Noosphere Architecture (L1)

Focus: concrete engineering architecture — what exists, how it connects, and what data flows between components. No marketing.

┌──────────────────────────────────────────────┐
│                MCP Interface                 │
│            (JSON-RPC 2.0 Transport)          │
└─────────────────┬────────────────────────────┘
                  │
┌─────────────────▼────────────────────────────┐
│              Meta-Agent Core                 │
│  ┌────────────────────────────────────────┐  │
│  │         Pattern Cache Engine           │  │
│  │  • Query classification                │  │
│  │  • Success pattern storage             │  │
│  │  • Intelligent cache lookup            │  │
│  │  • Continuous learning algorithms      │  │
│  └────────────────────────────────────────┘  │
└─────────────────┬────────────────────────────┘
                  │
    ┌─────────────┼─────────────────┐
    │             │                 │
    ▼             ▼                 ▼
┌─────────┐  ┌─────────┐  ┌─────────────────┐
│ Vector  │  │ Graph   │  │   AI Agents     │
│ Agent   │  │ Agent   │  │  (Specialized)  │
│         │  │         │  │                 │
│ • Embed │  │ • Neo4j │  │ • Librarian     │
│ • Index │  │ • Cypher│  │ • Researcher    │
│ • Search│  │ • Paths │  │ • Validator     │
└─────────┘  └─────────┘  └─────────────────┘

Components

Meta‑Agent (Learning Router)

Router with feedback-driven learning. Core mechanisms:

1. Query Classification

# Conceptual algorithm
classify_query(query, context) → {
  type: "semantic|structural|contextual|hybrid",
  complexity: 0.0-1.0,
  domain: "code|docs|architecture|research",
  history_similarity: 0.0-1.0
}

2. Pattern Caching System

# Cache structure
pattern_cache = {
  "query_signature": {
    "method_sequence": ["vector", "graph", "ai_agent"],
    "parameters": {...},
    "success_rate": 0.89,
    "avg_latency": 234ms,
    "last_updated": timestamp,
    "usage_count": 42
  }
}

3. Intelligent Decision Engine — Cache Hit: instant response via verified method sequence — Cache Miss: invoke appropriate agents and learn from results — Adaptive Learning: update patterns based on quality signals

Specialized Agents

Vector Agent (Semantic Search)

• Embedding generation using state-of-the-art models
• FAISS/Weaviate integration for scalable similarity search
• Domain-specific embedding fine-tuning

Graph Agent (Structural Relationships)

• Neo4j/DGraph backend for knowledge representation
• Cypher query optimization for complex relationship traversal
• Graph neural networks for pattern recognition

AI Staff Agents (Contextual Intelligence)

• Librarian Agent: Information discovery and cataloging
• Researcher Agent: Deep analysis and synthesis
• Validator Agent: Quality assessment and fact-checking
• Navigator Agent: Complex investigation guidance

Communication Protocols

Agent-to-Agent Communication

Meta-Agent ←→ Specialized Agents
    │              │
    │              ├─ Request/Response (JSON)
    │              ├─ Status Updates (Event Stream)
    │              └─ Performance Metrics (Telemetry)
    │
    └─ Shared Memory Pool
       ├─ Pattern Cache
       ├─ Query History  
       └─ Performance Data

Error Handling & Fallbacks — Circuit breakers for failing agents; graceful degradation and exponential backoff retries

Self-Learning Mechanisms

Success Pattern Recognition

The system continuously learns from three sources of feedback:

1. Explicit User Feedback

• Thumbs up/down on search results
• Quality ratings (1-5 stars)
• Relevance scoring for returned content

2. Implicit Behavioral Signals

• Time spent viewing results
• Follow-up query patterns
• Click-through rates on suggested content

3. System Performance Metrics

• Response latency by method
• Resource utilization patterns
• Error rates and recovery times

