proffesor-for-testing__agentic-qe/docs/architecture/AGENTDB-INTEGRATION-ARCHITECTURE.md

AgentDB Integration Architecture

Version: 1.0.0 Date: 2025-10-22 Status: Design Complete - Ready for Implementation Scope: Complete architectural design for AgentDB integration into AQE Fleet

---

Executive Summary

This document defines the complete architecture for integrating AgentDB into AQE agents, replacing JSON-based storage with production-ready vector search, QUIC synchronization, and neural training capabilities. The architecture ensures backward compatibility with v1.1.0 while enabling v1.2.0 claimed features.

Key Architectural Decisions

Decision	Choice	Rationale
Embedding Generation	Agent-side with service layer	Reduces network latency, enables offline operation
Embedding Model	@xenova/transformers (all-MiniLM-L6-v2)	384-dim, 80MB model, runs in Node.js, Apache 2.0 license
QUIC Peer Discovery	Manual configuration + mDNS	Simple initial implementation, extensible to service discovery
Vector Database	AgentDB with HNSW indexing	150x faster search, 4-32x memory reduction with quantization
RL Algorithms	AgentDB's 9 algorithms via plugins	Production-tested, 10-100x faster than custom Q-learning
Fallback Strategy	Graceful degradation to JSON storage	Zero breaking changes, optional opt-in
Synchronization	Push-based QUIC with conflict resolution	<1ms latency, TLS 1.3 security

---

1. System Architecture Overview

1.1 Component Hierarchy

┌─────────────────────────────────────────────────────────────────┐
│                         AQE Fleet Layer                          │
│  ┌────────────┐  ┌────────────┐  ┌────────────┐                │
│  │ QE Agents  │  │ QE Agents  │  │ QE Agents  │                │
│  │ (18 types) │  │ (18 types) │  │ (18 types) │                │
│  └─────┬──────┘  └─────┬──────┘  └─────┬──────┘                │
│        │                │                │                        │
│        └────────────────┼────────────────┘                        │
│                         ▼                                         │
│  ┌─────────────────────────────────────────────────┐            │
│  │            BaseAgent (Abstract)                  │            │
│  │  - Lifecycle hooks (onPreTask, onPostTask)      │            │
│  │  - Memory operations (store, retrieve)          │            │
│  │  - Event handling (emit, subscribe)             │            │
│  └─────────────────────┬───────────────────────────┘            │
│                        │                                         │
└────────────────────────┼─────────────────────────────────────────┘
                         │
┌────────────────────────┼─────────────────────────────────────────┐
│                AgentDB Service Layer                             │
│                        ▼                                         │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │              AgentDBService (Facade)                      │  │
│  │  - Unified interface for all AgentDB operations           │  │
│  │  - Pattern storage, retrieval, training                   │  │
│  │  - QUIC sync coordination                                 │  │
│  │  - Error handling and fallback                            │  │
│  └───┬──────────────────┬──────────────────┬─────────────────┘  │
│      │                  │                  │                     │
│      ▼                  ▼                  ▼                     │
│  ┌─────────┐  ┌─────────────────┐  ┌────────────────┐          │
│  │Embedding│  │  VectorStore    │  │  QUICService   │          │
│  │Generator│  │  (HNSW Index)   │  │  (P2P Sync)    │          │
│  └─────────┘  └─────────────────┘  └────────────────┘          │
│      │                  │                  │                     │
│      │                  ▼                  │                     │
│      │         ┌─────────────────┐         │                     │
│      │         │ NeuralTrainer   │         │                     │
│      │         │ (9 RL Algos)    │         │                     │
│      │         └─────────────────┘         │                     │
│      │                  │                  │                     │
└──────┼──────────────────┼──────────────────┼─────────────────────┘
       │                  │                  │
┌──────┼──────────────────┼──────────────────┼─────────────────────┐
│      │                  │                  │                     │
│      ▼                  ▼                  ▼                     │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                AgentDB Core (npm package)                │   │
│  │  - SQLite storage with vector extensions                │   │
│  │  - HNSW indexing (M=16, efConstruction=200)             │   │
│  │  - Learning plugins (9 RL algorithms)                    │   │
│  │  - QUIC protocol implementation (TLS 1.3)                │   │
│  │  - Quantization (scalar, binary, product)                │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                  │
│                    Storage Layer                                │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐         │
│  │  SQLite DB   │  │  HNSW Index  │  │  RL Models   │         │
│  │ (patterns.db)│  │  (in-memory) │  │ (checkpoints)│         │
│  └──────────────┘  └──────────────┘  └──────────────┘         │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

