This diagram shows how all OAI³ components integrate into a unified organizational intelligence system - from user interaction through MIA, CAGAs, CLAGAs, the 5-layer framework, and back to actionable intelligence delivery.
graph TB
subgraph "User Interface Layer"
UI[User Interface<br/>Web / Mobile / Voice]
User[Organization Leaders<br/>& Team Members]
end
subgraph "Orchestration Layer - MIA"
MIA[MIA<br/>Multifactorial Intelligence Alignment<br/>---<br/>Query Interpretation<br/>Agent Coordination<br/>Response Synthesis]
end
User -->|Natural Language Query| UI
UI -->|Structured Request| MIA
subgraph "Intelligence Generation - CAGAs"
direction LR
CAGA_A[CAGA-A<br/>Alignment]
CAGA_H[CAGA-H<br/>Human Cap.]
CAGA_T[CAGA-T<br/>Technical]
CAGA_P[CAGA-P<br/>Privacy]
CAGA_R[CAGA-R<br/>Risk]
CAGA_F[CAGA-F<br/>Financial]
CAGA_O[CAGA-O<br/>Opportunity]
end
subgraph "Cognitive Adaptation - CLAGAs"
CLAGA_System[CLAGA Network<br/>---<br/>Load Detection<br/>State Classification<br/>Delivery Formatting]
LoadStates[Cognitive States<br/>• Low Load<br/>• High Load<br/>• Critical Load]
end
MIA -->|Activate Domain Analysis| CAGA_A
MIA -->|Activate Domain Analysis| CAGA_H
MIA -->|Activate Domain Analysis| CAGA_T
MIA -->|Activate Domain Analysis| CAGA_P
MIA -->|Activate Domain Analysis| CAGA_R
MIA -->|Activate Domain Analysis| CAGA_F
CAGA_A -->|Analysis Results| Synthesis[Intelligence<br/>Synthesis]
CAGA_H -->|Analysis Results| Synthesis
CAGA_T -->|Analysis Results| Synthesis
CAGA_P -->|Analysis Results| Synthesis
CAGA_R -->|Analysis Results| Synthesis
CAGA_F -->|Analysis Results| Synthesis
Synthesis -->|Combined Intelligence| CAGA_O
CAGA_O -->|Prioritized Recommendations| MIA
MIA -->|Assess Cognitive State| CLAGA_System
CLAGA_System -->|Current State| LoadStates
LoadStates -->|Delivery Format| MIA
subgraph "Organizational Knowledge - 5 Layers"
direction TB
L1[Layer 1<br/>Systems & Workflow<br/>Coherence]
L2[Layer 2<br/>Decision<br/>Architecture]
L3[Layer 3<br/>Infrastructure<br/>Readiness]
L4[Layer 4<br/>Intelligence<br/>Integration]
L5[Layer 5<br/>Evolutionary<br/>Capacity]
L1 -->|Informs| L2
L2 -->|Constrains| L3
L3 -->|Enables| L4
L4 -->|Requires| L5
end
L1 -.->|Workflow Context| CAGA_T
L1 -.->|Process Data| CAGA_R
L2 -.->|Decision Context| CAGA_A
L2 -.->|Authority Data| CAGA_H
L3 -.->|Constraint Data| CAGA_F
L3 -.->|Capacity Data| CAGA_H
L3 -.->|Technical Limits| CAGA_T
L4 -.->|Integration History| CAGA_O
L4 -.->|Implementation Data| CAGA_R
L5 -.->|Performance Data| CAGA_F
L5 -.->|Evolution Patterns| CAGA_O
subgraph "Persistent Knowledge Base"
KB[(Organizational<br/>Knowledge Base<br/>---<br/>Historical Data<br/>Patterns<br/>Learnings)]
end
L1 -.->|Stores| KB
L2 -.->|Stores| KB
L3 -.->|Stores| KB
L4 -.->|Stores| KB
L5 -.->|Stores| KB
KB -.->|Historical Context| CAGA_A
KB -.->|Pattern Recognition| CAGA_O
KB -.->|Learning Data| CLAGA_System
MIA -->|Context-Aware<br/>Cognitively-Appropriate<br/>Intelligence| UI
UI -->|Actionable<br/>Recommendations| User
User -.->|Interaction Data| CLAGA_System
User -.->|Outcomes| L5
style MIA fill:#4A90E2,stroke:#2E5C8A,stroke-width:4px,color:#fff
style Synthesis fill:#9B59B6,stroke:#6C3483,stroke-width:3px,color:#fff
style CLAGA_System fill:#FFB347,stroke:#CC7A00,stroke-width:3px,color:#fff
style KB fill:#34495E,stroke:#1C2833,stroke-width:3px,color:#fff
style L5 fill:#FCE4EC,stroke:#E91E63,stroke-width:2px
What happens:
- User submits query through interface (web, mobile, voice)
- UI translates to structured request
- MIA receives and interprets query
Example:
User: "Should we implement AI-powered ticket categorization?"
