Persistent Context Foundation Research & Architecture Planning

Purpose: Comprehensive analysis of existing patterns and requirements for building persistent context infrastructure in Piper Morgan.

Investigation Date: August 20, 2025 - 3:07 PM Pacific Investigator: Cursor Agent Methodology: Excellence Flywheel - Systematic Verification First

🔍 EXISTING PERSISTENCE PATTERNS ANALYSIS

Current Session Management Infrastructure

✅ SessionManager (services/session/session_manager.py):

Database Context Storage Patterns

✅ Existing Context Fields:

File Context Storage

✅ File Storage Infrastructure:

User Preference Patterns

✅ Existing Preference Handling:

🏗️ INTEGRATION ANALYSIS

Domain Model Integration Points

✅ Ready for Extension:

Session Management Integration

✅ Current Architecture:

Database Integration Points

✅ Existing Infrastructure:

🎯 ARCHITECTURE REQUIREMENTS

Simple JSON Storage Approach Assessment

✅ Recommended Approach: Extend existing JSON context fields

Database Integration Considerations

✅ Current Database Support:

Future Extensibility Planning

✅ Scalable Architecture:

📋 IMPLEMENTATION CHECKLIST FOR CODE AGENT

Phase 1: Core Infrastructure (2 hours)

Phase 2: Context Persistence (1 hour)

Phase 3: Integration & Testing (1 hour)

🚧 POTENTIAL BLOCKERS & COMPLEXITIES

Technical Challenges

⚠️ JSON Schema Validation:

⚠️ Context Versioning:

⚠️ Performance on JSON Queries:

Architectural Decisions Needed

❓ Preference Scope:

❓ Context Inheritance Strategy:

❓ Conflict Resolution:

🎯 IMPLEMENTATION PATH MAPPING

Immediate Implementation (Code Agent - 4 hours)

  1. Hour 1-2: Core UserPreferenceManager and preference storage
  2. Hour 3: Session persistence and context inheritance
  3. Hour 4: Integration testing and API endpoints

Future Enhancements (Post-MVP)

🔧 TECHNICAL SPECIFICATIONS

UserPreferenceManager Interface

class UserPreferenceManager:
    async def get_preference(self, key: str, user_id: str = None,
                           session_id: str = None, default: Any = None) -> Any
    async def set_preference(self, key: str, value: Any, user_id: str = None,
                           session_id: str = None) -> bool
    async def get_all_preferences(self, user_id: str = None,
                                session_id: str = None) -> Dict[str, Any]
    async def merge_preferences(self, user_id: str, session_id: str) -> Dict[str, Any]
    async def clear_session_preferences(self, session_id: str) -> bool

Context Storage Schema

{
  "user_preferences": {
    "ui_theme": "dark",
    "notification_frequency": "daily",
    "default_file_types": ["md", "py", "json"]
  },
  "session_preferences": {
    "current_project": "piper-morgan",
    "preferred_agent": "cursor",
    "workflow_style": "agile"
  },
  "context_version": "1.0",
  "last_updated": "2025-08-20T15:07:00Z"
}

Database Extensions

-- Add preference-specific indexes if needed
CREATE INDEX idx_intent_context_preferences ON intents USING GIN (context);
CREATE INDEX idx_workflow_context_preferences ON workflows USING GIN (context);

-- Add preference validation constraints
ALTER TABLE intents ADD CONSTRAINT valid_context_json
  CHECK (jsonb_typeof(context) = 'object');

📊 SUCCESS METRICS

Functional Requirements

Performance Requirements

Quality Requirements

🚀 READY FOR IMPLEMENTATION

Status: ✅ RESEARCH COMPLETE - Code Agent can start implementation immediately Confidence: High - existing patterns provide solid foundation Risk Level: Low - leverages proven infrastructure Timeline: 4 hours achievable with clear implementation path

Next Steps: Code Agent should proceed with Phase 1 implementation using the UserPreferenceManager approach and existing JSON context fields.