    return plugin, container_name, blob_name
+
+
+def test_lookup_value_returns_target_value_from_assets_sheet():
+    """Verify lookup_value returns the requested period value from the selected sheet."""
+    print('Testing lookup_value on explicit worksheet...')
+
+    try:
+        plugin, _, _ = build_mock_workbook_plugin()
+        result_json = asyncio.run(plugin.lookup_value(
+            user_id='test-user',
+            conversation_id='test-conversation',
+            filename='Family Finances.xlsx',
+            lookup_column='Accounts',
+            lookup_value='Total Assets',
+            target_column='Nov-25',
+            sheet_name='Assets',
+            source='workspace',
+        ))
+        payload = json.loads(result_json)
+
+        assert 'error' not in payload, f"Unexpected error payload: {payload}"
+        assert payload['selected_sheet'] == 'Assets', payload
+        assert payload['value'] == 481225.18, payload
+        assert payload['total_matches'] == 1, payload
+
+        print('PASS lookup_value explicit worksheet')
+        return True
+
+    except Exception as exc:
+        print(f'FAIL test: {exc}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_default_sheet_override_allows_lookup_without_sheet_argument():
+    """Verify a likely-sheet override satisfies analytical calls that omit sheet_name."""
+    print('Testing lookup_value default-sheet override...')
+
+    try:
+        plugin, container_name, blob_name = build_mock_workbook_plugin()
+
+        initial_payload = json.loads(asyncio.run(plugin.lookup_value(
+            user_id='test-user',
+            conversation_id='test-conversation',
+            filename='Family Finances.xlsx',
+            lookup_column='Accounts',
+            lookup_value='Total Assets',
+            target_column='Nov-25',
+            source='workspace',
+        )))
+        assert 'Specify sheet_name or sheet_index on analytical calls.' in initial_payload.get('error', ''), initial_payload
+
+        plugin.set_default_sheet(container_name, blob_name, 'Assets')
+        override_payload = json.loads(asyncio.run(plugin.lookup_value(
+            user_id='test-user',
+            conversation_id='test-conversation',
+            filename='Family Finances.xlsx',
+            lookup_column='Accounts',
+            lookup_value='Total Assets',
+            target_column='Nov-25',
+            source='workspace',
+        )))
+
+        assert 'error' not in override_payload, override_payload
+        assert override_payload['selected_sheet'] == 'Assets', override_payload
+        assert override_payload['value'] == 481225.18, override_payload
+
+        print('PASS lookup_value default-sheet override')
+        return True
+
+    except Exception as exc:
+        print(f'FAIL test: {exc}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_likely_sheet_helper_handles_pluralized_question_text():
+    """Verify likely-sheet selection matches singular question terms to plural sheet names."""
+    print('Testing likely-sheet helper...')
+
+    try:
+        helpers, _ = load_tabular_route_helpers()
+        select_likely_sheet = helpers['_select_likely_workbook_sheet']
+        analytical_functions = helpers['TABULAR_ANALYSIS_FUNCTION_NAMES']
+
+        likely_sheet = select_likely_sheet(
+            ['Balance', 'Budget', 'Assets', 'Liabilities'],
+            'what were our asset values for nov 2025?',
+        )
+
+        assert 'lookup_value' in analytical_functions, analytical_functions
+        assert likely_sheet == 'Assets', likely_sheet
+
+        print('PASS likely-sheet helper')
+        return True
+
+    except Exception as exc:
+        print(f'FAIL test: {exc}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_route_uses_analytical_filters_and_lookup_guidance():
+    """Verify the route prompt and execution settings match the analytical-only design."""
+    print('Testing route analytical-only orchestration text...')
+
+    try:
+        _, route_content = load_tabular_route_helpers()
+
+        checks = {
+            'lookup_value is advertised first': 'AVAILABLE FUNCTIONS: lookup_value' in route_content,
+            'discovery tools are disabled for analysis': 'Discovery functions are not available in this analysis run because schema context is already pre-loaded.' in route_content,
+            'prompt includes likely worksheet hints': 'LIKELY WORKSHEET HINTS:' in route_content,
+            'analysis function filters are configured': 'included_functions' in route_content,
+            'retry attempts require analytical function use': 'FunctionChoiceBehavior.Required(' in route_content,
+            'likely sheet override is applied': 'tabular_plugin.set_default_sheet(container, blob_path, likely_sheet)' in route_content,
+        }
+
+        failed_checks = [name for name, passed in checks.items() if not passed]
+        assert not failed_checks, f"Missing expected orchestration behavior: {failed_checks}"
+
+        print('PASS route analytical-only orchestration text')
+        return True
+
+    except Exception as exc:
+        print(f'FAIL test: {exc}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == '__main__':
+    tests = [
+        test_lookup_value_returns_target_value_from_assets_sheet,
+        test_default_sheet_override_allows_lookup_without_sheet_argument,
+        test_likely_sheet_helper_handles_pluralized_question_text,
+        test_route_uses_analytical_filters_and_lookup_guidance,
+    ]
+
+    results = []
+    for test in tests:
+        print(f"\nRunning {test.__name__}...")
+        results.append(test())
+
+    success = all(results)
+    print(f"\nResults: {sum(results)}/{len(results)} tests passed")
+    sys.exit(0 if success else 1)
\ No newline at end of file
diff --git a/functional_tests/test_tabular_tool_error_retry_and_thoughts.py b/functional_tests/test_tabular_tool_error_retry_and_thoughts.py
new file mode 100644
index 00000000..2a111b1a
--- /dev/null
+++ b/functional_tests/test_tabular_tool_error_retry_and_thoughts.py
@@ -0,0 +1,186 @@
+#!/usr/bin/env python3
+# test_tabular_tool_error_retry_and_thoughts.py
+"""
+Functional test for tabular tool-error retry and thought visibility fix.
+Version: 0.239.037
+Implemented in: 0.239.037
+
+This test ensures that analytical tabular tool calls returning JSON error
+payloads are treated as failed attempts, are retried instead of being accepted
+as completed analysis, and surface clearer thought details for recovery.
+"""
+
+import ast
+import json
+import os
+import sys
+from types import SimpleNamespace
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(ROOT_DIR)
+
+ROUTE_FILE = os.path.join(ROOT_DIR, 'application', 'single_app', 'route_backend_chats.py')
+TARGET_ASSIGNMENTS = {
+    'TABULAR_DISCOVERY_FUNCTION_NAMES',
+    'TABULAR_ANALYSIS_FUNCTION_NAMES',
+    'TABULAR_THOUGHT_EXCLUDED_PARAMETER_NAMES',
+}
+TARGET_FUNCTIONS = {
+    'get_tabular_invocation_result_payload',
+    'get_tabular_invocation_error_message',
+    'split_tabular_analysis_invocations',
+    'summarize_tabular_invocation_errors',
+    'filter_tabular_citation_invocations',
