cache key error when user id changes -fixed task 1 31_10_2025 v5

APPLICATION_FEATURES_REPORT.md

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+# Research AI Assistant: Key Features Report
+
+## Executive Summary
+
+This application implements a **multi-agent orchestration system** for research assistance with transparent reasoning chains, context-aware conversation management, and adaptive expert consultation assignment. The system employs **task-based LLM routing**, **hierarchical context summarization**, and **non-blocking safety validation** to deliver contextually relevant, academically rigorous responses.
+
+---
+
+## 1. Multi-Agent Orchestration Architecture
+
+### 1.1 Central Orchestration Engine (`MVPOrchestrator`)
+- **Sequential workflow coordination**: Manages a deterministic pipeline of specialized agents
+- **Execution trace logging**: Maintains comprehensive audit trails of agent execution
+- **Graceful degradation**: Implements fallback mechanisms at every processing stage
+- **Reasoning chain generation**: Constructs explicit chain-of-thought (CoT) reasoning structures with:
+  - Hypothesis formation
+  - Evidence collection
+  - Confidence calibration
+  - Alternative path analysis
+  - Uncertainty identification
+
+### 1.2 Specialized Agent Modules
+
+#### Intent Recognition Agent (`IntentRecognitionAgent`)
+- **Multi-class intent classification**: Categorizes user queries into 8 intent types:
+  - Information requests
+  - Task execution
+  - Creative generation
+  - Analysis/research
+  - Casual conversation
+  - Troubleshooting
+  - Education/learning
+  - Technical support
+- **Dual-mode operation**: LLM-enhanced classification with rule-based fallback
+- **Confidence calibration**: Multi-factor confidence scoring with context enhancement
+- **Secondary intent detection**: Identifies complementary intent interpretations
+
+#### Skills Identification Agent (`SkillsIdentificationAgent`)
+- **Market analysis integration**: Leverages 9 industry categories with market share data
+- **Dual-stage processing**:
+  1. Market relevance analysis (reasoning_primary model)
+  2. Skill classification (classification_specialist model)
+- **Probability-based skill mapping**: Identifies expert skills with ≥20% relevance threshold
+- **Expert consultant assignment**: Feeds skill probabilities to synthesis agent for consultant profile selection
+
+#### Response Synthesis Agent (`SynthesisAgent`)
+- **Expert consultant integration**: Dynamically assigns ultra-expert profiles based on identified skills
+- **Multi-source synthesis**: Integrates outputs from multiple specialized agents
+- **Weighted expertise combination**: Creates composite consultant profiles from relevant skill domains
+- **Coherence scoring**: Evaluates response quality and structure
+
+#### Safety Check Agent (`SafetyCheckAgent`)
+- **Non-blocking safety validation**: Appends advisory warnings without content modification
+- **Multi-dimensional analysis**: Evaluates toxicity, bias, privacy, and controversial content
+- **Threshold-based warnings**: Generates contextual warnings when safety scores exceed thresholds
+- **Pattern-based fallback**: Rule-based detection when LLM analysis unavailable
+
+---
+
+## 2. Context Management System
+
+### 2.1 Hierarchical Context Architecture
+The system implements a **three-tier context summarization** strategy:
+
+#### Tier 1: User Context (500 tokens)
+- **Persistent persona summaries**: Cross-session user profiles generated from historical interactions
+- **Lifespan**: Persists across all sessions for a given user_id
+- **Generation trigger**: Automatically generated when user has sufficient interaction history
+- **Content**: Communication style, topic preferences, interaction patterns
+
+#### Tier 2: Session Context (100 tokens)
+- **Session-level summaries**: Summarizes all interactions within a single session
+- **Generation trigger**: Generated at session end
+- **Storage**: Stored in `session_contexts` table linked to user_id
+
+#### Tier 3: Interaction Context (50 tokens)
+- **Per-interaction summaries**: Compact summaries of individual exchanges
+- **Generation trigger**: Generated after each response
+- **Storage**: Stored in `interaction_contexts` table
+- **Retrieval**: Last 20 interaction contexts loaded per session
+
+### 2.2 Context Optimization Features
+- **Multi-level caching**: In-memory session cache + SQLite persistence
+- **Transaction-based updates**: Atomic database operations with write-ahead logging (WAL)
+- **Deduplication**: SHA-256 hash-based duplicate interaction prevention
+- **Cache invalidation**: Automatic cache clearing on user_id changes
+- **Database indexing**: Optimized queries with indexes on session_id, user_id, timestamps
+
+### 2.3 Context Delivery Format
+Context delivered to agents in structured format:
+```
+[User Context]
+[User persona summary - 500 tokens]
+
+[Interaction Context #N]
+[Most recent interaction summary - 50 tokens]
+
+[Interaction Context #N-1]
+[Previous interaction summary - 50 tokens]
+...
+```
+
+---
+
+## 3. LLM Routing System
+
+### 3.1 Task-Based Model Routing (`LLMRouter`)
+Implements **intelligent model selection** based on task specialization:
+
+| Task Type | Model Assignment | Purpose |
+|-----------|-----------------|---------|
+| `intent_classification` | `classification_specialist` | Fast intent categorization |
+| `embedding_generation` | `embedding_specialist` | Semantic similarity (currently unused) |
+| `safety_check` | `safety_checker` | Content moderation |
+| `general_reasoning` | `reasoning_primary` | Primary response generation |
+| `response_synthesis` | `reasoning_primary` | Multi-source synthesis |
+
+### 3.2 Model Configuration (`LLM_CONFIG`)
+- **Primary model**: `Qwen/Qwen2.5-7B-Instruct` (chat completions API)
+- **Fallback chain**: Primary → Fallback → Degraded mode
+- **Health checking**: Model availability monitoring with automatic fallback
+- **Retry logic**: Exponential backoff (1s → 16s max) with 3 retry attempts
+- **API protocol**: Hugging Face Chat Completions API (`router.huggingface.co/v1/chat/completions`)
+
+### 3.3 Performance Optimizations
+- **Timeout management**: 30-second request timeout
+- **Connection pooling**: Reusable HTTP connections
+- **Request/response logging**: Comprehensive logging of all LLM API interactions
+
+---
+
+## 4. Reasoning and Transparency
+
+### 4.1 Chain-of-Thought Reasoning
+The orchestrator generates **explicit reasoning chains** for each request:
+
+```python
+reasoning_chain = {
+    "chain_of_thought": {
+        "step_1": {
+            "hypothesis": "User intent analysis",
+            "evidence": [...],
+            "confidence": 0.85,
+            "reasoning": "..."
+        },
+        "step_2": {...},
+        ...
+    },
+    "alternative_paths": [...],
+    "uncertainty_areas": [...],
+    "evidence_sources": [...],
+    "confidence_calibration": {...}
+}
+```
+
+### 4.2 Reasoning Components
+- **Hypothesis formation**: Explicit hypothesis statements at each processing step
+- **Evidence collection**: Structured evidence arrays supporting each hypothesis
+- **Confidence calibration**: Weighted confidence scoring across reasoning steps
+- **Alternative path analysis**: Consideration of alternative interpretation paths
+- **Uncertainty identification**: Explicit documentation of low-confidence areas
+
+### 4.3 Metadata Generation
+Every response includes:
+- **Agent execution trace**: Complete log of agents executed
+- **Processing time**: Performance metrics
+- **Token count**: Resource usage tracking
+- **Confidence scores**: Overall confidence in response quality
+- **Skills identification**: Relevant expert skills for the query
+
+---
+
+## 5. Expert Consultant Assignment
+
+### 5.1 Dynamic Consultant Selection
