Experience Layer (L4): Cognitive Experience Engine

Foundation + Cognitive Extension Architecture: L4 provides real-time experience capture with session management (v0.1 ready), plus a comprehensive cognitive processing engine specification for advanced pattern recognition and learning (v0.3+ roadmap).

Experience Layer captures what actually happened (user/agent actions, tool calls, outcomes), compresses raw events into concise, privacy-safe summaries (Experience Units), and retrieves relevant units in milliseconds to enrich context for current requests.

Goals

• Fast retrieval for current requests: p95 < 8 ms, p50 ~ 3 ms (within overall orchestration budget)
• High signal with minimal tokens: short, focused summaries with links to canonical entities (L1)
• Strict privacy: allow/redact/block enforced at ingest and read paths; no PII leakage
• Predictable degradation: when low on time/tokens, return fewer/cheaper units or minimal metadata

How it works (simple)

1. Ingest: append-only ExperienceEvent records each notable interaction (who/what/when/outcome) with privacy flags and references to L1 entities/documents.
2. Summarize: background process compresses sets of events into ExperienceUnit (1–3 sentences, quality score, entity links). Redaction happens before indexing if required.
3. Index: hybrid index (vector + sparse + filters) over ExperienceUnit enables fast and precise retrieval.
4. Retrieve: given request intent + budgets + privacy, L4 returns top-K ExperienceUnit with scores and short “reason”.

Interfaces and Integration

• Write (ingest): POST /api/v1/experience with ExperienceEvent.v0; returns { id, stored: true }
• Read (retrieval): via existing render_request.v0; planner applies budgets.experience_top_k/time_ms; reply embeds slices.experience[] in render_context_reply.v0
• L1 linkage: ExperienceUnit carries entity refs from L1; retrieval can filter by entities from current request or L1 candidates
• KV policy: pin > compress > evict; pin small “golden” units; compress long tail; evict stale/low-signal

Privacy and Budgets

• Modes: allow | redact | block
  • redact: PII is removed before indexing
  • block: read path returns only IDs/metadata; no raw text
• Budgets: tokens_max and time_ms strictly respected; degradation path reduces top_k, simplifies scoring, and/or returns minimal results

Metrics and SLO

• Online retrieval latency: p95 < 8 ms, p50 ~ 3 ms
• Quality: Recall@K, Coverage over entities, Novelty vs Redundancy
• Pipeline: ingest p50/p95, summarize p95, index latency, compression ratio
• Privacy: redact_rate; leak_rate = 0 (strict)
• Observability: request_id, user_id/agent_id, counts (ingested, units, pinned, evicted)

🏗️ Architecture Overview (Foundation + Extension)

Current Foundation (v0.1) ✅

Future Cognitive Engine (v0.3+) 🔮

Comprehensive Testing & Validation Framework

Foundation Component Testing

Experience Event Processing:

describe('L4 Experience Layer Foundation Tests', () => {
  describe('Event Ingestion & Processing', () => {
    test('processes user interaction events', async () => {
      const interaction_event = {
        type: 'user_query',
        user_id: 'user_123',
        query: 'implement authentication with JWT',
        context: { project_id: 'proj_456', session_id: 'sess_789' },
        outcome: { success: true, tokens_used: 1500, quality_score: 0.85 },
        timestamp: new Date().toISOString()
      };
      
      const result = await l4.ingestEvent(interaction_event);
      
      expect(result.id).toBeDefined();
      expect(result.stored).toBe(true);
      expect(result.privacy_applied).toBe(true);
    });
    
    test('creates experience units from event clusters', async () => {
      const events = [
        { type: 'user_query', query: 'JWT authentication setup' },
        { type: 'tool_call', tool: 'code_search', query: 'JWT libraries' },
        { type: 'code_generation', language: 'typescript', success: true },
        { type: 'user_feedback', rating: 5, comment: 'exactly what I needed' }
      ];
      
      const experience_unit = await l4.createExperienceUnit(events);
      