+    'format_tabular_thought_parameter_value',
+    'get_tabular_tool_thought_payloads',
+    'get_tabular_status_thought_payloads',
+}
+
+
+def load_tabular_route_helpers():
+    """Load selected helper functions from the chat route source."""
+    with open(ROUTE_FILE, 'r', encoding='utf-8') as file_handle:
+        route_content = file_handle.read()
+
+    parsed = ast.parse(route_content, filename=ROUTE_FILE)
+    selected_nodes = []
+
+    for node in parsed.body:
+        if isinstance(node, ast.Assign):
+            target_names = {
+                target.id for target in node.targets if isinstance(target, ast.Name)
+            }
+            if target_names & TARGET_ASSIGNMENTS:
+                selected_nodes.append(node)
+        elif isinstance(node, ast.FunctionDef) and node.name in TARGET_FUNCTIONS:
+            selected_nodes.append(node)
+
+    module = ast.Module(body=selected_nodes, type_ignores=[])
+    namespace = {'json': json}
+    exec(compile(module, ROUTE_FILE, 'exec'), namespace)
+    return namespace, route_content
+
+
+def test_tool_error_payload_is_classified_as_failed_analysis():
+    """Verify JSON error payloads are treated as failed analytical tool calls."""
+    print("🔍 Testing tabular tool error classification...")
+
+    try:
+        helpers, _ = load_tabular_route_helpers()
+        get_error_message = helpers['get_tabular_invocation_error_message']
+        split_analysis_invocations = helpers['split_tabular_analysis_invocations']
+        filter_citations = helpers['filter_tabular_citation_invocations']
+        failed_result_payload = json.dumps({
+            'error': "aggregate_column is required unless operation='count'."
+        })
+
+        failed_invocation = SimpleNamespace(
+            function_name='group_by_datetime_component',
+            result=failed_result_payload,
+            error_message=None,
+        )
+
+        assert get_error_message(failed_invocation) == "aggregate_column is required unless operation='count'."
+
+        successful_invocations, failed_invocations = split_analysis_invocations([failed_invocation])
+        assert len(successful_invocations) == 0, 'Did not expect a successful analytical invocation.'
+        assert len(failed_invocations) == 1, 'Expected the analytical invocation to be classified as failed.'
+        assert filter_citations([failed_invocation]) == [], 'Failed analytical invocations should not become citations.'
+
+        print("✅ Tabular tool error classification passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_failed_tool_thoughts_and_recovery_status_are_rendered():
+    """Verify failed analytical calls produce failed tool thoughts and recovery status thoughts."""
+    print("🔍 Testing tabular failure thought payloads...")
+
+    try:
+        helpers, _ = load_tabular_route_helpers()
+        get_tool_payloads = helpers['get_tabular_tool_thought_payloads']
+        get_status_payloads = helpers['get_tabular_status_thought_payloads']
+        failed_result_payload = json.dumps({
+            'error': "aggregate_column is required unless operation='count'."
+        })
+
+        failed_invocation = SimpleNamespace(
+            function_name='group_by_datetime_component',
+            duration_ms=168.5,
+            success=True,
+            parameters={
+                'filename': 'faa_flight_operations_dataset.csv',
+                'datetime_component': 'hour',
+                'source': 'workspace',
+            },
+            result=failed_result_payload,
+            error_message=None,
+        )
+
+        tool_payloads = get_tool_payloads([failed_invocation])
+        status_payloads = get_status_payloads([failed_invocation], analysis_succeeded=True)
+
+        assert tool_payloads[0][0] == 'Tabular tool group_by_datetime_component on faa_flight_operations_dataset.csv (168ms) failed'
+        assert "error=aggregate_column is required unless operation='count'." in tool_payloads[0][1]
+        assert 'success=False' in tool_payloads[0][1]
+        assert status_payloads == [(
+            'Tabular analysis recovered via internal fallback after tool errors',
+            "aggregate_column is required unless operation='count'."
+        )], status_payloads
+
+        print("✅ Tabular failure thought payloads passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_route_contains_retry_feedback_for_tool_errors():
+    """Verify the route feeds previous tool errors back into retry attempts."""
+    print("🔍 Testing route retry feedback for tool errors...")
+
+    try:
+        _, route_content = load_tabular_route_helpers()
+
+        checks = {
+            'tool error prompt feedback': 'PREVIOUS TOOL ERRORS:' in route_content,
+            'tool error retry logging': 'used analytical tool(s) but all returned errors; retrying' in route_content,
+            'fallback recovery thought': 'Tabular analysis recovered via internal fallback after tool errors' in route_content,
+        }
+
+        failed_checks = [name for name, passed in checks.items() if not passed]
+        assert not failed_checks, f"Missing expected tool-error retry behavior: {failed_checks}"
+
+        print("✅ Route retry feedback for tool errors passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == '__main__':
+    tests = [
+        test_tool_error_payload_is_classified_as_failed_analysis,
+        test_failed_tool_thoughts_and_recovery_status_are_rendered,
+        test_route_contains_retry_feedback_for_tool_errors,
+    ]
+
+    results = []
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+
+    success = all(results)
+    print(f"\n📊 Results: {sum(results)}/{len(results)} tests passed")
+    sys.exit(0 if success else 1)
diff --git a/functional_tests/test_thoughts_feature.py b/functional_tests/test_thoughts_feature.py
new file mode 100644
index 00000000..c07df2da
--- /dev/null
+++ b/functional_tests/test_thoughts_feature.py
@@ -0,0 +1,520 @@
+#!/usr/bin/env python3
+"""
+Functional test for Processing Thoughts feature.
+Version: 0.239.003
+Implemented in: 0.239.003
+
+This test ensures that the Processing Thoughts feature is properly implemented
+across backend (ThoughtTracker, API endpoints, chat instrumentation) and
+frontend (polling, streaming, toggle, rendering) components.
+"""
+
+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'application', 'single_app'))
+
+
+def test_thoughts_backend_module():
+    """Test that functions_thoughts.py has proper ThoughtTracker and CRUD functions."""
+    print("Testing functions_thoughts.py module structure...")
+    try:
+        backend_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'functions_thoughts.py'
+        )
+
+        with open(backend_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'ThoughtTracker class': 'class ThoughtTracker' in content,
+            'add_thought method': 'def add_thought' in content,
+            'complete_thought method': 'def complete_thought' in content,
+            'enabled property': 'def enabled' in content or 'enabled' in content,
+            'get_thoughts_for_message': 'def get_thoughts_for_message' in content,