→ UI: Structured query object
→ MIA: Interprets intent, activates relevant agents
What happens:
- MIA determines which CAGAs are needed
- Activates appropriate domain specialists
- Provides organizational context to each
For this query, MIA activates:
- ✅ CAGA-A (Does this align with goals?)
- ✅ CAGA-H (Do we have capacity?)
- ✅ CAGA-T (Is infrastructure ready?)
- ✅ CAGA-P (Any compliance issues?)
- ✅ CAGA-R (What are the risks?)
- ✅ CAGA-F (What's the ROI?)
- ⏳ CAGA-O (Waits for synthesis)
What happens:
- Each CAGA pulls context from relevant OAI³ layers
- Historical data accessed from Knowledge Base
- Domain-specific analysis performed
Example data flows:
CAGA-T needs:
← Layer 1: Current workflow maps, tool inventory
← Layer 3: Technical infrastructure assessment
← KB: Past implementation patterns
CAGA-H needs:
← Layer 2: Decision authority, team structure
← Layer 3: Current capacity assessment
← KB: Historical workload data
CAGA-F needs:
← Layer 3: Budget constraints
← Layer 4: Past implementation costs
← KB: ROI patterns from similar projects
What happens:
- Each CAGA completes domain analysis
- Results flow to Synthesis Engine
- Multi-dimensional intelligence combined
Synthesis receives:
CAGA-A: Strategic Value = 85/100
CAGA-H: Capacity = Constrained (60%)
CAGA-T: Feasibility = 70/100
CAGA-P: Compliance = Medium risk (needs review)
CAGA-R: Risk = Medium (containable)
CAGA-F: ROI = 4.5 month payback
What happens:
- Synthesized intelligence flows to CAGA-O
- CAGA-O performs multi-factor ranking
- Prioritization and sequencing determined
CAGA-O Output:
Composite Score: 78/100
Priority Ranking: #3 of 12 opportunities
Recommended Sequence: Q2 (after CRM upgrade)
Tradeoffs identified and explained
What happens:
- While CAGAs analyze, CLAGAs assess user state
- Interaction patterns monitored
- Cognitive load level detected
For this interaction:
CLAGA Detection:
- Time: Monday 9:00 AM (low-stress time)
- Pace: Slow, exploratory queries
- Pattern: Asking "how" and "why" questions
→ State: LOW LOAD (detailed analysis mode)
What happens:
- CAGA-O recommendations received
- CLAGA state assessment received
- MIA determines appropriate delivery format
Decision:
Intelligence: Comprehensive analysis available
Cognitive State: LOW LOAD (user wants detail)
→ Delivery: DETAILED MODE
What happens:
- MIA formats response based on cognitive state
- Full context-aware intelligence delivered
- User receives actionable recommendation
Response to User (Detailed Mode):
┌──────────────────────────────────────────────┐
│ AI-Powered Ticket Categorization │
│ Recommendation: IMPLEMENT (Priority #3) │
├──────────────────────────────────────────────┤
│ │
│ Strategic Alignment: 85/100 ⭐ │
│ → Supports customer experience goals │
│ → Aligns with data-driven culture │
│ │
│ Human Capacity: 60/100 ⚠ │
│ → Team at 85% utilization │
│ → Requires 2-week training │
│ │
│ Technical Feasibility: 70/100 ✓ │
│ → CRM integration available │
│ → 6-8 week timeline │
│ │
│ [View Full Analysis] [Risk Assessment] │
│ [Implementation Roadmap] [Compare Options] │
└──────────────────────────────────────────────┘
What happens:
- User interaction stored in Knowledge Base
- Outcome tracked over time
- System learns and improves
Feedback captured:
User approved recommendation → Positive signal
Implementation began Q2 → Timeline data
Actual ROI: 4.2 months → Update CAGA-F model
Team adoption: 85% → Update CAGA-H patterns
What happens:
- Layer 5 monitors system health
- Detects organizational changes
- Triggers adaptation when needed
Example evolution trigger:
3 months later:
- Company expands to new product category
- Ticket categorization accuracy drops 15%
→ Layer 5 detects drift
→ Alerts user: "Model needs retraining"
→ Recommends: Update categorization with new data
Purpose: Coordinate multi-dimensional analysis
How it works:
- MIA activates CAGAs based on query type
- Passes organizational context to each
- Receives domain-specific analysis back
- Ensures all relevant factors considered
Critical Design:
- CAGAs operate independently (no cross-influence during analysis)
- MIA synthesizes after independent analysis complete
- Prevents groupthink, ensures diverse perspectives
Purpose: Adapt delivery to human cognitive capacity
How it works:
- MIA requests cognitive state assessment
- CLAGAs analyze user interaction patterns
- State classification returned (Low/High/Critical)
- MIA formats response accordingly
Critical Design:
- CLAGA assessment runs in parallel with CAGA analysis
- Does not block intelligence generation
- Can adjust mid-session if state changes
Purpose: Ground analysis in organizational reality
How it works:
- Each CAGA pulls context from relevant layers
- Layer 1-2 provide current state data
- Layer 3 provides constraint data
- Layer 4-5 provide historical/evolution data
Critical Design:
- CAGAs access only necessary layer data (not all layers)
- Permissions enforced (sensitive data protected)
- Real-time + historical context combined
Purpose: Enable learning and continuous improvement
How it works:
- All layers store data in Knowledge Base
- All agents access historical patterns
- Outcomes tracked for model improvement
- Cross-organizational patterns recognized
Critical Design:
- Graph database for complex relationships
- Vector embeddings for semantic search
- Time-series for evolution tracking
- Privacy controls for sensitive data
Purpose: Detect drift and trigger evolution
How it works:
- Layer 5 monitors all other layers
- Compares current state to baseline
- Detects changes that require adaptation
- Triggers refresh of affected layers
Critical Design:
- Passive monitoring (doesn't block operations)
- Proactive alerts (before breakdown)
- Feedback loop to Layer 1 (restart cycle)
Query: "What's our readiness score for AI adoption?"