Decision Caching Algorithms

Cache Key Generation

def generate_cache_key(query, context):
    features = {
        'query_embedding': embed(query)[:50],  # Dimensionality reduction
        'context_hash': hash(context),
        'user_profile': get_user_preferences(),
        'time_context': get_temporal_features()
    }
    return hash(json.dumps(features, sort_keys=True))

Cache Replacement Policy

• LFU + Recency: Least Frequently Used with time decay
• Success Rate Weighting: Higher success patterns persist longer
• Adaptive TTL: Time-to-live based on pattern stability

Pattern Evolution

def update_pattern(cache_key, execution_result):
    pattern = cache[cache_key]
    pattern.success_rate = weighted_average(
        pattern.success_rate, 
        execution_result.success,
        alpha=learning_rate
    )
    pattern.avg_latency = update_moving_average(
        pattern.avg_latency,
        execution_result.latency
    )
    if pattern.success_rate < threshold:
        mark_for_relearning(cache_key)

Continuous Optimization

— Metrics/dashboards, A/B tests, adaptive learning rates

Implementation Considerations

Technology Stack Dependencies

Core Infrastructure

• MCP Transport: JSON-RPC 2.0 over stdio/HTTP
• Meta-Agent Runtime: Python/Node.js with ML libraries
• Message Queue: Redis/RabbitMQ for agent communication
• Pattern Cache: Redis with persistence for pattern storage

Storage Systems

• Vector Database: Weaviate/Pinecone for embedding storage
• Graph Database: Neo4j for knowledge relationships
• Time Series DB: InfluxDB for performance metrics
• Document Store: MongoDB for unstructured data

Machine Learning Stack

• Classification: scikit-learn/XGBoost for query classification
• Embeddings: OpenAI/Cohere/local models (sentence-transformers)
• Reinforcement Learning: Ray RLlib for adaptive routing
• Feature Store: Feast for ML feature management

Scalability Architecture

Horizontal Scaling

Load Balancer
     │
┌────┴────┬────────┬────────┐
│ Meta-1  │ Meta-2 │ Meta-N │  (Meta-Agent Cluster)
└────┬────┴────┬───┴────┬───┘
     │         │        │
┌────▼───┐ ┌───▼───┐ ┌──▼────┐
│Vector-1│ │Graph-1│ │AI-Ag-1│  (Specialized Agent Pools)
│Vector-2│ │Graph-2│ │AI-Ag-2│
│Vector-N│ │Graph-N│ │AI-Ag-N│
└────────┘ └───────┘ └───────┘

Performance Characteristics (targets) — Cache Hit: <10ms; Cache Miss (vector/graph): 50–200ms; AI agent: 0.5–2s

Resource Requirements (guidance) — Dev: 8GB RAM; Small prod: 32GB RAM; GPU optional for embeddings

Deployment Strategies

Container Architecture

# Conceptual multi-container setup
services:
  meta-agent:
    image: noosphere/meta-agent:latest
    environment:
      - REDIS_URL=redis://cache:6379
      - NEO4J_URL=bolt://graph:7687
  
  vector-agent:
    image: noosphere/vector-agent:latest
    environment:
      - WEAVIATE_URL=http://weaviate:8080
  
  cache:
    image: redis:7-alpine
    volumes:
      - ./data/redis:/data
  
  graph:
    image: neo4j:5
    volumes:
      - ./data/neo4j:/data

Configuration Management

• Environment-specific configs for development/staging/production
• Feature flags for experimental routing algorithms
• A/B testing infrastructure for performance optimization
• Monitoring and alerting for system health

Version Control & Rollbacks (Git-native)

Goal: every data-changing operation in L1 is traceable, diffable, and reversible.

Core principles

• Immutable artifacts, mutable pointers: writes create new artifacts; the active pointer flips atomically after validation.
• Git as the source of truth: human-auditable history with signed commits and tags for releases/checkpoints.
• One command to revert: operators can move the system to any previous ref safely.