      expect(experience_unit.summary).toBeDefined();
      expect(experience_unit.summary.length).toBeLessThan(200); // Concise
      expect(experience_unit.entity_links.length).toBeGreaterThan(0);
      expect(experience_unit.quality_score).toBeGreaterThan(0.7);
    });
  });
  
  describe('Privacy & Security', () => {
    test('redacts PII from experience events', async () => {
      const event_with_pii = {
        type: 'user_query',
        query: 'My email john.doe@company.com needs JWT auth',
        user_metadata: { name: 'John Doe', role: 'developer' }
      };
      
      const processed = await l4.processWithPrivacy(event_with_pii, 'redact');
      
      expect(processed.query).not.toContain('john.doe@company.com');
      expect(processed.query).toContain('[EMAIL_REDACTED]');
      expect(processed.user_metadata.name).toBe('[NAME_REDACTED]');
    });
    
    test('blocks sensitive events in block mode', async () => {
      const sensitive_event = {
        type: 'code_generation',
        code: 'const API_KEY = "sk-secretkey123"',
        privacy_level: 'sensitive'
      };
      
      const result = await l4.processWithPrivacy(sensitive_event, 'block');
      
      expect(result.blocked).toBe(true);
      expect(result.metadata_only).toBe(true);
      expect(result.code).toBeUndefined();
    });
  });
  
  describe('Retrieval Performance', () => {
    test('meets retrieval latency SLA', async () => {
      const query = {
        intent: 'implement database connection pooling',
        entities: ['database', 'connection', 'pool'],
        top_k: 5,
        time_ms: 8
      };
      
      const start = Date.now();
      const results = await l4.retrieveExperienceUnits(query);
      const latency = Date.now() - start;
      
      expect(latency).toBeLessThan(8); // p95 < 8ms SLA
      expect(results.units.length).toBeLessThanOrEqual(5);
      expect(results.stats.retrieval_time_ms).toBeLessThan(8);
    });
  });
});

Load Testing Framework

Concurrent Experience Processing:

describe('L4 Production Load Tests', () => {
  test('handles concurrent experience ingestion', async () => {
    const concurrent_events = Array.from({ length: 200 }, (_, i) => ({
      type: 'user_interaction',
      user_id: `user_${i}`,
      session_id: `session_${Math.floor(i / 10)}`,
      query: `test query ${i}`,
      outcome: { success: true, quality_score: 0.8 }
    }));
    
    const start_time = Date.now();
    const results = await Promise.allSettled(
      concurrent_events.map(event => l4.ingestEvent(event))
    );
    const total_time = Date.now() - start_time;
    
    const successful = results.filter(r => r.status === 'fulfilled');
    const success_rate = successful.length / results.length;
    
    expect(success_rate).toBeGreaterThan(0.95); // 95%+ success rate
    expect(total_time).toBeLessThan(10000); // 200 events in < 10 seconds
  });
});

Production Operations Framework

Experience Layer Monitoring

Real-time Metrics:

experience_layer_monitoring:
  metrics:
    # Event processing metrics
    - name: l4_event_ingestion_rate
      type: gauge
      labels: [event_type, user_segment]
      
    - name: l4_experience_unit_creation_duration
      type: histogram
      buckets: [0.1, 0.5, 1.0, 2.0, 5.0]
      labels: [complexity_level]
      
    - name: l4_retrieval_latency_seconds
      type: histogram
      buckets: [0.001, 0.003, 0.005, 0.008, 0.015]
      labels: [query_complexity]
      
    # Privacy and quality metrics
    - name: l4_privacy_redaction_rate
      type: gauge
      labels: [redaction_type]
      
    - name: l4_experience_quality_score
      type: histogram
      buckets: [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
      
  alerts:
    - name: L4HighIngestionLatency
      condition: l4_event_ingestion_rate < 100
      severity: warning
      duration: 2m
      
    - name: L4RetrievalSLAViolation
      condition: l4_retrieval_latency_seconds{quantile="0.95"} > 0.008
      severity: critical
      duration: 1m