Flow:
User → UI → MIA
MIA → CLAGA (assess state) → LOW LOAD detected
MIA → Layer 3 only (readiness data)
Layer 3 → MIA (readiness score: 65/100)
MIA → UI → User
Time: <500ms
CAGAs activated: None (simple data retrieval)
Query: "Should we pivot to enterprise customers?"
Flow:
User → UI → MIA
MIA → All 7 CAGAs activated
CAGAs → Access Layers 1-5 + KB
Each CAGA → Domain analysis
All CAGAs → Synthesis Engine
Synthesis → CAGA-O (ranking)
CAGA-O → MIA
MIA → CLAGA (assess state) → HIGH LOAD detected
MIA → Format as SIMPLIFIED
MIA → UI → User
Time: 2-5 seconds
CAGAs activated: All 7
Data accessed: Full organizational context
Query: "Customer onboarding is broken NOW!"
Flow:
User → UI → MIA
MIA → CLAGA (assess state) → CRITICAL LOAD
MIA → CAGA-R only (risk analysis)
CAGA-R → Layer 1 (workflow dependencies)
CAGA-R → Quick risk assessment
MIA → Format as EMERGENCY MODE
MIA → UI → User: "Pause onboarding. Alert customers."
Time: <1 second
CAGAs activated: 1 (CAGA-R only)
Detail deferred: Full analysis available after crisis
Every response is grounded in:
- Current organizational state (Layers 1-2)
- Organizational constraints (Layer 3)
- Implementation history (Layer 4)
- Evolution patterns (Layer 5)
- Cross-organizational patterns (KB)
Result: No generic recommendations. Everything is specific to this organization.
Every analysis considers:
- Strategic alignment (CAGA-A)
- Human impact (CAGA-H)
- Technical feasibility (CAGA-T)
- Compliance (CAGA-P)
- Risk (CAGA-R)
- Financial (CAGA-F)
- Synthesis (CAGA-O)
Result: No single-factor thinking. All relevant dimensions considered.
Every delivery is adapted to:
- Current cognitive load (CLAGA detection)
- User preferences (learned over time)
- Situational context (time, urgency)
Result: Right information, right format, right time.
Every interaction improves:
- CAGA models (better domain analysis)
- CLAGA models (better load detection)
- Knowledge Base (richer patterns)
- Layer 5 (better evolution detection)
Result: System gets smarter over time.
System adapts when:
- Organization changes (detected by Layer 5)
- Workflows evolve (updated in Layer 1)
- New constraints emerge (updated in Layer 3)
- Performance degrades (caught by monitoring)
Result: Intelligence doesn't degrade; it evolves.
Frontend:
- Web: React (responsive UI)
- Mobile: React Native
- Voice: Natural language processing
Backend:
- API Layer: Node.js / FastAPI
- MIA Orchestration: Python (LLM-based)
- CAGA Engines: Python (ML + rule-based)
- CLAGA Engine: Python (real-time detection)
Data:
- Knowledge Base: Neo4j (graph) + PostgreSQL (relational)
- Vector Store: Pinecone / Weaviate
- Cache: Redis
- Queue: RabbitMQ / Kafka
Infrastructure:
- Cloud: AWS / GCP
- Containers: Docker + Kubernetes
- Monitoring: Prometheus + Grafana
- Simple queries: <500ms response
- Complex analysis: <5s response
- Emergency mode: <1s response
- CLAGA detection: <50ms
- System uptime: 99.9%
- Horizontal scaling for CAGAs (parallel execution)
- Async processing for complex queries
- Caching for frequent analyses
- Load balancing across MIA instances
- Created: December 2025
- Version: 2.0
- Part of: OAI³ Framework Architecture Documentation
- Related Diagrams:
- MIA Orchestration Flow
- CAGA Network Architecture
- CLAGA Adaptation Flow
- OAI³ Implementation Stack
- CosentriQ Platform Architecture