+            'get_pending_thoughts': 'def get_pending_thoughts' in content,
+            'archive_thoughts_for_conversation': 'def archive_thoughts_for_conversation' in content,
+            'delete_thoughts_for_conversation': 'def delete_thoughts_for_conversation' in content,
+            'delete_thoughts_for_message': 'def delete_thoughts_for_message' in content,
+            'cosmos_thoughts_container import': 'cosmos_thoughts_container' in content,
+            'step_index tracking': 'step_index' in content,
+            'step_type field': 'step_type' in content,
+            'error handling (try/except)': 'except Exception' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_route_module():
+    """Test that route_backend_thoughts.py has proper API endpoints."""
+    print("\nTesting route_backend_thoughts.py API endpoints...")
+    try:
+        route_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'route_backend_thoughts.py'
+        )
+
+        with open(route_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'register function': 'def register_route_backend_thoughts' in content,
+            'message thoughts endpoint': '/messages/' in content and '/thoughts' in content,
+            'pending thoughts endpoint': '/thoughts/pending' in content,
+            'swagger_route decorator': 'swagger_route' in content,
+            'login_required decorator': 'login_required' in content,
+            'user_required decorator': 'user_required' in content,
+            'get_auth_security import': 'get_auth_security' in content,
+            'enable_thoughts check': 'enable_thoughts' in content,
+            'returns enabled flag': "'enabled'" in content or '"enabled"' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_chat_instrumentation():
+    """Test that route_backend_chats.py has thought instrumentation points."""
+    print("\nTesting route_backend_chats.py thought instrumentation...")
+    try:
+        chats_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'route_backend_chats.py'
+        )
+
+        with open(chats_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'ThoughtTracker import': 'ThoughtTracker' in content,
+            'thought_tracker instantiation': 'thought_tracker' in content and 'ThoughtTracker(' in content,
+            'add_thought calls': 'thought_tracker.add_thought' in content,
+            'content_safety thought': "'content_safety'" in content or '"content_safety"' in content,
+            'search thought': "add_thought('search'" in content or 'add_thought("search"' in content,
+            'web_search thought': "'web_search'" in content or '"web_search"' in content,
+            'generation thought': "'generation'" in content or '"generation"' in content,
+            'thoughts_enabled in response': 'thoughts_enabled' in content,
+            'SSE thought event (streaming)': '"type": "thought"' in content or "'type': 'thought'" in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_frontend_module():
+    """Test that chat-thoughts.js has polling, streaming, and toggle logic."""
+    print("\nTesting chat-thoughts.js frontend module...")
+    try:
+        js_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'static', 'js', 'chat', 'chat-thoughts.js'
+        )
+
+        with open(js_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'startThoughtPolling export': 'export function startThoughtPolling' in content,
+            'stopThoughtPolling export': 'export function stopThoughtPolling' in content,
+            'handleStreamingThought export': 'export function handleStreamingThought' in content,
+            'createThoughtsToggleHtml export': 'export function createThoughtsToggleHtml' in content,
+            'attachThoughtsToggleListener export': 'export function attachThoughtsToggleListener' in content,
+            'polling interval setup': 'setInterval' in content,
+            'polling endpoint fetch': '/thoughts/pending' in content,
+            'message thoughts fetch': '/thoughts' in content,
+            'icon map (search)': 'bi-search' in content,
+            'icon map (globe)': 'bi-globe' in content,
+            'icon map (robot)': 'bi-robot' in content,
+            'icon map (lightning)': 'bi-lightning' in content,
+            'icon map (shield)': 'bi-shield-check' in content,
+            'enable_thoughts check': 'enable_thoughts' in content,
+            'd-none toggle': 'd-none' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_chat_messages_integration():
+    """Test that chat-messages.js integrates thought polling and toggle."""
+    print("\nTesting chat-messages.js thoughts integration...")
+    try:
+        messages_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'static', 'js', 'chat', 'chat-messages.js'
+        )
+
+        with open(messages_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'chat-thoughts import': 'chat-thoughts' in content,
+            'startThoughtPolling import': 'startThoughtPolling' in content,
+            'stopThoughtPolling import': 'stopThoughtPolling' in content,
+            'createThoughtsToggleHtml import': 'createThoughtsToggleHtml' in content,
+            'attachThoughtsToggleListener import': 'attachThoughtsToggleListener' in content,
+            'startThoughtPolling call': 'startThoughtPolling(' in content,
+            'stopThoughtPolling call': 'stopThoughtPolling(' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_streaming_integration():
+    """Test that chat-streaming.js handles thought SSE events."""
+    print("\nTesting chat-streaming.js thought event handling...")
+    try:
+        streaming_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'static', 'js', 'chat', 'chat-streaming.js'
+        )
+
+        with open(streaming_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'handleStreamingThought import': 'handleStreamingThought' in content,
+            'thought type check': "'thought'" in content or '"thought"' in content,
+            'handleStreamingThought call': 'handleStreamingThought(' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_loading_indicator():
+    """Test that chat-loading-indicator.js supports thought text updates."""
+    print("\nTesting chat-loading-indicator.js thought text support...")
+    try:
+        indicator_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'static', 'js', 'chat', 'chat-loading-indicator.js'
+        )
+
+        with open(indicator_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'updateLoadingIndicatorText export': 'export function updateLoadingIndicatorText' in content
+                                                  or 'updateLoadingIndicatorText' in content,
+            'text element update': 'thought-live-text' in content or 'loading-text' in content
+                                   or 'textContent' in content or 'innerHTML' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_admin_settings():
+    """Test that admin settings UI includes the thoughts toggle."""
+    print("\nTesting admin settings UI for thoughts toggle...")
+    try:
+        html_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'templates', 'admin_settings.html'
+        )
+
+        with open(html_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'enable_thoughts checkbox': 'id="enable_thoughts"' in content,
+            'enable_thoughts name attr': 'name="enable_thoughts"' in content,
+            'Processing Thoughts label': 'Processing Thoughts' in content,
+            'lightbulb icon': 'bi-lightbulb' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_settings_default():
+    """Test that functions_settings.py includes enable_thoughts default."""
+    print("\nTesting functions_settings.py default setting...")
+    try:
+        settings_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'functions_settings.py'
+        )
+
+        with open(settings_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'enable_thoughts in defaults': 'enable_thoughts' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_cosmos_containers():
+    """Test that config.py defines thoughts Cosmos DB containers."""
+    print("\nTesting config.py Cosmos DB container definitions...")
+    try:
+        config_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'config.py'
+        )
+
+        with open(config_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'thoughts container name': 'cosmos_thoughts_container_name' in content,
+            'thoughts container object': 'cosmos_thoughts_container' in content,
+            'archive_thoughts container name': 'cosmos_archived_thoughts_container_name' in content,
+            'archive_thoughts container object': 'cosmos_archived_thoughts_container' in content,
+            'partition key /user_id': '/user_id' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_conversation_archive():
+    """Test that route_backend_conversations.py handles thought archive/delete."""
+    print("\nTesting route_backend_conversations.py thought archive support...")
+    try:
+        conv_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'route_backend_conversations.py'
+        )
+
+        with open(conv_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'archive_thoughts_for_conversation import': 'archive_thoughts_for_conversation' in content,
+            'delete_thoughts_for_conversation import': 'delete_thoughts_for_conversation' in content,
+            'archive_thoughts call': 'archive_thoughts_for_conversation(' in content,
+            'delete_thoughts call': 'delete_thoughts_for_conversation(' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_css_styles():
+    """Test that chats.css includes thought-related styles."""
+    print("\nTesting chats.css thought styles...")
+    try:
+        css_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'static', 'css', 'chats.css'
+        )
+
+        with open(css_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'thoughts-toggle-btn style': '.thoughts-toggle-btn' in content,
+            'thoughts-container style': '.thoughts-container' in content,
+            'thought-step style': '.thought-step' in content,
+            'animate-pulse animation': '.animate-pulse' in content,
+            'thought-pulse keyframes': '@keyframes thought-pulse' in content,
+            'dark mode thoughts toggle': '[data-bs-theme="dark"] .thoughts-toggle-btn' in content,
+            'dark mode thought step': '[data-bs-theme="dark"] .thought-step' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+def test_thoughts_app_registration():
+    """Test that app.py registers the thoughts route blueprint."""
+    print("\nTesting app.py route registration...")
+    try:
+        app_file = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)),
+            'application', 'single_app', 'app.py'
+        )
+
+        with open(app_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        checks = {
+            'import register function': 'register_route_backend_thoughts' in content,
+            'register call': 'register_route_backend_thoughts(app)' in content,
+        }
+
+        all_passed = True
+        for name, passed in checks.items():
+            status = 'PASS' if passed else 'FAIL'
+            print(f"  [{status}] {name}")
+            if not passed:
+                all_passed = False
+
+        return all_passed
+
+    except Exception as e:
+        print(f"  [FAIL] Exception: {e}")
+        return False
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("Processing Thoughts Feature - Functional Tests")
+    print("Version: 0.239.003")
+    print("=" * 60)
+
+    tests = [
+        test_thoughts_backend_module,
+        test_thoughts_route_module,
+        test_thoughts_chat_instrumentation,
+        test_thoughts_frontend_module,
+        test_thoughts_chat_messages_integration,
+        test_thoughts_streaming_integration,
+        test_thoughts_loading_indicator,
+        test_thoughts_admin_settings,
+        test_thoughts_settings_default,
+        test_thoughts_cosmos_containers,
+        test_thoughts_conversation_archive,
+        test_thoughts_css_styles,
+        test_thoughts_app_registration,
+    ]
+
+    results = []
+    for test_fn in tests:
+        try:
+            result = test_fn()
+            results.append(result)
+        except Exception as e:
+            print(f"  [FAIL] Unhandled exception: {e}")
+            results.append(False)
+
+    print("\n" + "=" * 60)
+    passed = sum(1 for r in results if r)
+    total = len(results)
+    print(f"Results: {passed}/{total} test groups passed")
+
+    if passed == total:
+        print("All tests passed!")
+    else:
+        print("Some tests failed. Review output above.")
+
+    print("=" * 60)
+    sys.exit(0 if passed == total else 1)
diff --git a/functional_tests/test_workspace_tabular_trigger_and_thoughts.py b/functional_tests/test_workspace_tabular_trigger_and_thoughts.py
new file mode 100644
index 00000000..e0aeb333
--- /dev/null
+++ b/functional_tests/test_workspace_tabular_trigger_and_thoughts.py
@@ -0,0 +1,204 @@
+#!/usr/bin/env python3
+# test_workspace_tabular_trigger_and_thoughts.py
+"""
+Functional test for workspace-selected tabular trigger and per-tool thoughts fix.
+Version: 0.239.035
+Implemented in: 0.239.035
+
+This test ensures that explicitly selected workspace tabular files still trigger
+SK mini-agent analysis even when retrieval context is sparse, and that
+processing thoughts show individual tabular tool calls instead of only generic
+wrapper messages.
+"""
+
+import ast
+import os
+import sys
+from types import SimpleNamespace
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+ROUTE_FILE = os.path.join(ROOT_DIR, 'application', 'single_app', 'route_backend_chats.py')
+
+
+def read_route_backend_chats():
+    """Read the chat route implementation for structural verification."""
+    with open(ROUTE_FILE, 'r', encoding='utf-8') as file_handle:
+        return file_handle.read()
+
+
+def load_tabular_thought_helpers():
+    """Load selected tabular thought helpers from the route source."""
+    parsed = ast.parse(read_route_backend_chats(), filename=ROUTE_FILE)
+    selected_nodes = []
+