+The synthesis agent employs **ExpertConsultantAssigner** to create composite consultant profiles:
+
+- **10 predefined expert profiles**: Data analysis, technical programming, project management, financial analysis, digital marketing, business consulting, cybersecurity, healthcare technology, educational technology, environmental science
+- **Weighted expertise combination**: Creates "ultra-expert" profiles by combining relevant consultants based on skill probabilities
+- **Experience aggregation**: Sums years of experience across combined experts
+- **Style integration**: Merges consulting styles from multiple domains
+
+### 5.2 Market Analysis Integration
+- **9 industry categories** with market share and growth rate data
+- **Specialized skill mapping**: 3-7 specialized skills per category
+- **Relevance scoring**: Skills ranked by relevance to user query
+- **Market context**: Response synthesis informed by industry trends
+
+---
+
+## 6. Safety and Bias Mitigation
+
+### 6.1 Non-Blocking Safety System
+- **Warning-based approach**: Appends safety advisories without blocking content
+- **Multi-dimensional analysis**: Evaluates toxicity, bias, privacy, controversial content
+- **Intent-aware thresholds**: Different thresholds per intent category
+- **Automatic warning injection**: Safety warnings automatically appended when thresholds exceeded
+
+### 6.2 Safety Thresholds
+```python
+safety_thresholds = {
+    "toxicity_or_harmful_language": 0.3,
+    "potential_biases_or_stereotypes": 0.05,  # Low threshold for bias
+    "privacy_or_security_concerns": 0.2,
+    "controversial_or_sensitive_topics": 0.3
+}
+```
+
+### 6.3 User Choice Feature (Paused)
+- **Design**: Originally designed to prompt user for revision approval
+- **Current implementation**: Warnings automatically appended to responses
+- **No blocking**: All responses delivered regardless of safety scores
+
+---
+
+## 7. User Interface
+
+### 7.1 Mobile-First Design
+- **Responsive layout**: Adaptive UI for mobile, tablet, desktop
+- **Touch-optimized**: 44px minimum touch targets (iOS/Android guidelines)
+- **Font sizing**: 16px minimum to prevent mobile browser zoom
+- **Viewport management**: 60vh chat container with optimized scrolling
+
+### 7.2 UI Components
+- **Chat interface**: Gradio chatbot with message history
+- **Skills display**: Visual tags showing identified expert skills with confidence indicators
+- **Details tab**: Collapsible accordions showing:
+  - Reasoning chain (JSON)
+  - Agent performance metrics
+  - Session context data
+- **Session management**: User selection dropdown, session ID display, new session button
+
+### 7.3 Progressive Web App Features
+- **Offline capability**: Cached session data
+- **Dark mode support**: CSS media queries for system preference
+- **Accessibility**: Screen reader compatible, keyboard navigation
+
+---
+
+## 8. Database Architecture
+
+### 8.1 Schema Design
+**Tables:**
+1. `sessions`: Session metadata, context data, user_id tracking
+2. `interactions`: Individual interaction records with context snapshots
+3. `user_contexts`: Persistent user persona summaries (500 tokens)
+4. `session_contexts`: Session-level summaries (100 tokens)
+5. `interaction_contexts`: Individual interaction summaries (50 tokens)
+6. `user_change_log`: Audit log of user_id changes
+
+### 8.2 Data Integrity Features
+- **Transaction management**: Atomic operations with rollback on failure
+- **Foreign key constraints**: Referential integrity enforcement
+- **Deduplication**: SHA-256 hash-based unique interaction tracking
+- **Indexing**: Optimized indexes on frequently queried columns
+
+### 8.3 Concurrency Management
+- **Thread-safe transactions**: RLock-based locking for concurrent access
+- **Write-Ahead Logging (WAL)**: SQLite WAL mode for better concurrency
+- **Busy timeout**: 5-second timeout for lock acquisition
+- **Connection pooling**: Efficient database connection reuse
+
+---
+
+## 9. Performance Optimizations
+
+### 9.1 Caching Strategy
+- **Multi-level caching**: In-memory session cache + persistent SQLite storage
+- **Cache TTL**: 1-hour time-to-live for session cache
+- **LRU eviction**: Least-recently-used eviction policy
+- **Cache warming**: Pre-loading frequently accessed sessions
+
+### 9.2 Request Processing
+- **Async/await architecture**: Fully asynchronous agent execution
+- **Parallel agent execution**: Concurrent execution when execution_plan specifies parallel order
+- **Sequential fallback**: Sequential execution for dependency-sensitive tasks
+- **Timeout protection**: 30-second timeout for safety revision loops
+
+### 9.3 Resource Management
+- **Token budget management**: Configurable max_tokens per model
+- **Session size limits**: 10MB per session maximum
+- **Interaction history limits**: Last 40 interactions kept in memory, 20 loaded from database
+
+---
+
+## 10. Error Handling and Resilience
+
+### 10.1 Graceful Degradation
+- **Multi-level fallbacks**: LLM → Rule-based → Default responses
+- **Error isolation**: Agent failures don't cascade to system failure
+- **Fallback responses**: Always returns user-facing response, never None
+- **Comprehensive logging**: All errors logged with stack traces
+
+### 10.2 Loop Prevention
+- **Safety response detection**: Prevents recursive safety checks on binary responses
+- **Context retrieval caching**: 5-second cache prevents rapid successive context fetches
+- **User change tracking**: Prevents context loops when user_id changes mid-session
+- **Deduplication**: Prevents duplicate interaction processing
+
+---
+
+## 11. Academic Rigor Features
+
+### 11.1 Transparent Reasoning
+- **Explicit CoT chains**: All reasoning steps documented
+- **Evidence citation**: Structured evidence arrays for each hypothesis
+- **Uncertainty quantification**: Explicit confidence scores and uncertainty areas
+- **Alternative consideration**: Documented alternative interpretation paths
+
+### 11.2 Reproducibility
+- **Execution traces**: Complete logs of agent execution order
+- **Interaction IDs**: Unique identifiers for every interaction
+- **Timestamp tracking**: Precise timestamps for all operations
+- **Database audit trail**: Complete interaction history persisted
+
+### 11.3 Quality Metrics
+- **Confidence calibration**: Weighted confidence scoring across steps
+- **Coherence scoring**: Response quality evaluation
+- **Processing time tracking**: Performance monitoring
+- **Token usage tracking**: Resource consumption monitoring
+
+---
+
+## Technical Specifications
+
+### Dependencies
+- **Gradio**: UI framework
+- **SQLite**: Database persistence
+- **Hugging Face API**: LLM inference
+- **asyncio**: Asynchronous execution
+- **Python 3.x**: Core runtime
+
+### Deployment
+- **Platform**: Hugging Face Spaces (configurable)
+- **Containerization**: Dockerfile support
+- **GPU support**: Optional ZeroGPU allocation on HF Spaces
+- **Environment**: Configurable via environment variables
+
+---
+
+## Summary
+
+This application implements a **sophisticated multi-agent research assistance system** with the following distinguishing features:
+
+1. **Hierarchical context summarization** (50/100/500 token tiers)
+2. **Transparent reasoning chains** with explicit CoT documentation
+3. **Dynamic expert consultant assignment** based on skill identification
+4. **Non-blocking safety validation** with automatic warning injection
+5. **Task-based LLM routing** with intelligent fallback chains
+6. **Mobile-optimized interface** with PWA capabilities
+7. **Robust error handling** with graceful degradation at every layer
+8. **Academic rigor** through comprehensive metadata and audit trails
+
+The system prioritizes **transparency**, **reliability**, and **contextual relevance** while maintaining **production-grade error handling** and **performance optimization**.
+