Contracts (v0.1)

Inputs

• ExperienceEvent.v0:
• SummarizationJob.v0:

Processing

• Ingest validates schema and privacy; pipeline batches and schedules summarization; indexer updates hybrid index

Outputs

• ExperienceUnit.v0:
• RetrievalReply.v0: { units[], stats: { retrieval_time_ms, top_k, filters } }

SLO / Targets

• Ingest p95 < 20ms; Summarize p95 < 200ms per cluster; Retrieval p95 < 8ms

Edge Cases

• Block mode events → metadata-only stored; redaction failures → quarantine queue

Resilience & Observability

Resilience

• Backpressure on ingest; dead-letter queue for malformed/blocked events
• Circuit breakers around summarizer and indexer; replay-safe jobs

Metrics

• l4_event_ingestion_rate, l4_experience_unit_creation_duration, l4_retrieval_latency_seconds
• l4_privacy_redaction_rate, l4_experience_quality_score

Tracing

• request_id/session_id on ingest and retrieval spans; include counts and privacy decisions

Privacy & Security

Controls

• privacy_mode allow|redact|block at both write/read paths; PII detectors prior to indexing
• Storage class segregation for sensitive units; encryption at rest + in transit

Access

• Role-scoped reads; metadata-only for blocked sessions

Deployment Architecture

Experience Processing Pipeline:

l4_deployment:
  services:
    event_processor:
      image: mnemoverse/l4-processor:latest
      replicas: 3
      resources:
        requests: { cpu: 200m, memory: 512Mi }
        limits: { cpu: 1, memory: 2Gi }
        
    experience_indexer:
      image: mnemoverse/l4-indexer:latest
      replicas: 2
      resources:
        requests: { cpu: 500m, memory: 1Gi }
        
    retrieval_engine:
      image: mnemoverse/l4-retrieval:latest
      replicas: 5
      resources:
        requests: { cpu: 300m, memory: 768Mi }
        
  databases:
    events_store:
      type: postgresql
      partitioning: by_date
      retention: 90_days
      
    vector_index:
      type: qdrant
      collection: experience_units
      vector_size: 384

🔮 Future: Cognitive Engine Specification

Note: The Cognitive Engine represents the advanced intelligence layer of L4, planned for v0.3+. This specification defines the architecture for pattern recognition, intelligent summarization, and meta-learning capabilities.

Cognitive Architecture Framework

1. Pattern Recognition Engine

interface PatternRecognitionEngine {
  // Identify recurring patterns in user behavior
  detectBehavioralPatterns(events: ExperienceEvent[]): Pattern[];
  
  // Recognize problem-solving strategies
  analyzeSolutionPatterns(solutions: SolutionEvent[]): StrategyPattern[];
  
  // Identify knowledge gaps and learning opportunities
  identifyLearningPatterns(interactions: UserInteraction[]): LearningOpportunity[];
}

interface Pattern {
  id: string;
  type: 'behavioral' | 'solution' | 'learning' | 'communication';
  frequency: number;
  confidence: number;
  triggers: PatternTrigger[];
  outcomes: PatternOutcome[];
}

2. Intelligent Summarization System

interface IntelligentSummarizer {
  // Context-aware summarization with different granularities
  summarizeExperience(
    events: ExperienceEvent[], 
    context: SummarizationContext
  ): ExperienceSummary;
  
  // Adaptive summarization based on user expertise level
  adaptSummaryToUser(
    summary: ExperienceSummary,
    user_profile: UserExpertiseProfile
  ): AdaptedSummary;
  
  // Cross-session pattern summarization
  synthesizeLearningJourney(
    sessions: UserSession[]
  ): LearningJourneySummary;
}

3. Cognitive State Machine

interface CognitiveStateMachine {
  // Track cognitive state throughout interactions
  updateCognitiveState(
    current_state: CognitiveState,
    new_event: ExperienceEvent
  ): CognitiveState;
  