+    for node in parsed.body:
+        if isinstance(node, ast.Assign):
+            target_names = {
+                target.id for target in node.targets if isinstance(target, ast.Name)
+            }
+            if 'TABULAR_THOUGHT_EXCLUDED_PARAMETER_NAMES' in target_names:
+                selected_nodes.append(node)
+        elif isinstance(node, ast.FunctionDef) and node.name in {
+            'get_tabular_invocation_result_payload',
+            'get_tabular_invocation_error_message',
+            'format_tabular_thought_parameter_value',
+            'get_tabular_tool_thought_payloads',
+        }:
+            selected_nodes.append(node)
+
+    module = ast.Module(body=selected_nodes, type_ignores=[])
+    namespace = {'json': __import__('json')}
+    exec(compile(module, ROUTE_FILE, 'exec'), namespace)
+    return namespace
+
+
+def test_workspace_selected_tabular_trigger():
+    """Verify explicitly selected workspace tabular files participate in trigger detection."""
+    print("🔍 Testing workspace-selected tabular trigger detection...")
+
+    try:
+        content = read_route_backend_chats()
+
+        checks = {
+            'selected workspace helper exists': 'def get_selected_workspace_tabular_filenames(' in content,
+            'combined workspace helper exists': 'def collect_workspace_tabular_filenames(' in content,
+            'workspace trigger uses selected ids': 'selected_document_ids=selected_document_ids' in content,
+            'workspace trigger uses selected id': 'selected_document_id=selected_document_id' in content,
+            'workspace-specific fallback prompt': 'IMPORTANT: The selected workspace tabular file(s) are' in content,
+            'workspace trigger gated by document search': 'if hybrid_search_enabled and workspace_tabular_files' in content,
+        }
+
+        failed_checks = [name for name, passed in checks.items() if not passed]
+        assert not failed_checks, f"Missing expected workspace trigger elements: {failed_checks}"
+
+        print("✅ Workspace-selected tabular trigger checks passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_tabular_analysis_thoughts_are_recorded():
+    """Verify processing thoughts now expose individual tabular tool calls."""
+    print("🔍 Testing tabular analysis thoughts instrumentation...")
+
+    try:
+        content = read_route_backend_chats()
+
+        checks = {
+            'tool thought payload helper exists': 'def get_tabular_tool_thought_payloads(' in content,
+            'non-streaming tool thought loop': 'for thought_content, thought_detail in tabular_thought_payloads:' in content,
+            'streaming tool thought loop': "yield emit_thought('tabular_analysis', thought_content, thought_detail)" in content,
+            'generic workspace wrapper thought removed': 'Running tabular analysis on {len(workspace_tabular_files)} workspace file(s)' not in content,
+            'generic completion wrapper thought removed': 'Tabular analysis completed using {len(tabular_sk_citations)} tool call(s)' not in content,
+            'failure thought remains': 'Tabular analysis could not compute results; using schema context instead' in content,
+        }
+
+        failed_checks = [name for name, passed in checks.items() if not passed]
+        assert not failed_checks, f"Missing expected tabular thought instrumentation: {failed_checks}"
+
+        print("✅ Tabular analysis thoughts checks passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_tabular_tool_thought_payload_formatting():
+    """Verify individual tabular tool calls produce readable thought payloads."""
+    print("🔍 Testing tabular tool thought payload formatting...")
+
+    try:
+        helpers = load_tabular_thought_helpers()
+        payload_builder = helpers['get_tabular_tool_thought_payloads']
+
+        invocations = [
+            SimpleNamespace(
+                function_name='group_by_datetime_component',
+                duration_ms=42.8,
+                success=True,
+                parameters={
+                    'user_id': 'test-user',
+                    'conversation_id': 'test-conversation',
+                    'filename': 'faa.csv',
+                    'datetime_component': 'hour',
+                    'operation': 'mean',
+                },
+                error_message=None,
+            )
+        ]
+
+        thought_payloads = payload_builder(invocations)
+        assert len(thought_payloads) == 1, f"Expected one thought payload, got {len(thought_payloads)}"
+
+        thought_content, thought_detail = thought_payloads[0]
+        assert thought_content == 'Tabular tool group_by_datetime_component on faa.csv (42ms)', thought_content
+        assert 'datetime_component=hour' in thought_detail, thought_detail
+        assert 'operation=mean' in thought_detail, thought_detail
+        assert 'user_id=' not in thought_detail, thought_detail
+        assert 'conversation_id=' not in thought_detail, thought_detail
+
+        print("✅ Tabular tool thought payload formatting passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_tabular_sk_prompt_requires_tool_use():
+    """Verify the mini-agent retries when it answers without using tabular tools."""
+    print("🔍 Testing mandatory tabular tool-use prompt hardening...")
+
+    try:
+        content = read_route_backend_chats()
+
+        checks = {
+            'mandatory tool-use prompt': (
+                'You MUST use one or more ' in content
+                and 'tabular_processing plugin functions before answering.' in content
+            ),
+            'retry mode prompt': 'RETRY MODE: Your previous attempt did not execute the data-analysis tools.' in content,
+            'retry logging': 'returned narrative without tool use; retrying' in content,
+            'strict retry attempts': 'maximum_auto_invoke_attempts=10 if force_tool_use else 7' in content,
+        }
+
+        failed_checks = [name for name, passed in checks.items() if not passed]
+        assert not failed_checks, f"Missing expected tabular SK prompt hardening: {failed_checks}"
+
+        print("✅ Tabular SK prompt hardening checks passed")
+        return True
+
+    except Exception as exc:
+        print(f"❌ Test failed: {exc}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+if __name__ == '__main__':
+    tests = [
+        test_workspace_selected_tabular_trigger,
+        test_tabular_analysis_thoughts_are_recorded,
+        test_tabular_tool_thought_payload_formatting,
+        test_tabular_sk_prompt_requires_tool_use,
+    ]
+
+    results = []
+    for test in tests:
+        print(f"\n🧪 Running {test.__name__}...")
+        results.append(test())
+
+    success = all(results)
+    print(f"\n📊 Results: {sum(results)}/{len(results)} tests passed")
+    sys.exit(0 if success else 1)
diff --git a/test_output.txt b/test_output.txt
new file mode 100644
index 00000000..5b13bd8b
--- /dev/null
+++ b/test_output.txt
@@ -0,0 +1,109 @@
+EXIT: 0
+STDOUT:
+[Log] [Plugin Function Logger] Decorating function for plugin -- {'function_name': 'list_tabular_files', 'plugin_name': 'TabularProcessingPlugin'}