CONTEXT_PROVISION_ANALYSIS.md

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+# Context Provision Analysis: Intent Agent Context Flow
+
+## Problem Statement
+
+The Intent Recognition Agent (`src/agents/intent_agent.py`) expects a context dictionary with a `conversation_history` key, but the actual context structure provided by `EfficientContextManager` does not include this key. This results in `Available Context: []` being shown in the intent recognition prompt.
+
+## Context Flow Trace
+
+### Step 1: Orchestrator Retrieves Context
+
+**Location**: `src/orchestrator_engine.py:172`
+
+```python
+context = await self._get_or_create_context(session_id, user_input, user_id)
+```
+
+This calls `context_manager.manage_context()` which returns a context dictionary.
+
+### Step 2: Context Structure Returned by Context Manager
+
+**Location**: `src/context_manager.py:550-579` (`_optimize_context` method)
+
+The context manager returns the following structure:
+
+```python
+{
+    "session_id": str,
+    "user_id": str,
+    "user_context": str,  # 500-token user persona summary
+    "interaction_contexts": [  # List of interaction summary dicts
+        {
+            "summary": str,  # 50-token interaction summary
+            "timestamp": str
+        },
+        ...
+    ],
+    "combined_context": str,  # Formatted string: "[User Context]\n...\n[Interaction Context #N]\n..."
+    "preferences": dict,
+    "active_tasks": list,
+    "last_activity": str
+}
+```
+
+### Step 3: Intent Agent Receives Context
+
+**Location**: `src/orchestrator_engine.py:201-203`
+
+```python
+intent_result = await self.agents['intent_recognition'].execute(
+    user_input=user_input,
+    context=context
+)
+```
+
+### Step 4: Intent Agent Attempts to Access Context
+
+**Location**: `src/agents/intent_agent.py:109`
+
+```python
+def _build_chain_of_thought_prompt(self, user_input: str, context: Dict[str, Any]) -> str:
+    return f"""
+    Analyze the user's intent step by step:
+
+    User Input: "{user_input}"
+    
+    Available Context: {context.get('conversation_history', [])[-2:] if context else []}
+    ...
+    """
+```
+
+**Issue**: The code attempts to access `context.get('conversation_history', [])`, but the context dictionary **does not contain this key**.
+
+## Root Cause Analysis
+
+### Expected Context Structure (by Intent Agent)
+The intent agent expects:
+```python
+context = {
+    'conversation_history': [
+        {...},  # Previous conversation turn
+        {...},  # Another previous turn
+        ...
+    ]
+}
+```
+
+### Actual Context Structure (from Context Manager)
+The context manager provides:
+```python
+context = {
+    'interaction_contexts': [
+        {'summary': '...', 'timestamp': '...'},
+        {'summary': '...', 'timestamp': '...'},
+        ...
+    ],
+    'user_context': '...',
+    'combined_context': '...',
+    ...
+}
+```
+
+### Why This Mismatch Exists
+
+1. **Historical Evolution**: The intent agent was likely designed with an earlier context structure in mind
+2. **Context Manager Redesign**: The context manager was redesigned to use hierarchical summarization (50/100/500 token tiers) instead of full conversation history
+3. **Missing Adaptation**: The intent agent was not updated to use the new context structure
+
+## Impact Analysis
+
+### First Turn (No Previous Context)
+- `interaction_contexts` = `[]` (empty list)
+- `user_context` = `""` (empty string if first-time user)
+- **Result**: `Available Context: []` ✓ (Correct, but for wrong reason - key doesn't exist)
+
+### Second Turn (After First Interaction)
+- `interaction_contexts` = `[{summary: "...", timestamp: "..."}]` (1 interaction)
+- `user_context` = `""` or persona summary if user has history
+- **Result**: `Available Context: []` ✗ (Incorrect - context exists but wrong key accessed)
+
+### Third Turn and Beyond
+- `interaction_contexts` = `[{...}, {...}, ...]` (multiple interactions)
+- `user_context` = Persona summary (if user has sufficient history)
+- **Result**: `Available Context: []` ✗ (Incorrect - rich context exists but not accessible)
+
+## Context Accumulation Over Multiple Turns
+
+### Turn 1: User says "What is machine learning?"
+1. Context Manager retrieves context:
+   - `interaction_contexts`: `[]` (no previous interactions)
+   - `user_context`: `""` (first-time user, no persona yet)
+   - Context passed to intent agent
+
+2. Intent Agent builds prompt:
+   - `context.get('conversation_history', [])[-2:]` → `[]`
+   - **Shows**: `Available Context: []`
+
+3. After response, interaction context generated:
+   - `generate_interaction_context()` called
+   - Creates 50-token summary: "User asked about machine learning definition"
+   - Stored in `interaction_contexts` table
+
+### Turn 2: User says "How does it differ from deep learning?"
+1. Context Manager retrieves context:
+   - `interaction_contexts`: `[{summary: "User asked about machine learning definition", timestamp: "..."}]`
+   - `user_context`: Still `""` (not enough history for persona)
+   - Context passed to intent agent
+
+2. Intent Agent builds prompt:
+   - `context.get('conversation_history', [])[-2:]` → `[]` (key doesn't exist!)
+   - **Shows**: `Available Context: []` ✗ **SHOULD show the interaction summary**
+
+3. After response, another interaction context generated:
+   - Creates: "User asked about differences between machine learning and deep learning"
+   - Stored in `interaction_contexts` table
+   - Now has 2 interaction contexts
+
+### Turn 3: User says "Can you explain neural networks?"
+1. Context Manager retrieves context:
+   - `interaction_contexts`: `[{summary: "...deep learning..."}, {summary: "...machine learning..."}]`
+   - `user_context`: Still `""` (persona generated only after sufficient history)
+   - Context passed to intent agent
+
+2. Intent Agent builds prompt:
+   - `context.get('conversation_history', [])[-2:]` → `[]` (key doesn't exist!)
+   - **Shows**: `Available Context: []` ✗ **SHOULD show 2 interaction summaries**
+
+### After ~20-50 Interactions (User Persona Generation)
+1. Context Manager retrieves context:
+   - `interaction_contexts`: `[{...}, {...}, ...]` (up to 20 most recent)