1.2 Data Flow: Pattern Storage

1. Agent executes task
   │
   ▼
2. BaseAgent.onPostTask() hook triggered
   │
   ▼
3. EmbeddingGenerator.generateEmbedding(taskData)
   │ → Transforms text to 384-dim vector using all-MiniLM-L6-v2
   │
   ▼
4. AgentDBService.storePattern(pattern, embedding)
   │ → Stores in SQLite with vector metadata
   │ → Builds/updates HNSW index
   │
   ▼
5. QUICService.syncPattern(pattern) [if enabled]
   │ → Broadcasts to peers via QUIC (TLS 1.3)
   │ → <1ms latency for local network
   │
   ▼
6. NeuralTrainer.train() [every 100 patterns]
   │ → Triggers incremental RL algorithm training
   │ → Updates decision models
   │
   ▼
7. Pattern available for retrieval via vector search

1.3 Data Flow: Pattern Retrieval

1. Agent receives new task
   │
   ▼
2. BaseAgent.onPreTask() hook triggered
   │
   ▼
3. EmbeddingGenerator.generateEmbedding(taskQuery)
   │ → Query: "Generate unit tests for UserService"
   │ → Embedding: [0.12, -0.45, 0.78, ...] (384 dims)
   │
   ▼
4. VectorStore.searchSimilar(queryEmbedding, k=10)
   │ → HNSW index search (<100µs)
   │ → Returns top-k similar patterns with distances
   │
   ▼
5. AgentDBService.retrieve(queryEmbedding, options)
   │ → Filters by confidence threshold (default: 0.6)
   │ → Applies MMR for diversity
   │ → Synthesizes context from patterns
   │
   ▼
6. BaseAgent enriches task context with retrieved patterns
   │ → Similar tasks from other agents
   │ → Best practices and successful strategies
   │ → Error patterns to avoid
   │
   ▼
7. Agent executes task with enhanced context

---

2. Component Specifications

2.1 AgentDBService (Facade)

Responsibility: Unified interface for all AgentDB operations with graceful fallback.

Interface

// File: src/core/agentdb/AgentDBService.ts

export interface AgentDBConfig {
  // Database
  dbPath: string;                      // Path to SQLite database

  // Vector search
  embeddingModel: 'all-MiniLM-L6-v2' | 'code-bert' | 'custom';
  embeddingDimensions: number;         // Default: 384
  hnswM: number;                       // HNSW parameter M (default: 16)
  hnswEfConstruction: number;          // HNSW build param (default: 200)
  hnswEfSearch: number;                // HNSW search param (default: 100)

  // QUIC synchronization
  enableQUICSync: boolean;             // Enable P2P sync
  quicPort: number;                    // QUIC server port (default: 4433)
  quicPeers: string[];                 // Peer addresses ['host:port']
  quicSyncInterval: number;            // Sync interval in ms (default: 1000)
  quicBatchSize: number;               // Patterns per sync batch (default: 100)
  quicTLSCert?: string;                // Path to TLS certificate
  quicTLSKey?: string;                 // Path to TLS private key

  // Neural training
  enableNeuralTraining: boolean;       // Enable RL algorithms
  neuralAlgorithm: 'q-learning' | 'sarsa' | 'actor-critic' | 'dqn' | 'a3c' | 'ppo' | 'ddpg' | 'td3' | 'sac';
  neuralTrainingInterval: number;      // Patterns between training (default: 100)
  neuralEpochs: number;                // Epochs per training (default: 10)
  neuralBatchSize: number;             // Batch size for training (default: 32)

