Update the root agent to call the arch_mismatch_detector_agent

Author: sherine-k

ci_analysis_agent/agent.py

@@ -22,9 +22,16 @@
 from sub_agents.installation_analyst import installation_analyst_agent
 from sub_agents.e2e_test_analyst import e2e_test_analyst_agent
 from sub_agents.mustgather_analyst import mustgather_analyst_agent
-
+from sub_agents.arch_mismatch_detector import arch_mismatch_detector_agent
 import os
 MODEL = os.environ.get("MODEL", "ollama_chat/qwen3:4b")
+
+def get_job_name(url: str) -> str:
+    return url.split("/")[-2]
+
+def get_build_id(url: str) -> str:
+    return url.split("/")[-1]
+
 ci_analysis_advisor = LlmAgent(
     name="ci_analysis_advisor",
     model=LiteLlm(model=MODEL),
@@ -34,7 +41,10 @@
     instruction=prompt.CI_ANALYSIS_COORDINATOR_PROMPT,
     output_key="ci_analysis_advisor_output",
     tools=[
+        get_job_name,
+        get_build_id,
         AgentTool(agent=installation_analyst_agent),
+        AgentTool(agent=arch_mismatch_detector_agent),
         AgentTool(agent=e2e_test_analyst_agent),
         AgentTool(agent=mustgather_analyst_agent),
     ],

ci_analysis_agent/prompt.py

@@ -46,7 +46,7 @@
 2. ALWAYS perform e2e test analysis to identify test failures and patterns
 3. Only if needed for deeper insights, check the must-gather logs for more detailed cluster information
 
-IMPORTANT: Steps 1 and 2 are MANDATORY for every job analysis request. Do not skip e2e analysis.
+IMPORTANT: Steps 1,2 and 3 are MANDATORY for every job analysis request. Do not skip e2e analysis.
 
 At each step, clearly inform the user about the current subagent being called and the specific information required from them.
 After each subagent completes its task, explain the output provided and how it contributes to the overall root cause analysis process.
@@ -60,6 +60,12 @@
 Action: Parse the URL for the job_name and build_id. Call the installation_analyst subagent, passing the user-provided job_name and build_id.
 Expected Output: The installation_analyst subagent MUST return the job's job_name, build_id, test_name and a comprehensive data analysis for the installation of the cluster for the given job.
 
+* Arch Mismatch Analysis (Subagent: arch_mismatch_detector) - MANDATORY
+
+Input: In the input for the function call (arch_mismatch_detector tool), provide the job_name and build_id. 
+Action:  Call the arch_mismatch_detector subagent, passing the user-provided job_name and build_id.
+Expected Output: The arch_mismatch_detector subagent MUST return a comprehensive analysis on existence of arch mismatch errors (binaries with exec format error) in the job.
+
 * E2E Test Analysis (Subagent: e2e_test_analyst) - MANDATORY
 
 Input: The installation_analysis_output from the installation_analyst subagent.

requirements.txt

@@ -3,4 +3,5 @@ litellm>=1.74.0
 drain3>=0.9.0
 google-cloud-storage>=2.10.0
 python-dotenv>=1.0.0
-httpx>=0.24.0 
+httpx>=0.24.0 
+pytest

sub_agents/arch_mismatch_detector/agent.py

@@ -78,7 +78,7 @@ async def get_job_start_and_end_time_async(job_name: str, build_id: str)-> Dict[
     # model="gemini-2.0-flash",
     name="arch_mismatch_detector_agent",
     instruction=prompt.ARCH_MISMATCH_DETECTOR_PROMPT,
-    output_key="installation_analysis_output",
+    output_key="arch_mismatch_detector_output",
     tools=[
         get_job_start_and_end_time_tool,
         MCPToolset(

sub_agents/e2e_test_analyst/agent.py

@@ -329,6 +329,7 @@ def get_junit_results_tool(job_name: str, build_id: str, test_name: str):
 
 e2e_test_analyst_agent = Agent(
     model=LiteLlm(model=MODEL),
+    description="Analyzes e2e test logs and provides detailed analysis.",
     name="e2e_test_analyst_agent",
     instruction=prompt.E2E_TEST_SPECIALIST_PROMPT,
     output_key="e2e_test_analysis_output",

sub_agents/installation_analyst/agent.py

@@ -317,6 +317,7 @@ def get_install_logs_tool(job_name: str, build_id: str, test_name: str):
     return run_async_in_thread(get_install_logs_async(job_name, build_id, test_name))
 
 installation_analyst_agent = Agent(
+    description="Analyzes installation logs and provides detailed analysis.",
     model=LiteLlm(model=MODEL),
     name="installation_analyst_agent",
     instruction=prompt.INSTALLATION_SPECIALIST_PROMPT,