Tracked artifacts

• Knowledge Graph snapshots: periodic/export-on-change dumps (Cypher/CSV or JSON-LD) stored with content-addressed names; tagged by Git SHA and human tag (e.g., l1-graph@v2025-09-06).
• Vector index snapshots: read-only index bundles (e.g., FAISS/Weaviate export) published to object storage; manifest maps Git SHA → snapshot URI and schema/version.
• Document corpus/versioned chunks: deterministic chunking; each chunk carries content hash and corpus ref for reproducibility.
• Router configs and policies: routing thresholds, KV policy presets, feature flags; stored alongside code in the repo.

Write path and change-sets

• Any mutation (ingest/update/delete) produces a change-set: nodes/edges added/updated/removed, affected documents, and estimated impacts.
• Auto-commit: change-set serialized to repo (or a dedicated data-repo) with a signed commit message including request_id, author, scope, and safety checks.
• Review gates: risky ops (large fan-out, low confidence) require approval; enforced via branch protection/ACL.

Rollback mechanisms

• Soft rollback (instant): flip active pointers (graph snapshot, vector index, corpus) to a previous ref; new artifacts are kept for later analysis.
• Hard rollback (restore): rehydrate Neo4j/DGraph from snapshot export and reattach indexes from the manifest; validate, then promote.
• MCP admin ops (suggested):
  • /admin/version/current → { commit, tags, active: {graph, vector, corpus} }
  • /admin/version/checkout { ref } → switch active pointers with 2-phase validation
  • /admin/version/restore-index { ref } → force reattach vector index
  • /admin/version/status → readiness and consistency report

Consistency & safety

• Two-phase promotion: build new artifacts → validate (schema/consistency/latency) → flip pointer.
• Provenance in results: each returned item includes {commit, corpus_ref, snapshot_id} for end-to-end traceability.
• KV Policy interplay: pin prevents eviction across versions; evict allowed only when snapshot is superseded and not pinned.

Storage layout (example)

/l1-artifacts/
  graph/
    8f/8f12c.../graph.jsonld           # content-addressed
    tags/l1-graph@v2025-09-06 -> 8f12c...
  vector/
    3a/3a9b.../index.faiss
    3a/3a9b.../manifest.json            # dims, metric, schema ver
  corpus/
    54/54aa.../chunks.parquet
  manifests/
    8f12c.../release.json               # {graph, vector, corpus}

Edge cases

• Partial failure: do not flip pointers unless all artifacts validate; auto-roll back.
• Large artifacts: use rsync-in-place/object storage with integrity checks.
• Concurrency: serialize promotions via a single-leader lock; readers always see a consistent tuple of pointers.

Comprehensive Testing & Validation Framework

Component Testing Architecture

Meta-Agent Router Testing:

describe('Meta-Agent Router', () => {
  describe('Pattern Cache Engine', () => {
    test('learns from successful query patterns', async () => {
      const successful_pattern = {
        query: "React performance optimization",
        method: "hybrid",  
        success_metrics: { relevance: 0.9, user_satisfaction: 0.85 }
      };
      
      await patternCache.recordSuccess(successful_pattern);
      
      const similar_query = "React performance best practices";
      const recommendation = await patternCache.getMethodRecommendation(similar_query);
      
      expect(recommendation.method).toBe("hybrid");
      expect(recommendation.confidence).toBeGreaterThan(0.7);
    });
    
    test('adapts routing based on query complexity', async () => {
      const simple_query = "what is JavaScript";
      const complex_query = "implement distributed consensus with Byzantine fault tolerance";
      
      const simple_routing = await metaAgent.routeQuery(simple_query);
      const complex_routing = await metaAgent.routeQuery(complex_query);
      
      expect(simple_routing.method).toBe("vector"); // Simple vector search sufficient
      expect(complex_routing.method).toBe("agent");  // Requires AI agent research
      expect(complex_routing.agent_type).toBe("researcher");
    });
  });
  
  describe('Hybrid Search Orchestration', () => {
    test('combines vector and graph results effectively', async () => {
      const query = "authentication patterns OAuth2 JWT";
      
      const hybrid_results = await metaAgent.hybridSearch({
        query,
        vector_weight: 0.6,
        graph_weight: 0.4,
        max_results: 10
      });
      
      expect(hybrid_results.fragments.length).toBeLessThanOrEqual(10);
      expect(hybrid_results.source_breakdown.vector).toBeGreaterThan(0);
      expect(hybrid_results.source_breakdown.graph).toBeGreaterThan(0);
      expect(hybrid_results.diversity_score).toBeGreaterThan(0.6);
    });
  });
});