  // Performance
  cacheSize: number;                   // In-memory cache size (default: 1000)
  quantizationType: 'none' | 'scalar' | 'binary' | 'product';

  // Fallback
  enableFallback: boolean;             // Graceful degradation (default: true)
  fallbackToJSON: boolean;             // Use JSON if AgentDB fails (default: true)
}

export interface Pattern {
  id: string;                          // Unique pattern ID
  type: 'experience' | 'error' | 'success' | 'strategy';
  agentId: string;                     // Agent that created pattern
  agentType: string;                   // Agent type (e.g., 'test-generator')

  // Content
  data: {
    taskType: string;                  // Task type
    taskDescription: string;           // Task description
    result: any;                       // Execution result
    context: Record<string, any>;      // Execution context
    metrics: {
      executionTime: number;
      success: boolean;
      accuracy?: number;
      confidence: number;
    };
  };

  // Vector search
  embedding: number[];                 // 384-dim embedding vector

  // Metadata
  confidence: number;                  // Pattern confidence (0-1)
  usageCount: number;                  // Times used
  successCount: number;                // Times successful
  createdAt: number;                   // Creation timestamp
  lastUsed: number;                    // Last usage timestamp
  tags: string[];                      // Searchable tags
}

export interface RetrievalOptions {
  k: number;                           // Number of results
  minConfidence?: number;              // Minimum confidence (default: 0.6)
  agentTypes?: string[];               // Filter by agent types
  patternTypes?: string[];             // Filter by pattern types
  dateRange?: {                        // Filter by date
    start: number;
    end: number;
  };
  useMMR?: boolean;                    // Maximal Marginal Relevance (default: true)
  mmrLambda?: number;                  // MMR diversity (default: 0.5)
  synthesizeContext?: boolean;         // Generate context summary (default: true)
}

export interface RetrievalResult {
  patterns: Array<Pattern & { similarity: number; distance: number }>;
  synthesizedContext?: string;         // AI-generated context summary
  stats: {
    queryTime: number;                 // Query execution time (ms)
    totalPatterns: number;             // Total patterns in DB
    cacheHit: boolean;                 // Cache hit indicator
  };
}

export interface NeuralTrainingMetrics {
  algorithm: string;                   // RL algorithm used
  loss: number;                        // Training loss
  valLoss?: number;                    // Validation loss
  accuracy?: number;                   // Model accuracy
  duration: number;                    // Training duration (ms)
  epochs: number;                      // Epochs completed
  samplesProcessed: number;            // Training samples
}

export class AgentDBService {
  private config: AgentDBConfig;
  private embeddingGenerator: EmbeddingGenerator;
  private vectorStore: VectorStore;
  private quicService?: QUICService;
  private neuralTrainer?: NeuralTrainer;
  private cache: LRUCache<string, Pattern[]>;
  private fallbackStorage?: JSONStorage; // Fallback if AgentDB unavailable
  private initialized: boolean = false;

  constructor(config: AgentDBConfig);

  // Lifecycle
  async initialize(): Promise<void>;
  async close(): Promise<void>;

  // Pattern operations
  async storePattern(pattern: Pattern): Promise<string>;
  async retrievePattern(id: string): Promise<Pattern | null>;
  async searchSimilar(queryEmbedding: number[], options: RetrievalOptions): Promise<RetrievalResult>;
  async deletePattern(id: string): Promise<boolean>;

  // Batch operations
  async storeBatch(patterns: Pattern[]): Promise<string[]>;
  async searchBatch(queries: number[][], options: RetrievalOptions): Promise<RetrievalResult[]>;

  // Neural training
  async train(patterns?: Pattern[]): Promise<NeuralTrainingMetrics>;
  async predictStrategy(context: any): Promise<{ strategy: string; confidence: number }>;

  // QUIC synchronization
  async syncWithPeers(patterns: Pattern[]): Promise<{ synced: number; failed: number }>;
  async startQUICServer(): Promise<void>;
  async stopQUICServer(): Promise<void>;

  // Statistics
  async getStats(): Promise<{
    totalPatterns: number;
    patternsByType: Record<string, number>;
    patternsByAgent: Record<string, number>;
    avgConfidence: number;
    cacheHitRate: number;
    vectorIndexSize: number;
    quicPeersConnected: number;
    neuralModelAccuracy?: number;
  }>;

  // Health check
  async healthCheck(): Promise<{
    agentdb: boolean;
    vectorSearch: boolean;
    quicSync: boolean;
    neuralTraining: boolean;
    fallbackActive: boolean;
  }>;
}

---

2.2 EmbeddingGenerator

Responsibility: Generate vector embeddings from text/code using transformer models.