+[Log] [Plugin Function Logger] Decorating function for plugin -- {'function_name': 'describe_tabular_file', 'plugin_name': 'TabularProcessingPlugin'}

+[Log] [Plugin Function Logger] Decorating function for plugin -- {'function_name': 'aggregate_column', 'plugin_name': 'TabularProcessingPlugin'}

+[Log] [Plugin Function Logger] Decorating function for plugin -- {'function_name': 'filter_rows', 'plugin_name': 'TabularProcessingPlugin'}

+[Log] [Plugin Function Logger] Decorating function for plugin -- {'function_name': 'query_tabular_data', 'plugin_name': 'TabularProcessingPlugin'}

+[Log] [Plugin Function Logger] Decorating function for plugin -- {'function_name': 'group_by_aggregate', 'plugin_name': 'TabularProcessingPlugin'}

+[Log] [Plugin Function Logger] Decorating function for plugin -- {'function_name': 'group_by_datetime_component', 'plugin_name': 'TabularProcessingPlugin'}

+

+🧪 Running test_describe_tabular_file_returns_workbook_schema_summary...

+🔍 Testing workbook-aware describe_tabular_file output...

+[Log] [Plugin Function Logger] Function call started -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file'}

+[Log] [Plugin Function Logger] Function parameters -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'source': 'workspace'}, 'param_string': 'user_id=test-user, conversation_id=test-conversation, filename=Family Finances.xlsx, source=workspace'}

+[Log] [Plugin Function Logger] Function completed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "is_workbook": true,\n  "sheet_names": [\n    "Balance",\n    "Assets"\n  ],\n  "sheet_count": 2,\n  "selected_sheet": null,\n  "per_sheet_schemas": {\n    "Balance":...', 'duration_ms': 7.000207901000977, 'full_function_name': 'TabularProcessingPlugin.describe_tabular_file'}

+Warning: Failed to get settings from cache, read from Cosmos DB instead. Called from e:\repos\simplechat\application\single_app\functions_debug.py:25 in debug_print().

+[DEBUG] [INFO]: [Plugin SUCCESS] TabularProcessingPlugin.describe_tabular_file (7.0ms)

+[Log] Plugin function executed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'duration_ms': 7.000207901000977, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.318234', 'parameter_count': 4, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "is_workbook": true,\n  "sheet_names": [\n    "Balance",\n    "Assets"\n  ],\n  "sheet_count": 2,\n  "selected_sheet": null,\n  "per_sheet_schemas": {\n    "Balance":...', 'result_type': 'str'}

+[Log] [Plugin Invocation] TabularProcessingPlugin.describe_tabular_file -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'duration_ms': 7.000207901000977, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.318234', 'parameter_count': 4, 'result_type': 'str', 'error_message': None, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "is_workbook": true,\n  "sheet_names": [\n    "Balance",\n    "Assets"\n  ],\n  "sheet_count": 2,\n  "selected_sheet": null,\n  "per_sheet_schemas": {\n    "Balance": {\n      "selected_sheet": "Balance",\n      "row_count": 2,\n      "column_count": 2,\n      "columns": [\n        "Accounts",\n        "Nov-25"\n      ],\n      "dtypes": {\n        "Accounts": "object",\n        "Nov-25": "float64"\n      },\n      "preview": [\n        {\n          "Accounts": "Total Monthly... [truncated]'}

+✅ Workbook-aware describe output passed

+

+🧪 Running test_default_sheet_override_resolves_automatically...

+🔍 Testing default sheet override...

+[Log] [Plugin Function Logger] Function call started -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows'}

+[Log] [Plugin Function Logger] Function parameters -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'source': 'workspace'}, 'param_string': 'user_id=test-user, conversation_id=test-conversation, filename=Family Finances.xlsx, column=Accounts, operator===, value=Total Assets, source=workspace'}

+[Log] [TabularProcessingPlugin] Error filtering rows: Workbook 'Family Finances.xlsx' has multiple sheets: ['Balance', 'Assets']. Specify sheet_name or sheet_index on analytical calls. -- None

+[Log] [Plugin Function Logger] Function completed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'result_preview': '{"error": "Workbook \'Family Finances.xlsx\' has multiple sheets: [\'Balance\', \'Assets\']. Specify sheet_name or sheet_index on analytical calls."}', 'duration_ms': 0.9989738464355469, 'full_function_name': 'TabularProcessingPlugin.filter_rows'}

+Warning: Failed to get settings from cache, read from Cosmos DB instead. Called from e:\repos\simplechat\application\single_app\functions_debug.py:25 in debug_print().

+[DEBUG] [INFO]: [Plugin SUCCESS] TabularProcessingPlugin.filter_rows (1.0ms)

+[Log] Plugin function executed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 0.9989738464355469, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.353680', 'parameter_count': 7, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'source': 'workspace'}, 'result_preview': '{"error": "Workbook \'Family Finances.xlsx\' has multiple sheets: [\'Balance\', \'Assets\']. Specify sheet_name or sheet_index on analytical calls."}', 'result_type': 'str'}

+[Log] [Plugin Invocation] TabularProcessingPlugin.filter_rows -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 0.9989738464355469, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.353680', 'parameter_count': 7, 'result_type': 'str', 'error_message': None, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'source': 'workspace'}, 'result_preview': '{"error": "Workbook \'Family Finances.xlsx\' has multiple sheets: [\'Balance\', \'Assets\']. Specify sheet_name or sheet_index on analytical calls."}'}

+[Log] [Plugin Function Logger] Function call started -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows'}

+[Log] [Plugin Function Logger] Function parameters -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'source': 'workspace'}, 'param_string': 'user_id=test-user, conversation_id=test-conversation, filename=Family Finances.xlsx, column=Accounts, operator===, value=Total Assets, source=workspace'}

+[Log] [Plugin Function Logger] Function completed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "selected_sheet": "Assets",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Assets",\n      "Nov-25": 481225.18\n    }\n  ...', 'duration_ms': 2.000093460083008, 'full_function_name': 'TabularProcessingPlugin.filter_rows'}

+Warning: Failed to get settings from cache, read from Cosmos DB instead. Called from e:\repos\simplechat\application\single_app\functions_debug.py:25 in debug_print().

+[DEBUG] [INFO]: [Plugin SUCCESS] TabularProcessingPlugin.filter_rows (2.0ms)

+[Log] Plugin function executed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 2.000093460083008, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.385684', 'parameter_count': 7, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "selected_sheet": "Assets",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Assets",\n      "Nov-25": 481225.18\n    }\n  ...', 'result_type': 'str'}

+[Log] [Plugin Invocation] TabularProcessingPlugin.filter_rows -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 2.000093460083008, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.385684', 'parameter_count': 7, 'result_type': 'str', 'error_message': None, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "selected_sheet": "Assets",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Assets",\n      "Nov-25": 481225.18\n    }\n  ]\n}'}

+✅ Default sheet override passed

+

+🧪 Running test_multisheet_analysis_uses_requested_sheet...

+🔍 Testing selected worksheet preservation...

+[Log] [Plugin Function Logger] Function call started -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows'}

+[Log] [Plugin Function Logger] Function parameters -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'sheet_name': 'Assets', 'source': 'workspace'}, 'param_string': 'user_id=test-user, conversation_id=test-conversation, filename=Family Finances.xlsx, column=Accounts, operator===, value=Total Assets, sheet_name=Assets, source=workspace'}

+[Log] [Plugin Function Logger] Function completed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "selected_sheet": "Assets",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Assets",\n      "Nov-25": 481225.18\n    }\n  ...', 'duration_ms': 2.000570297241211, 'full_function_name': 'TabularProcessingPlugin.filter_rows'}

+Warning: Failed to get settings from cache, read from Cosmos DB instead. Called from e:\repos\simplechat\application\single_app\functions_debug.py:25 in debug_print().

