CONTEXT_STRUCTURE_FIX_IMPLEMENTATION.md

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:

# 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:

# 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:

# 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:

# 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:

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

# 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