+   - `user_context`: `"User persona: Interested in AI topics, asks technical questions..."` (500-token summary)
+   - Context passed to intent agent
+
+2. Intent Agent builds prompt:
+   - `context.get('conversation_history', [])[-2:]` → `[]` (key doesn't exist!)
+   - **Shows**: `Available Context: []` ✗ **SHOULD show rich context including user persona and interaction history**
+
+## Available Context Data (Not Being Used)
+
+### What Context Actually Contains
+
+#### Turn 1:
+```python
+context = {
+    "interaction_contexts": [],  # Empty - first turn
+    "user_context": "",  # Empty - first-time user
+    "combined_context": "",  # Empty
+}
+```
+
+#### Turn 2:
+```python
+context = {
+    "interaction_contexts": [
+        {"summary": "User asked about machine learning definition", "timestamp": "..."}
+    ],
+    "user_context": "",  # Still empty
+    "combined_context": "[Interaction Context #1]\nUser asked about machine learning definition",
+}
+```
+
+#### Turn 3+:
+```python
+context = {
+    "interaction_contexts": [
+        {"summary": "User asked about differences between ML and DL", "timestamp": "..."},
+        {"summary": "User asked about machine learning definition", "timestamp": "..."},
+        # ... more interactions
+    ],
+    "user_context": "User persona: Interested in AI topics...",  # If sufficient history
+    "combined_context": "[User Context]\nUser persona...\n\n[Interaction Context #2]\n...\n\n[Interaction Context #1]\n...",
+}
+```
+
+## Recommended Solutions
+
+### Option 1: Use `interaction_contexts` Directly (Minimal Change)
+
+**Modify**: `src/agents/intent_agent.py:109`
+
+```python
+# OLD:
+Available Context: {context.get('conversation_history', [])[-2:] if context else []}
+
+# NEW:
+Available Context: {[ic.get('summary', '') for ic in context.get('interaction_contexts', [])[-2:]] if context else []}
+```
+
+**Pros**:
+- Minimal code change
+- Uses actual context data
+- Shows last 2 interaction summaries
+
+**Cons**:
+- Only shows summaries, not full conversation
+- Loses timestamp information
+
+### Option 2: Use `combined_context` (Preferred)
+
+**Modify**: `src/agents/intent_agent.py:109`
+
+```python
+# OLD:
+Available Context: {context.get('conversation_history', [])[-2:] if context else []}
+
+# NEW:
+Available Context: {context.get('combined_context', 'No previous context available') if context else 'No context available'}
+```
+
+**Pros**:
+- Uses pre-formatted context string
+- Includes both user context and interaction contexts
+- More informative for intent recognition
+- Better reflects the hierarchical context system
+
+**Cons**:
+- May include more than just last 2 turns (includes up to 10 interactions)
+- Longer context string
+
+### Option 3: Build Conversation History from Interaction Contexts (Most Flexible)
+
+**Modify**: `src/agents/intent_agent.py:101-118`
+
+```python
+def _build_chain_of_thought_prompt(self, user_input: str, context: Dict[str, Any]) -> str:
+    """Build Chain of Thought prompt for intent recognition"""
+    
+    # Extract conversation history from interaction_contexts
+    conversation_history = []
+    if context:
+        interaction_contexts = context.get('interaction_contexts', [])
+        # Get last 2 interaction summaries for context
+        for ic in interaction_contexts[-2:]:
+            conversation_history.append({
+                'summary': ic.get('summary', ''),
+                'timestamp': ic.get('timestamp', '')
+            })
+    
+    # Optionally include user context
+    user_context_summary = ""
+    if context and context.get('user_context'):
+        user_context_summary = f"\nUser Context: {context.get('user_context')[:200]}..."  # Truncate for brevity
+    
+    return f"""
+    Analyze the user's intent step by step:
+
+    User Input: "{user_input}"
+    
+    Previous Context: {conversation_history if conversation_history else 'No previous interactions'}
+    {user_context_summary}
+    
+    Step 1: Identify key entities, actions, and questions in the input
+    Step 2: Map to intent categories: {', '.join(self.intent_categories)}
+    Step 3: Consider the conversation flow and user's likely goals
+    Step 4: Assign confidence scores (0.0-1.0) for each relevant intent
+    Step 5: Provide reasoning for the classification
+    
+    Respond with JSON format containing primary_intent, secondary_intents, confidence_scores, and reasoning_chain.
+    """
+```
+
+**Pros**:
+- Flexible format
+- Can include both interaction history and user context
+- Properly handles empty context
+- More informative for LLM
+
+**Cons**:
+- More code changes
+- Slightly more complex
+
+## Current Behavior Summary
+
+| Turn | Interaction Contexts Available | User Context Available | Intent Agent Sees |
+|------|-------------------------------|----------------------|-------------------|
+| 1    | 0                             | No                   | `[]` (empty)      |
+| 2    | 1                             | No                   | `[]` (empty) ✗    |
+| 3    | 2                             | Possibly             | `[]` (empty) ✗    |
+| 10+  | 10-20                         | Yes (if sufficient history) | `[]` (empty) ✗ |
+
+**Key Issue**: Intent agent never sees the available context data because it looks for the wrong key (`conversation_history` instead of `interaction_contexts` or `combined_context`).
+
+## Testing Recommendations
+
+1. **Verify Context Structure**: Log the actual context dict passed to intent agent
+2. **Test Multiple Turns**: Verify context accumulates correctly over multiple interactions
+3. **Test Persona Generation**: Verify user_context appears after sufficient history
+4. **Compare Intent Accuracy**: Measure if fixing context access improves intent recognition accuracy
+
+## Implementation Priority
+
+**High Priority**: This bug prevents the intent agent from using available conversation context, which likely:
+- Reduces intent recognition accuracy for follow-up questions
+- Prevents context-aware intent classification
+- Wastes the hierarchical context summarization system
+
+**Recommended Fix**: Option 2 (Use `combined_context`) as it's the simplest and most comprehensive solution.
+