AI Agent Testing

Librarian Agent Validation:

describe('AI Librarian Agent', () => {
  test('conducts systematic research for complex queries', async () => {
    const research_query = "microservices vs monolithic architecture trade-offs";
    
    const research_result = await librarianAgent.conductResearch({
      query: research_query,
      depth: "comprehensive",
      include_counterarguments: true
    });
    
    expect(research_result.methodology).toBeDefined();
    expect(research_result.findings.length).toBeGreaterThan(3);
    expect(research_result.sources.length).toBeGreaterThan(5);
    expect(research_result.synthesis_summary).toBeDefined();
    
    // Should include both perspectives
    const content = research_result.findings.map(f => f.content).join(" ");
    expect(content).toMatch(/(advantage|benefit).*microservices/i);
    expect(content).toMatch(/(advantage|benefit).*monolithic/i);
  });
  
  test('validates information quality and sources', async () => {
    const validation_request = {
      content: "React 18 introduced concurrent features for better performance",
      sources: ["react.dev", "github.com/facebook/react"],
      domain: "frontend_development"
    };
    
    const validation_result = await librarianAgent.validateInformation(validation_request);
    
    expect(validation_result.accuracy_score).toBeGreaterThan(0.8);
    expect(validation_result.source_credibility).toBeGreaterThan(0.9);
    expect(validation_result.temporal_relevance).toBeGreaterThan(0.8);
    expect(validation_result.supporting_evidence).toBeDefined();
  });
});

Performance Testing

System Load Testing:

describe('Noosphere Performance Tests', () => {
  test('handles concurrent search requests', async () => {
    const concurrent_queries = Array.from({ length: 100 }, (_, i) => ({
      query: `test query ${i}`,
      method: "hybrid",
      user_id: `user-${i}`
    }));
    
    const start_time = Date.now();
    const results = await Promise.allSettled(
      concurrent_queries.map(q => noosphere.search(q))
    );
    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); // 100 queries in < 10 seconds
    
    // Individual query performance
    const response_times = successful.map(r => 
      (r as PromiseFulfilledResult<any>).value.processing_time_ms
    );
    const p95_latency = response_times.sort()[Math.floor(response_times.length * 0.95)];
    expect(p95_latency).toBeLessThan(800); // P95 < 800ms
  });
  
  test('maintains quality under load', async () => {
    const quality_queries = [
      "implement JWT authentication",
      "database indexing strategies",
      "React state management patterns",
      "distributed systems consistency"
    ];
    
    const quality_results = await Promise.all(
      quality_queries.map(async query => {
        const results = await noosphere.search({ query, method: "hybrid" });
        return {
          query,
          quality_score: results.quality_metrics.overall_score,
          relevance_score: results.quality_metrics.relevance_score
        };
      })
    );
    
    const avg_quality = quality_results.reduce((sum, r) => sum + r.quality_score, 0) / quality_results.length;
    const avg_relevance = quality_results.reduce((sum, r) => sum + r.relevance_score, 0) / quality_results.length;
    
    expect(avg_quality).toBeGreaterThan(0.75);
    expect(avg_relevance).toBeGreaterThan(0.8);
  });
});

Production Operations Framework

Knowledge Pipeline Monitoring

Ingestion & Quality Metrics:

noosphere_operations:
  data_pipeline:
    ingestion_monitoring:
      - name: noosphere_ingestion_rate
        type: gauge
        labels: [source_type, content_domain]
        
      - name: noosphere_content_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]
        labels: [source, validation_method]
        