+[DEBUG] [INFO]: [Plugin SUCCESS] TabularProcessingPlugin.filter_rows (2.0ms)

+[Log] Plugin function executed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 2.000570297241211, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.406189', 'parameter_count': 8, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'sheet_name': 'Assets', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "selected_sheet": "Assets",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Assets",\n      "Nov-25": 481225.18\n    }\n  ...', 'result_type': 'str'}

+[Log] [Plugin Invocation] TabularProcessingPlugin.filter_rows -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 2.000570297241211, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.406189', 'parameter_count': 8, 'result_type': 'str', 'error_message': None, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Family Finances.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Assets', 'sheet_name': 'Assets', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Family Finances.xlsx",\n  "selected_sheet": "Assets",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Assets",\n      "Nov-25": 481225.18\n    }\n  ]\n}'}

+✅ Selected worksheet preservation passed

+

+🧪 Running test_single_sheet_workbook_handles_datetime_headers...

+🔍 Testing single-sheet workbook datetime headers...

+[Log] [Plugin Function Logger] Function call started -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file'}

+[Log] [Plugin Function Logger] Function parameters -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Sales Summary.xlsx', 'source': 'workspace'}, 'param_string': 'user_id=test-user, conversation_id=test-conversation, filename=Sales Summary.xlsx, source=workspace'}

+[Log] [Plugin Function Logger] Function completed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'result_preview': '{\n  "selected_sheet": "Summary",\n  "row_count": 2,\n  "column_count": 2,\n  "columns": [\n    "Accounts",\n    "Nov-25"\n  ],\n  "dtypes": {\n    "Accounts": "object",\n    "Nov-25": "float64"\n  },\n  "preview...', 'duration_ms': 1.0001659393310547, 'full_function_name': 'TabularProcessingPlugin.describe_tabular_file'}

+Warning: Failed to get settings from cache, read from Cosmos DB instead. Called from e:\repos\simplechat\application\single_app\functions_debug.py:25 in debug_print().

+[DEBUG] [INFO]: [Plugin SUCCESS] TabularProcessingPlugin.describe_tabular_file (1.0ms)

+[Log] Plugin function executed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'duration_ms': 1.0001659393310547, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.428189', 'parameter_count': 4, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Sales Summary.xlsx', 'source': 'workspace'}, 'result_preview': '{\n  "selected_sheet": "Summary",\n  "row_count": 2,\n  "column_count": 2,\n  "columns": [\n    "Accounts",\n    "Nov-25"\n  ],\n  "dtypes": {\n    "Accounts": "object",\n    "Nov-25": "float64"\n  },\n  "preview...', 'result_type': 'str'}

+[Log] [Plugin Invocation] TabularProcessingPlugin.describe_tabular_file -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'describe_tabular_file', 'duration_ms': 1.0001659393310547, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.428189', 'parameter_count': 4, 'result_type': 'str', 'error_message': None, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Sales Summary.xlsx', 'source': 'workspace'}, 'result_preview': '{\n  "selected_sheet": "Summary",\n  "row_count": 2,\n  "column_count": 2,\n  "columns": [\n    "Accounts",\n    "Nov-25"\n  ],\n  "dtypes": {\n    "Accounts": "object",\n    "Nov-25": "float64"\n  },\n  "preview": [\n    {\n      "Accounts": "Total Sales",\n      "Nov-25": "1500.25"\n    },\n    {\n      "Accounts": "Total Profit",\n      "Nov-25": "420.75"\n    }\n  ],\n  "null_counts": {\n    "Accounts": 0,\n    "Nov-25": 0\n  },\n  "filename": "Sales Summary.xlsx",\n  "is_workbook": true,\n  "sheet_names": [\n    "Summa... [truncated]'}

+[Log] [Plugin Function Logger] Function call started -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows'}

+[Log] [Plugin Function Logger] Function parameters -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Sales Summary.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Sales', 'source': 'workspace'}, 'param_string': 'user_id=test-user, conversation_id=test-conversation, filename=Sales Summary.xlsx, column=Accounts, operator===, value=Total Sales, source=workspace'}

+[Log] [Plugin Function Logger] Function completed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'result_preview': '{\n  "filename": "Sales Summary.xlsx",\n  "selected_sheet": "Summary",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Sales",\n      "Nov-25": 1500.25\n    }\n  ]\n}', 'duration_ms': 2.0012855529785156, 'full_function_name': 'TabularProcessingPlugin.filter_rows'}

+Warning: Failed to get settings from cache, read from Cosmos DB instead. Called from e:\repos\simplechat\application\single_app\functions_debug.py:25 in debug_print().

+[DEBUG] [INFO]: [Plugin SUCCESS] TabularProcessingPlugin.filter_rows (2.0ms)

+[Log] Plugin function executed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 2.0012855529785156, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.447189', 'parameter_count': 7, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Sales Summary.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Sales', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Sales Summary.xlsx",\n  "selected_sheet": "Summary",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Sales",\n      "Nov-25": 1500.25\n    }\n  ]\n}', 'result_type': 'str'}

+[Log] [Plugin Invocation] TabularProcessingPlugin.filter_rows -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'filter_rows', 'duration_ms': 2.0012855529785156, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.447189', 'parameter_count': 7, 'result_type': 'str', 'error_message': None, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'Sales Summary.xlsx', 'column': 'Accounts', 'operator': '==', 'value': 'Total Sales', 'source': 'workspace'}, 'result_preview': '{\n  "filename": "Sales Summary.xlsx",\n  "selected_sheet": "Summary",\n  "total_matches": 1,\n  "returned_rows": 1,\n  "data": [\n    {\n      "Accounts": "Total Sales",\n      "Nov-25": 1500.25\n    }\n  ]\n}'}

+✅ Single-sheet workbook datetime headers passed

+

+🧪 Running test_tabular_analysis_uses_blob_location_fallback...

+🔍 Testing tabular blob location fallback during analysis...

+[Log] [Plugin Function Logger] Function call started -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'group_by_aggregate'}

+[Log] [Plugin Function Logger] Function parameters -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'group_by_aggregate', 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'faa_flight_operations_dataset.csv', 'group_by_column': 'Airline', 'aggregate_column': 'GateTurnTimeMinutes', 'operation': 'min', 'source': 'chat'}, 'param_string': 'user_id=test-user, conversation_id=test-conversation, filename=faa_flight_operations_dataset.csv, group_by_column=Airline, aggregate_column=GateTurnTimeMinutes, operation=min, source=chat'}

+[Log] [Plugin Function Logger] Function completed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'group_by_aggregate', 'result_preview': '{\n  "filename": "faa_flight_operations_dataset.csv",\n  "selected_sheet": null,\n  "group_by": "Airline",\n  "aggregate_column": "GateTurnTimeMinutes",\n  "operation": "min",\n  "groups": 3,\n  "top_results...', 'duration_ms': 19.16646957397461, 'full_function_name': 'TabularProcessingPlugin.group_by_aggregate'}

+Warning: Failed to get settings from cache, read from Cosmos DB instead. Called from e:\repos\simplechat\application\single_app\functions_debug.py:25 in debug_print().