CONTEXT_STRUCTURE_FIX_IMPLEMENTATION.md

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+# Context Structure Fix Implementation
+
+## Summary
+
+Fixed context structure mismatches across all agents to properly use the Context Manager's actual data structure. All agents now correctly access `interaction_contexts`, `user_context`, and `combined_context` instead of non-existent keys like `conversation_history` or `interactions`.
+
+## Changes Made
+
+### 1. Intent Recognition Agent (`src/agents/intent_agent.py`)
+
+**Problem**: Was accessing `context.get('conversation_history', [])` which doesn't exist.
+
+**Fix**: 
+- Now uses `combined_context` (preferred) or builds from `interaction_contexts` and `user_context`
+- Shows last 2 interaction summaries for context awareness
+- Includes user context if available
+- Provides informative message when no context is available
+
+**Key Changes**:
+```python
+# OLD (line 109):
+Available Context: {context.get('conversation_history', [])[-2:] if context else []}
+
+# NEW:
+# Uses combined_context if available, otherwise builds from interaction_contexts
+combined_context = context.get('combined_context', '')
+interaction_contexts = context.get('interaction_contexts', [])
+user_context = context.get('user_context', '')
+```
+
+**Impact**: Intent agent now sees actual conversation history, improving intent recognition accuracy for follow-up questions.
+
+---
+
+### 2. Response Synthesis Agent (`src/agents/synthesis_agent.py`)
+
+**Problem**: Was accessing `context.get('interactions', [])` which doesn't exist.
+
+**Fix**:
+- `_build_context_section()` now uses `combined_context` (preferred) or builds from `interaction_contexts`
+- Updated `_summarize_interaction_contexts()` to work with Context Manager structure
+- Added backward compatibility via `_summarize_interactions()` wrapper
+- Updated logging and metadata to use correct keys
+
+**Key Changes**:
+```python
+# OLD (line 534):
+interactions = context.get('interactions', [])
+
+# NEW:
+combined_context = context.get('combined_context', '')
+interaction_contexts = context.get('interaction_contexts', [])
+user_context = context.get('user_context', '')
+```
+
+**Impact**: Synthesis agent now uses actual conversation context for generating contextually relevant responses.
+
+---
+
+### 3. Safety Check Agent (`src/agents/safety_agent.py`)
+
+**Problem**: Wasn't using context at all in safety analysis.
+
+**Fix**:
+- Enhanced `_build_safety_prompt()` to include context information
+- Uses `user_context` and recent `interaction_contexts` for context-aware safety analysis
+- Helps safety agent understand conversational context when assessing content appropriateness
+
+**Key Changes**:
+```python
+# Added context awareness:
+user_context = context.get('user_context', '')
+interaction_contexts = context.get('interaction_contexts', [])
+# Includes context in safety analysis prompt
+```
+
+**Impact**: Safety analysis now considers conversation context, improving appropriateness assessment.
+
+---
+
+### 4. Skills Identification Agent (`src/agents/skills_identification_agent.py`)
+
+**Problem**: Wasn't using context in skill identification.
+
+**Fix**:
+- Enhanced `_build_market_analysis_prompt()` to accept and use context parameter
+- Includes user context and recent interaction contexts in market analysis
+- Helps identify skills based on conversation continuity
+
+**Key Changes**:
+```python
+# Updated method signature:
+def _build_market_analysis_prompt(self, user_input: str, context: Dict[str, Any] = None)
+
+# Added context information:
+user_context = context.get('user_context', '')
+interaction_contexts = context.get('interaction_contexts', [])
+```
+
+**Impact**: Skills identification now considers conversation history for better skill relevance.
+
+---
+
+## Context Structure Reference
+
+All agents now correctly use the Context Manager's structure:
+
+```python
+context = {
+    "session_id": str,
+    "user_id": str,
+    "user_context": str,  # 500-token user persona summary
+    "interaction_contexts": [  # List of interaction summary dicts
+        {
+            "summary": str,  # 50-token interaction summary
+            "timestamp": str
+        },
+        ...
+    ],
+    "combined_context": str,  # Pre-formatted: "[User Context]\n...\n[Interaction Context #N]\n..."
+    "preferences": dict,
+    "active_tasks": list,
+    "last_activity": str
+}
+```
+
+## Implementation Strategy
+
+### Priority Order
+1. **Use `combined_context` first** - Pre-formatted by Context Manager, most efficient
+2. **Fallback to building from components** - If `combined_context` not available
+3. **Handle empty context gracefully** - Informative messages when no context exists
+
+### Context Access Pattern
+```python
+# Preferred pattern used across all agents:
+if context:
+    # Option 1: Use pre-formatted combined_context
+    combined_context = context.get('combined_context', '')
+    if combined_context:
+        # Use combined_context directly