      - name: noosphere_knowledge_freshness_hours
        type: gauge
        labels: [domain, source_type]
        
  search_performance:
    - name: noosphere_search_cache_hit_ratio
      type: gauge
      labels: [cache_type, query_complexity]
      
    - name: noosphere_agent_utilization
      type: gauge
      labels: [agent_type, complexity_level]
      
    - name: noosphere_hybrid_search_composition
      type: gauge
      labels: [vector_ratio, graph_ratio, agent_ratio]
      
  alerts:
    - name: NoosphereIngestionStalled
      condition: rate(noosphere_ingestion_rate[10m]) < 10
      severity: warning
      duration: 5m
      
    - name: NoosphereQualityDegradation
      condition: avg(noosphere_content_quality_score) < 0.6
      severity: critical
      duration: 2m
      
    - name: NoosphereAgentOverload
      condition: noosphere_agent_utilization > 0.9
      severity: warning
      duration: 1m

Auto-scaling & Resource Management

Dynamic Resource Allocation:

class NoosphereResourceManager {
  private scaling_policies = {
    vector_search: {
      scale_up_threshold: { cpu: 0.7, memory: 0.8, query_queue: 50 },
      scale_down_threshold: { cpu: 0.3, memory: 0.4, query_queue: 5 },
      min_replicas: 2,
      max_replicas: 20
    },
    graph_search: {
      scale_up_threshold: { cpu: 0.8, memory: 0.9, query_queue: 20 },
      scale_down_threshold: { cpu: 0.2, memory: 0.3, query_queue: 2 },
      min_replicas: 1,
      max_replicas: 10
    },
    ai_agents: {
      scale_up_threshold: { cpu: 0.6, utilization: 0.8, queue_depth: 10 },
      scale_down_threshold: { cpu: 0.2, utilization: 0.3, queue_depth: 1 },
      min_replicas: 1,
      max_replicas: 15
    }
  };
  
  async evaluateScalingNeeds(): Promise<ScalingDecision[]> {
    const current_metrics = await this.getSystemMetrics();
    const decisions: ScalingDecision[] = [];
    
    for (const [component, policy] of Object.entries(this.scaling_policies)) {
      const component_metrics = current_metrics[component];
      
      if (this.shouldScaleUp(component_metrics, policy.scale_up_threshold)) {
        decisions.push({
          component,
          action: 'scale_up',
          target_replicas: Math.min(
            component_metrics.current_replicas + this.calculateScaleUpDelta(component_metrics),
            policy.max_replicas
          ),
          reason: this.getScalingReason(component_metrics, 'up')
        });
      } else if (this.shouldScaleDown(component_metrics, policy.scale_down_threshold)) {
        decisions.push({
          component,
          action: 'scale_down',
          target_replicas: Math.max(
            component_metrics.current_replicas - 1,
            policy.min_replicas
          ),
          reason: this.getScalingReason(component_metrics, 'down')
        });
      }
    }
    
    return decisions;
  }
}

Security & Privacy Implementation

Enhanced Security Framework:

class NoosphereSecurityManager {
  private security_policies = {
    data_classification: {
      public: { encryption: false, access_logging: false },
      internal: { encryption: true, access_logging: true },
      sensitive: { encryption: true, access_logging: true, approval_required: true },
      pii: { encryption: true, access_logging: true, anonymization: true }
    },
    query_validation: {
      max_query_length: 10000,
      blocked_patterns: [/\bDROP\b/i, /\bDELETE\b/i, /\bEXEC\b/i],
      rate_limits: { per_user: 1000, per_minute: 10000 },
      content_filters: ['explicit_content', 'harmful_instructions']
    }
  };
  
  async validateKnowledgeQuery(
    query: KnowledgeQuery,
    user_context: UserContext
  ): Promise<SecurityValidationResult> {
    
    const validations = await Promise.all([
      this.validateQuerySyntax(query),
      this.checkContentFilters(query),
      this.validateUserPermissions(user_context),
      this.checkRateLimits(user_context.user_id),
      this.scanForSecurityThreats(query)
    ]);
    