  // Predict next likely actions based on cognitive state
  predictNextActions(state: CognitiveState): ActionPrediction[];
  
  // Adapt system behavior based on cognitive load
  adaptToCognitiveLoad(
    load: CognitiveLoadMetrics
  ): AdaptationStrategy;
}

4. Meta-learning Framework

interface MetaLearningFramework {
  // Learn about learning: how users acquire new knowledge
  analyzeLearningEffectiveness(
    learning_sessions: LearningSession[]
  ): LearningInsights;
  
  // Optimize experience delivery based on learning patterns
  optimizeExperienceDelivery(
    user_profile: UserProfile,
    learning_insights: LearningInsights
  ): OptimizationStrategy;
  
  // Continuous improvement of cognitive models
  updateCognitiveModels(
    feedback: UserFeedback[],
    performance_metrics: PerformanceMetrics
  ): ModelUpdateResult;
}

Cognitive Engine Implementation Roadmap

Phase 1: Foundation (Current)

• ✅ Experience event capture and processing
• ✅ Basic summarization and indexing
• ✅ Privacy-preserving retrieval
• ✅ Session management and context tracking

Phase 2: Pattern Recognition (v0.2)

• 🔮 Behavioral pattern detection
• 🔮 Solution strategy recognition
• 🔮 Learning opportunity identification
• 🔮 Basic cognitive state tracking

Phase 3: Intelligent Processing (v0.3)

• 🔮 Context-aware intelligent summarization
• 🔮 Advanced cognitive state machine
• 🔮 Predictive action recommendation
• 🔮 Adaptive user experience optimization

Phase 4: Meta-learning (v0.4)

• 🔮 Learning effectiveness analysis
• 🔮 Continuous model improvement
• 🔮 Cross-user pattern synthesis
• 🔮 Cognitive model personalization

Implementation Roadmap

Phase 1: Foundation Completion (v0.1) - 3 weeks

• [ ] Complete event ingestion pipeline
• [ ] Implement privacy engine with redaction modes
• [ ] Build hybrid indexing for experience units
• [ ] Optimize retrieval performance (p95 < 8ms)

Phase 2: Enhanced Foundation (v0.2) - 4 weeks

• [ ] Advanced session management
• [ ] Quality scoring improvements
• [ ] Cross-session pattern detection (basic)
• [ ] Enhanced monitoring and observability

Phase 3: Cognitive Engine MVP (v0.3) - 8 weeks

• [ ] Pattern recognition engine implementation
• [ ] Intelligent summarization system
• [ ] Basic cognitive state machine
• [ ] Meta-learning framework foundation

Success Criteria

Foundation (v0.1):

• Experience retrieval latency p95 < 8ms
• Privacy redaction accuracy > 99%
• Event processing throughput > 1000 events/second
• Experience unit quality score > 0.8

Cognitive Engine (v0.3):

• Pattern recognition accuracy > 85%
• Summarization quality score > 0.9
• User satisfaction with adaptive experience > 4.2/5.0
• Learning effectiveness improvement > 30%

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Related Documentation

Current Implementation:

• Pipeline Architecture — Event processing and unit creation
• Privacy Framework — PII handling and redaction modes
• Retrieval System — Fast experience unit retrieval
• Metrics & SLOs — Performance monitoring and targets

Future Extensions:

• 🔮 Cognitive Engine Specification — Advanced pattern recognition and learning
• 🔮 Search Alias — Fast hints channel powering retrieval
• �️ Roadmap — Phased delivery including cognitive extensions

System Integration:

• Contracts & APIs — Interface specifications
• L1 Integration — Knowledge base linkage
• L2 Integration — Project context awareness
• Overall Architecture — Complete system overview

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Status: Foundation ready (v0.1) → Cognitive engine specified (v0.3+) → Production target (v0.2)

Next Priority: Complete foundation implementation with privacy engine, hybrid indexing, and performance optimization to achieve p95 < 8ms retrieval SLA.

Experience Layer (L4)

Task-level trails: task → nodes used → result → validation. Search across solved paths.