+        context_info = combined_context
+    
+    # Option 2: Build from components
+    else:
+        user_context = context.get('user_context', '')
+        interaction_contexts = context.get('interaction_contexts', [])
+        # Build context_info from components
+```
+
+## Testing Recommendations
+
+### Test Scenarios
+
+1. **First Turn (No Context)**
+   - Verify agents handle empty context gracefully
+   - Verify informative messages when no context available
+
+2. **Second Turn (1 Interaction)**
+   - Verify agents access `interaction_contexts[0]`
+   - Verify context appears in prompts
+
+3. **Multiple Turns (3+ Interactions)**
+   - Verify agents use last N interaction contexts
+   - Verify context accumulates correctly
+
+4. **With User Persona (20+ Interactions)**
+   - Verify `user_context` appears in prompts
+   - Verify `combined_context` includes user context
+
+### Expected Behavior
+
+| Turn | Intent Agent Sees | Synthesis Agent Sees | Safety Agent Sees | Skills Agent Sees |
+|------|------------------|---------------------|-------------------|-------------------|
+| 1    | "No previous context" | Empty | No context | No context |
+| 2    | Interaction #1 summary | Interaction #1 | Recent context | Recent context |
+| 3+   | Last 2 interactions | All/Summarized interactions | Recent context | Recent context |
+| 20+  | User context + interactions | User context + interactions | User context | User context |
+
+## Benefits
+
+1. **Intent Recognition**: Now context-aware, better accuracy for follow-up questions
+2. **Response Synthesis**: Uses conversation history for more relevant responses
+3. **Safety Analysis**: Context-aware appropriateness assessment
+4. **Skills Identification**: Considers conversation continuity for better skill matching
+5. **Consistency**: All agents use the same context structure
+6. **Performance**: Uses pre-formatted `combined_context` when available (more efficient)
+
+## Backward Compatibility
+
+- Synthesis agent includes `_summarize_interactions()` wrapper for backward compatibility
+- All changes are additive (enhancements) rather than breaking changes
+- Fallback logic handles missing or incomplete context gracefully
+
+## Files Modified
+
+1. `src/agents/intent_agent.py` - Fixed context access in `_build_chain_of_thought_prompt()`
+2. `src/agents/synthesis_agent.py` - Fixed `_build_context_section()` and related methods
+3. `src/agents/safety_agent.py` - Enhanced `_build_safety_prompt()` with context
+4. `src/agents/skills_identification_agent.py` - Enhanced `_build_market_analysis_prompt()` with context
+
+## Verification
+
+✅ No linting errors  
+✅ All agents use correct context keys  
+✅ Backward compatibility maintained  
+✅ Graceful handling of empty context  
+✅ Consistent implementation pattern across all agents
+

src/agents/intent_agent.py

@@ -101,16 +101,47 @@ class IntentRecognitionAgent:
     def _build_chain_of_thought_prompt(self, user_input: str, context: Dict[str, Any]) -> str:
         """Build Chain of Thought prompt for intent recognition"""
         
+        # Extract context information from Context Manager structure
+        context_info = ""
+        if context:
+            # Use combined_context if available (pre-formatted by Context Manager)
+            combined_context = context.get('combined_context', '')
+            if combined_context:
+                # Use the pre-formatted context from Context Manager
+                context_info = f"\n\nAvailable Context:\n{combined_context[:1000]}..."  # Truncate if too long
+            else:
+                # Fallback: Build from interaction_contexts if combined_context not available
+                interaction_contexts = context.get('interaction_contexts', [])
+                user_context = context.get('user_context', '')
+                
+                context_parts = []
+                if user_context:
+                    context_parts.append(f"User Context: {user_context[:300]}...")
+                
+                if interaction_contexts:
+                    # Show last 2 interaction summaries for context
+                    recent_contexts = interaction_contexts[-2:]
+                    context_parts.append("Recent Interactions:")
+                    for idx, ic in enumerate(recent_contexts, 1):
+                        summary = ic.get('summary', '')
+                        if summary:
+                            context_parts.append(f"  {idx}. {summary}")
+                
+                if context_parts:
+                    context_info = "\n\nAvailable Context:\n" + "\n".join(context_parts)
+        
+        if not context_info:
+            context_info = "\n\nAvailable Context: No previous context available (first interaction in session)."
+        
         return f"""
         Analyze the user's intent step by step:
 
         User Input: "{user_input}"
-        
-        Available Context: {context.get('conversation_history', [])[-2:] if context else []}
+        {context_info}
         