    const security_violations = validations.filter(v => !v.passed);
    
    if (security_violations.length > 0) {
      await this.logSecurityIncident({
        type: 'knowledge_query_violation',
        user_id: user_context.user_id,
        query_hash: this.hashQuery(query),
        violations: security_violations,
        timestamp: new Date().toISOString()
      });
      
      return {
        approved: false,
        violations: security_violations,
        sanitized_query: this.sanitizeQuery(query, security_violations)
      };
    }
    
    return { approved: true };
  }
  
  private async anonymizeSensitiveContent(content: string): Promise<string> {
    // PII detection and anonymization
    const pii_patterns = {
      email: /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/g,
      phone: /\b\d{3}-\d{3}-\d{4}\b/g,
      ssn: /\b\d{3}-\d{2}-\d{4}\b/g,
      credit_card: /\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b/g
    };
    
    let anonymized = content;
    for (const [type, pattern] of Object.entries(pii_patterns)) {
      anonymized = anonymized.replace(pattern, `[${type.toUpperCase()}_REDACTED]`);
    }
    
    return anonymized;
  }
}

Deployment & Infrastructure

Production Deployment Configuration:

# Noosphere L1 production deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: noosphere-meta-agent
  namespace: mnemoverse-l1
spec:
  replicas: 3
  selector:
    matchLabels:
      app: noosphere-meta-agent
  template:
    metadata:
      labels:
        app: noosphere-meta-agent
    spec:
      containers:
      - name: meta-agent
        image: mnemoverse/noosphere-meta-agent:v0.1.0
        ports:
        - containerPort: 8080
        env:
        - name: PATTERN_CACHE_SIZE
          value: "10000"
        - name: VECTOR_INDEX_PATH
          value: "/data/vector-index"
        - name: GRAPH_DB_URL
          value: "neo4j://neo4j-service:7687"
        resources:
          requests:
            cpu: 1
            memory: 2Gi
          limits:
            cpu: 4
            memory: 8Gi
        volumeMounts:
        - name: vector-data
          mountPath: /data
      volumes:
      - name: vector-data
        persistentVolumeClaim:
          claimName: noosphere-vector-pvc
---
apiVersion: v1
kind: Service
metadata:
  name: noosphere-service
spec:
  selector:
    app: noosphere-meta-agent
  ports:
  - port: 80
    targetPort: 8080
  type: ClusterIP

Implementation Roadmap

Phase 1: Core Infrastructure (v0.1) - 5 weeks

Week 1-2: Foundation Components

• [ ] MCP protocol implementation and client interface
• [ ] Vector search engine with embedding generation
• [ ] Neo4j knowledge graph setup and basic schemas
• [ ] Content ingestion pipeline with quality validation

Week 3-4: Intelligence Layer

• [ ] Meta-agent router with pattern cache engine
• [ ] AI Librarian agent with research capabilities
• [ ] Hybrid search orchestration (vector + graph + agent)
• [ ] Feedback collection and continuous learning system

Week 5: Integration & Testing

• [ ] End-to-end search workflow integration
• [ ] Performance optimization and caching
• [ ] Comprehensive testing framework
• [ ] Basic monitoring and alerting

Phase 2: Production Hardening (v0.2) - 3 weeks

Week 1: Performance & Reliability

• [ ] Advanced caching strategies (pattern cache + search cache)
• [ ] Auto-scaling and load balancing implementation
• [ ] Comprehensive monitoring dashboard
• [ ] Performance benchmarking and optimization

Week 2-3: Security & Operations

• [ ] Security framework with PII detection
• [ ] Privacy-preserving techniques
• [ ] Production deployment automation
• [ ] Disaster recovery procedures

Phase 3: Advanced Intelligence (v0.3) - 4 weeks

Week 1-2: Enhanced AI Capabilities

• [ ] Advanced AI agents (Researcher, Validator)
• [ ] Machine learning optimization for routing
• [ ] Personalized knowledge recommendations
• [ ] Cross-domain knowledge synthesis