+[DEBUG] [INFO]: [Plugin SUCCESS] TabularProcessingPlugin.group_by_aggregate (19.2ms)

+[Log] Plugin function executed successfully -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'group_by_aggregate', 'duration_ms': 19.16646957397461, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.483355', 'parameter_count': 7, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'faa_flight_operations_dataset.csv', 'group_by_column': 'Airline', 'aggregate_column': 'GateTurnTimeMinutes', 'operation': 'min', 'source': 'chat'}, 'result_preview': '{\n  "filename": "faa_flight_operations_dataset.csv",\n  "selected_sheet": null,\n  "group_by": "Airline",\n  "aggregate_column": "GateTurnTimeMinutes",\n  "operation": "min",\n  "groups": 3,\n  "top_results...', 'result_type': 'str'}

+[Log] [Plugin Invocation] TabularProcessingPlugin.group_by_aggregate -- {'plugin_name': 'TabularProcessingPlugin', 'function_name': 'group_by_aggregate', 'duration_ms': 19.16646957397461, 'success': True, 'user_id': None, 'timestamp': '2026-03-13T16:50:35.483355', 'parameter_count': 7, 'result_type': 'str', 'error_message': None, 'parameters': {'user_id': 'test-user', 'conversation_id': 'test-conversation', 'filename': 'faa_flight_operations_dataset.csv', 'group_by_column': 'Airline', 'aggregate_column': 'GateTurnTimeMinutes', 'operation': 'min', 'source': 'chat'}, 'result_preview': '{\n  "filename": "faa_flight_operations_dataset.csv",\n  "selected_sheet": null,\n  "group_by": "Airline",\n  "aggregate_column": "GateTurnTimeMinutes",\n  "operation": "min",\n  "groups": 3,\n  "top_results": {\n    "AAL": 70,\n    "SWA": 55,\n    "DAL": 44\n  },\n  "result": {\n    "AAL": 70,\n    "DAL": 44,\n    "SWA": 55\n  },\n  "highest_group": "AAL",\n  "highest_value": 70,\n  "lowest_group": "DAL",\n  "lowest_value": 44,\n  "average_group_value": 56.333333333333336,\n  "median_group_value": 55.0,\n  "second_hi... [truncated]'}

+✅ Tabular blob location fallback passed

+

+🧪 Running test_route_preview_and_ui_surface_sheet_metadata...

+🔍 Testing route and UI sheet metadata integration...

+✅ Route and UI sheet metadata integration passed

+

+📊 Results: 6/6 tests passed

+
+STDERR:
+[Plugin Function Logger] Function completed successfully

+Plugin function executed successfully

+[Plugin Invocation] TabularProcessingPlugin.describe_tabular_file

+[TabularProcessingPlugin] Error filtering rows: Workbook 'Family Finances.xlsx' has multiple sheets: ['Balance', 'Assets']. Specify sheet_name or sheet_index on analytical calls.

+[Plugin Function Logger] Function completed successfully

+Plugin function executed successfully

+[Plugin Invocation] TabularProcessingPlugin.filter_rows

+[Plugin Function Logger] Function completed successfully

+Plugin function executed successfully

+[Plugin Invocation] TabularProcessingPlugin.filter_rows

+[Plugin Function Logger] Function completed successfully

+Plugin function executed successfully

+[Plugin Invocation] TabularProcessingPlugin.filter_rows

+[Plugin Function Logger] Function completed successfully

+Plugin function executed successfully

+[Plugin Invocation] TabularProcessingPlugin.describe_tabular_file

+[Plugin Function Logger] Function completed successfully

+Plugin function executed successfully

+[Plugin Invocation] TabularProcessingPlugin.filter_rows

+[Plugin Function Logger] Function completed successfully

+Plugin function executed successfully

+[Plugin Invocation] TabularProcessingPlugin.group_by_aggregate

diff --git a/test_output2.txt b/test_output2.txt
new file mode 100644
index 00000000..0f9285d1
--- /dev/null
+++ b/test_output2.txt
@@ -0,0 +1,3 @@
+functional_tests/test_tabular_datetime_component_analysis.py: 📊 Results: 4/4 tests passed
+functional_tests/test_tabular_grouped_peak_summary.py: 📊 Results: 3/3 tests passed
+functional_tests/test_tabular_tool_error_retry_and_thoughts.py: 📊 Results: 3/3 tests passed
diff --git a/test_results_tabular.json b/test_results_tabular.json
new file mode 100644
index 00000000..29878167
--- /dev/null
+++ b/test_results_tabular.json
@@ -0,0 +1,26 @@
+[
+  {
+    "file": "functional_tests/test_tabular_multisheet_workbook_support.py",
+    "exit_code": 0,
+    "summary": " Results: 7/7 tests passed",
+    "stderr_tail": "Plugin function executed successfully\n[Plugin Invocation] TabularProcessingPlugin.filter_rows\n[Plugin Function Logger] Function completed successfully\nPlugin function executed successfully\n[Plugin Invocation] TabularProcessingPlugin.group_by_aggregate"
+  },
+  {
+    "file": "functional_tests/test_tabular_datetime_component_analysis.py",
+    "exit_code": 0,
+    "summary": " Results: 4/4 tests passed",
+    "stderr_tail": "Plugin function executed successfully\n[Plugin Invocation] TabularProcessingPlugin.group_by_datetime_component\n[Plugin Function Logger] Function completed successfully\nPlugin function executed successfully\n[Plugin Invocation] TabularProcessingPlugin.group_by_datetime_component"
+  },
+  {
+    "file": "functional_tests/test_tabular_grouped_peak_summary.py",
+    "exit_code": 0,
+    "summary": " Results: 3/3 tests passed",
+    "stderr_tail": "Plugin function executed successfully\n[Plugin Invocation] TabularProcessingPlugin.group_by_datetime_component\n[Plugin Function Logger] Function completed successfully\nPlugin function executed successfully\n[Plugin Invocation] TabularProcessingPlugin.group_by_aggregate"
+  },
+  {
+    "file": "functional_tests/test_tabular_tool_error_retry_and_thoughts.py",
+    "exit_code": 0,
+    "summary": " Results: 3/3 tests passed",
+    "stderr_tail": ""
+  }
+]
\ No newline at end of file