         Step 1: Identify key entities, actions, and questions in the input
         Step 2: Map to intent categories: {', '.join(self.intent_categories)}
-        Step 3: Consider the conversation flow and user's likely goals
+        Step 3: Consider the conversation flow and user's likely goals (if context available)
         Step 4: Assign confidence scores (0.0-1.0) for each relevant intent
         Step 5: Provide reasoning for the classification
         

src/agents/safety_agent.py

@@ -149,12 +149,28 @@ class SafetyCheckAgent:
         }
     
     def _build_safety_prompt(self, response: str, context: Dict[str, Any]) -> str:
-        """Build prompt for safety analysis"""
+        """Build prompt for safety analysis with context awareness"""
+        
+        # Extract relevant context information for safety analysis
+        context_info = ""
+        if context:
+            # Get user context if available (might indicate user's background/preferences)
+            user_context = context.get('user_context', '')
+            if user_context:
+                context_info = f"\n\nUser Context (for safety context): {user_context[:200]}..."
+            
+            # Optionally include recent interaction context to understand conversation flow
+            interaction_contexts = context.get('interaction_contexts', [])
+            if interaction_contexts:
+                recent_context = interaction_contexts[-1].get('summary', '') if interaction_contexts else ''
+                if recent_context:
+                    context_info += f"\nRecent conversation: {recent_context[:200]}..."
         
         return f"""
         Analyze the following text for safety concerns and provide warnings if needed:
         
         Text to analyze: "{response[:500]}..."  # Truncate for large responses
+        {context_info}
         
         Analyze for:
         1. Toxicity or harmful language
@@ -162,6 +178,8 @@ class SafetyCheckAgent:
         3. Privacy or security concerns
         4. Controversial or sensitive topics
         
+        Consider the conversation context when assessing whether content is appropriate in this specific conversational context.
+        
         Provide analysis in JSON format with:
         - safety_scores for each category (0-1 scale)
         - detected_issues list

src/agents/skills_identification_agent.py

@@ -158,8 +158,8 @@ class SkillsIdentificationAgent:
         
         if self.llm_router:
             try:
-                # Build market analysis prompt
-                market_prompt = self._build_market_analysis_prompt(user_input)
+                # Build market analysis prompt with context
+                market_prompt = self._build_market_analysis_prompt(user_input, context)
                 
                 logger.info(f"{self.agent_id} calling reasoning_primary for market analysis")
                 llm_response = await self.llm_router.route_inference(
@@ -211,8 +211,8 @@ class SkillsIdentificationAgent:
         # Fallback to rule-based classification
         return self._rule_based_skill_classification(user_input)
     
-    def _build_market_analysis_prompt(self, user_input: str) -> str:
-        """Build prompt for market analysis using reasoning_primary model"""
+    def _build_market_analysis_prompt(self, user_input: str, context: Dict[str, Any] = None) -> str:
+        """Build prompt for market analysis using reasoning_primary model with optional context"""
         
         market_data = "\n".join([
             f"- {category}: {data['market_share']}% market share, {data['growth_rate']}% growth rate"
@@ -224,9 +224,29 @@ class SkillsIdentificationAgent:
             for category, data in self.market_categories.items()
         ])
         
+        # Add context information if available
+        context_info = ""
+        if context:
+            user_context = context.get('user_context', '')
+            interaction_contexts = context.get('interaction_contexts', [])
+            
+            if user_context:
+                context_info = f"\n\nUser Context (persona summary): {user_context[:300]}..."
+            
+            if interaction_contexts:
+                # Include recent interaction context to understand topic continuity
+                recent_contexts = interaction_contexts[-2:]  # Last 2 interactions
+                if recent_contexts:
+                    context_info += "\n\nRecent conversation context:"
+                    for idx, ic in enumerate(recent_contexts, 1):
+                        summary = ic.get('summary', '')
+                        if summary:
+                            context_info += f"\n  {idx}. {summary}"
+        
         return f"""Analyze the following user input and identify the most relevant industry categories and specialized skills based on current market data.
 
 User Input: "{user_input}"
+{context_info}
 
 Current Market Distribution:
 {market_data}
@@ -235,10 +255,11 @@ Specialized Skills by Category (top 3 per category):
 {specialized_skills}
 
 Task: 
-1. Identify which industry categories are most relevant to the user's input
+1. Identify which industry categories are most relevant to the user's input (consider conversation context if provided)
 2. Select 1-3 specialized skills from each relevant category that best match the user's needs
 3. Provide market share percentages and growth rates for identified categories
 4. Explain your reasoning for each selection
+5. If conversation context is available, consider how previous topics might inform the skill identification
 
 Respond in JSON format:
 {{

src/agents/synthesis_agent.py

@@ -296,7 +296,8 @@ class EnhancedSynthesisAgent:
         
         logger.info(f"{self.agent_id} synthesizing {len(agent_outputs)} agent outputs")
         if context:
-            logger.info(f"{self.agent_id} context has {len(context.get('interactions', []))} interactions")
+            interaction_count = len(context.get('interaction_contexts', [])) if context else 0
+            logger.info(f"{self.agent_id} context has {interaction_count} interaction contexts")
             
         # STEP 1: Extract skill probabilities from skills_result
         skill_probabilities = self._extract_skill_probabilities(skills_result)
@@ -354,7 +355,8 @@ class EnhancedSynthesisAgent:
                 "synthesis_quality_metrics": self._calculate_quality_metrics({"final_response": clean_response}),
                 "synthesis_metadata": {
                     "agent_outputs_count": len(agent_outputs),
-                    "context_interactions": len(context.get('interactions', [])) if context else 0,
+                    "context_interactions": len(context.get('interaction_contexts', [])) if context else 0,
+                    "user_context_available": bool(context.get('user_context', '')) if context else False,
                     "expert_enhanced": True,
                     "processing_timestamp": datetime.now().isoformat()
                 }
@@ -527,70 +529,112 @@ Response:"""
         return prompt
     
     def _build_context_section(self, context: Dict[str, Any]) -> str:
-        """Build context section with summarization for long conversations"""
+        """Build context section with summarization for long conversations
+        
+        Uses Context Manager structure:
+        - combined_context: Pre-formatted context string (preferred)
+        - interaction_contexts: List of interaction summaries with 'summary' and 'timestamp'
+        - user_context: User persona summary string
+        """
         if not context:
             return ""
         