Architecture Decision: Agent-Side Generation

Rationale:

• Reduced latency: No network calls to external embedding service
• Offline operation: Works without internet connectivity
• Cost efficiency: No API costs for embedding generation
• Privacy: Code never leaves agent's environment
• Scalability: No central bottleneck for embedding generation

Trade-off: Each agent requires ~80MB memory for model (acceptable for QE workload)

Interface

// File: src/core/agentdb/EmbeddingGenerator.ts

import { pipeline } from '@xenova/transformers';

export interface EmbeddingOptions {
  model: 'all-MiniLM-L6-v2' | 'code-bert-base' | 'custom';
  dimensions: number;                  // Output dimensions (default: 384)
  normalize: boolean;                  // L2 normalize (default: true)
  pooling: 'mean' | 'cls' | 'max';    // Pooling strategy (default: 'mean')
  truncation: boolean;                 // Truncate long inputs (default: true)
  maxLength: number;                   // Max token length (default: 512)
}

export class EmbeddingGenerator {
  private model: any;                  // Transformers pipeline
  private config: EmbeddingOptions;
  private modelLoaded: boolean = false;

  constructor(config: EmbeddingOptions);

  // Initialization
  async initialize(): Promise<void> {
    // Load model: Sentence-BERT all-MiniLM-L6-v2 (80MB)
    // - 384 dimensions
    // - 22M parameters
    // - Trained on 1B+ sentence pairs
    // - Apache 2.0 license
    this.model = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
    this.modelLoaded = true;
  }

  // Single embedding generation
  async generateEmbedding(text: string): Promise<number[]> {
    // 1. Tokenize input
    // 2. Pass through transformer
    // 3. Apply pooling strategy
    // 4. Normalize to unit vector
    // 5. Return 384-dim embedding
  }

  // Batch embedding generation (10x faster than sequential)
  async generateBatch(texts: string[]): Promise<number[][]> {
    // Batched inference for efficiency
  }

  // Code-specific embedding
  async generateCodeEmbedding(code: string, language: string): Promise<number[]> {
    // Add code-specific preprocessing:
    // - Remove comments
    // - Normalize whitespace
    // - Extract function signatures
    // Then generate embedding
  }

  // Query-specific embedding
  async generateQueryEmbedding(query: string): Promise<number[]> {
    // Add query prefix for better retrieval
    // "Represent this query for retrieval: {query}"
  }

  // Utility
  async computeSimilarity(emb1: number[], emb2: number[]): Promise<number> {
    // Cosine similarity: dot(emb1, emb2) / (||emb1|| * ||emb2||)
  }
}

Why all-MiniLM-L6-v2?

Criteria	all-MiniLM-L6-v2	code-bert-base	text-embedding-ada-002
Dimensions	384	768	1536
Size	80MB	420MB	API only
Speed	~5ms/text	~15ms/text	~50ms + network
License	Apache 2.0	Apache 2.0	Proprietary
Offline	✅ Yes	✅ Yes	❌ No
Cost	Free	Free	$0.0001/1k tokens
Code Quality	Good	Excellent	Excellent
Docs Quality	Excellent	Good	Excellent

Decision: Start with all-MiniLM-L6-v2 for balance, add code-bert-base option later.

---

2.3 VectorStore (HNSW Index)

Responsibility: Fast similarity search using Hierarchical Navigable Small World graphs.