Week 3-4: Knowledge Evolution

• [ ] Temporal knowledge tracking and versioning
• [ ] Knowledge graph evolution and learning
• [ ] Advanced quality metrics and validation
• [ ] Multi-modal knowledge support

Success Criteria

Performance Targets:

• Search latency P95 < 500ms (vector), < 800ms (hybrid)
• System availability > 99.9%
• Knowledge ingestion rate > 1000 documents/hour
• Cache hit ratio > 75%

Quality Metrics:

• Search relevance accuracy > 85%
• Knowledge quality score > 0.8
• Source diversity index > 0.6
• User satisfaction rating > 4.0/5.0

Operational Excellence:

• Security incident rate < 0.01%
• Data consistency > 99.5%
• MTTR < 10 minutes for critical issues
• Automated test coverage > 90%

---

References & Further Reading

Knowledge Management & Information Retrieval:

• Vector Search: Pinecone (2023). "The Vector Database Primer" - Modern embedding and retrieval techniques
• Hybrid Search: Karpukhin, V. et al. (2020). "Dense Passage Retrieval for Open-Domain Question Answering" EMNLP
• Knowledge Graphs: Hogan, A. et al. (2021). "Knowledge Graphs" ACM Computing Surveys, 54(4), 1-37
• Semantic Search: Reimers, N. & Gurevych, I. (2019). "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks" EMNLP

Multi-Agent Systems:

• Swarm Intelligence: Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). "Swarm Intelligence: From Natural to Artificial Systems" Oxford University Press
• Multi-Agent Coordination: Shoham, Y. & Leyton-Brown, K. (2008). "Multi-Agent Systems: Algorithmic, Game-Theoretic, and Logical Foundations" Cambridge University Press
• Agent Communication: FIPA (2002). "Agent Communication Language Specifications" Foundation for Intelligent Physical Agents

Machine Learning & Optimization:

• Reinforcement Learning: Sutton, R.S. & Barto, A.G. (2018). "Reinforcement Learning: An Introduction" (2nd Edition) MIT Press
• Pattern Recognition: Bishop, C.M. (2006). "Pattern Recognition and Machine Learning" Springer
• Caching Algorithms: Podlipnig, S. & Böszörmenyi, L. (2003). "A survey of web cache replacement strategies" ACM Computing Surveys, 35(4), 374-398

System Architecture:

• Distributed Systems: Kleppmann, M. (2017). "Designing Data-Intensive Applications" O'Reilly Media
• Microservices: Newman, S. (2021). "Building Microservices" (2nd Edition) O'Reilly Media
• Event-Driven Architecture: Hohpe, G. & Woolf, B. (2003). "Enterprise Integration Patterns" Addison-Wesley

Internal References:

• System Overview: ../README.md — Complete architecture overview
• Orchestration Integration: ../orchestration/README.md — L2 orchestration layer
• Contracts: ../contracts/README.md — Inter-layer communication protocols
• Evaluation Framework: ../evaluation/README.md — Quality assessment methodologies
• Architecture Standards: /ARCHITECTURE_DOCUMENTATION_STANDARDS.md — Documentation requirements

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Related Component Documentation

Core Components:

• Main Overview: ./README.md — L1 system overview and quick navigation
• Search Abstraction: ./search-abstraction.md — Unified search interface and behavior
• Vector Search: ./vector-search.md — Embedding models and indexing strategies
• Knowledge Graph: ./knowledge-graph.md — Entity schemas and graph traversal
• AI Librarian: ./ai-librarian.md — Agent roles and research capabilities

Integration & Advanced Features:

• MCP Protocol: ./mcp-integration.md — Model Context Protocol specifications
• Hyperbolic Space: ./hyperbolic-space.md — Mathematical foundations and geometry
• Configuration: ./config.md — System configuration and deployment settings

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Status: Technical architecture complete → Implementation ready → Production target (v0.2)

Next Priority: Phase 1 implementation — MCP protocol, vector search engine, knowledge graph, and meta-agent router with pattern cache.