-        interactions = context.get('interactions', [])
-        
-        if len(interactions) <= 8:
-            # Show all interactions for short conversations
-            context_section = "\n\nPrevious conversation:\n"
-            for i, interaction in enumerate(interactions, 1):
-                context_section += f"Q{i}: {interaction.get('user_input', '')}\n"
-                if interaction.get('assistant_response') or interaction.get('response'):
-                    context_section += f"A{i}: {interaction.get('assistant_response') or interaction.get('response', '')}\n\n"
-        else:
-            # Summarize older interactions, show recent ones in full
-            recent_interactions = interactions[-8:]  # Last 8 interactions
-            older_interactions = interactions[:-8]   # Everything before last 8
-            
-            # Create summary of older interactions
-            summary = self._summarize_interactions(older_interactions)
-            
-            context_section = f"\n\nConversation Summary (earlier context):\n{summary}\n\nRecent conversation details:\n"
-            
-            for i, interaction in enumerate(recent_interactions, 1):
-                context_section += f"Q{i}: {interaction.get('user_input', '')}\n"
-                if interaction.get('assistant_response') or interaction.get('response'):
-                    context_section += f"A{i}: {interaction.get('assistant_response') or interaction.get('response', '')}\n\n"
+        # Prefer combined_context if available (pre-formatted by Context Manager)
+        combined_context = context.get('combined_context', '')
+        if combined_context:
+            # Use the pre-formatted context from Context Manager
+            # It already includes User Context and Interaction Contexts formatted
+            return f"\n\nConversation Context:\n{combined_context}"
+        
+        # Fallback: Build from individual components if combined_context not available
+        interaction_contexts = context.get('interaction_contexts', [])
+        user_context = context.get('user_context', '')
+        
+        context_section = ""
+        
+        # Add user context if available
+        if user_context:
+            context_section += f"\n\nUser Context (Persona Summary):\n{user_context[:500]}...\n"
+        
+        # Add interaction contexts
+        if interaction_contexts:
+            if len(interaction_contexts) <= 8:
+                # Show all interaction summaries for short conversations
+                context_section += "\n\nPrevious Conversation Summary:\n"
+                for i, ic in enumerate(interaction_contexts, 1):
+                    summary = ic.get('summary', '')
+                    if summary:
+                        context_section += f"  {i}. {summary}\n"
+            else:
+                # Summarize older interactions, show recent ones
+                recent_contexts = interaction_contexts[-8:]  # Last 8 interactions
+                older_contexts = interaction_contexts[:-8]   # Everything before last 8
+                
+                # Create summary of older interactions
+                summary = self._summarize_interaction_contexts(older_contexts)
+                
+                context_section += f"\n\nConversation Summary (earlier context):\n{summary}\n\nRecent Conversation:\n"
+                
+                for i, ic in enumerate(recent_contexts, 1):
+                    summary_text = ic.get('summary', '')
+                    if summary_text:
+                        context_section += f"  {i}. {summary_text}\n"
         
         return context_section
     
-    def _summarize_interactions(self, interactions: List[Dict[str, Any]]) -> str:
-        """Summarize older interactions to preserve key context"""
-        if not interactions:
+    def _summarize_interaction_contexts(self, interaction_contexts: List[Dict[str, Any]]) -> str:
+        """Summarize older interaction contexts to preserve key context
+        
+        Uses Context Manager structure where interaction_contexts contains:
+        - summary: 50-token interaction summary string
+        - timestamp: Interaction timestamp
+        """
+        if not interaction_contexts:
             return "No prior context."
         
-        # Extract key topics and themes
+        # Extract key topics and themes from summaries
         topics = []
         key_points = []
         
-        for interaction in interactions:
-            user_input = interaction.get('user_input', '')
-            assistant_response = interaction.get('assistant_response') or interaction.get('response', '')
+        for ic in interaction_contexts:
+            summary = ic.get('summary', '')
             
-            # Extract topics (simple keyword extraction)
-            if user_input:
-                # Simple topic extraction - could be enhanced with NLP
-                user_words = user_input.lower().split()
-                key_terms = [word for word in user_words if len(word) > 4][:3]
+            if summary:
+                # Extract topics from summary (simple keyword extraction)
+                # Summaries are already condensed, so extract meaningful terms
+                words = summary.lower().split()
+                key_terms = [word for word in words if len(word) > 4][:3]
                 topics.extend(key_terms)
-            
-            # Extract key points from responses (first sentence)
-            if assistant_response:
-                first_sentence = assistant_response.split('.')[0][:100]
-                if first_sentence:
-                    key_points.append(first_sentence + "...")
+                
+                # Use summary as key point (already a summary)
+                key_points.append(summary[:150])
         
         # Build summary
         unique_topics = list(set(topics))[:5]  # Top 5 unique topics
-        recent_points = key_points[-3:]  # Last 3 key points
+        recent_points = key_points[-5:]  # Last 5 key points
         
-        summary = f"Topics discussed: {', '.join(unique_topics)}\n"
-        summary += f"Key points: {' '.join(recent_points)}"
+        summary_text = f"Topics discussed: {', '.join(unique_topics) if unique_topics else 'General discussion'}\n"
+        summary_text += f"Key points: {' | '.join(recent_points) if recent_points else 'No specific points'}"
         
-        return summary
+        return summary_text
+    
+    def _summarize_interactions(self, interactions: List[Dict[str, Any]]) -> str:
+        """Legacy method for backward compatibility - delegates to _summarize_interaction_contexts"""
+        # Convert old format to new format if needed
+        if interactions and 'summary' in interactions[0]:
+            # Already in new format
+            return self._summarize_interaction_contexts(interactions)
+        else:
+            # Old format - convert
+            interaction_contexts = []
+            for interaction in interactions:
+                user_input = interaction.get('user_input', '')
+                assistant_response = interaction.get('assistant_response') or interaction.get('response', '')
+                # Create a simple summary
+                summary = f"User asked: {user_input[:100]}..." if user_input else ""
+                if summary:
+                    interaction_contexts.append({'summary': summary})
+            return self._summarize_interaction_contexts(interaction_contexts)
     
     def _extract_intent_info(self, agent_outputs: List[Dict[str, Any]]) -> Dict[str, Any]:
         """Extract intent information from agent outputs"""