Architecture Decision: HNSW Parameters

HNSW Parameters Rationale:

• M=16: Trade-off between recall and index size (16 connections per node)
• efConstruction=200: Build quality (higher = better recall, slower build)
• efSearch=100: Search quality (higher = better recall, slower search)

Performance targets:

• Build time: <10ms for 1000 patterns
• Search time: <100µs for top-10 query
• Recall@10: >95% (vs exhaustive search)

Interface

// File: src/core/agentdb/VectorStore.ts

export interface HNSWConfig {
  dimensions: number;                  // Embedding dimensions (384)
  M: number;                           // HNSW parameter M (default: 16)
  efConstruction: number;              // Build parameter (default: 200)
  efSearch: number;                    // Search parameter (default: 100)
  metric: 'cosine' | 'l2' | 'ip';     // Distance metric (default: 'cosine')
  maxElements: number;                 // Max index capacity (default: 100000)
}

export interface SearchResult {
  id: string;                          // Pattern ID
  distance: number;                    // Distance score
  similarity: number;                  // Similarity score (1 - distance)
}

export class VectorStore {
  private db: any;                     // AgentDB instance
  private index: any;                  // HNSW index (hnswlib-node)
  private config: HNSWConfig;
  private indexBuilt: boolean = false;

  constructor(db: any, config: HNSWConfig);

  // Index management
  async buildIndex(patterns: Pattern[]): Promise<void> {
    // 1. Initialize HNSW index
    // 2. Add vectors with IDs
    // 3. Build graph structure
    // 4. Save index to disk (optional)
  }

  async rebuildIndex(): Promise<void> {
    // Full index rebuild from database
  }

  async addVector(id: string, embedding: number[]): Promise<void> {
    // Add single vector to index
    // Update HNSW graph incrementally
  }

  async addVectorBatch(vectors: Array<{ id: string; embedding: number[] }>): Promise<void> {
    // Batch addition (10x faster)
  }

  async removeVector(id: string): Promise<boolean> {
    // Remove vector from index
    // Note: HNSW doesn't support efficient deletion
    // Approach: Mark deleted, rebuild periodically
  }

  // Search operations
  async search(queryEmbedding: number[], k: number): Promise<SearchResult[]> {
    // HNSW approximate nearest neighbor search
    // Returns top-k results with distances
  }

  async searchWithFilter(
    queryEmbedding: number[],
    k: number,
    filter: (id: string) => boolean
  ): Promise<SearchResult[]> {
    // Search with post-filtering
    // Note: HNSW doesn't support pre-filtering efficiently
  }

  // Maximal Marginal Relevance (diversity)
  async searchMMR(
    queryEmbedding: number[],
    k: number,
    lambda: number = 0.5
  ): Promise<SearchResult[]> {
    // 1. Retrieve top-k*2 candidates
    // 2. Iteratively select diverse results
    // 3. Balance relevance (lambda) and diversity (1-lambda)
  }

  // Statistics
  getIndexStats(): {
    totalVectors: number;
    dimensions: number;
    indexSizeBytes: number;
    avgSearchTimeMs: number;
  };
}

Why HNSW over alternatives?

Algorithm	Build Time	Search Time	Recall	Memory
HNSW	O(log N)	O(log N)	95%+	High
IVF	O(N)	O(√N)	90%+	Medium
LSH	O(N)	O(1)	80%+	Low
Exhaustive	O(1)	O(N)	100%	Low

Decision: HNSW for best recall/speed trade-off (industry standard for <10M vectors).

---

2.4 QUICService (P2P Synchronization)

Responsibility: Synchronize patterns across agents using QUIC protocol with TLS 1.3.

Architecture Decision: QUIC over WebSocket

QUIC Benefits:

• 0-RTT connection establishment (vs 3-RTT for TCP+TLS)
• Multiplexed streams without head-of-line blocking
• Built-in encryption (TLS 1.3 mandatory)
• Connection migration (survives IP changes)
• Faster than WebSocket by 40-60% for low-latency data

Performance targets:

• Connection establishment: <10ms (local network)
• Pattern sync latency: <1ms (local network)
• Throughput: >10k patterns/sec
• Packet loss tolerance: <5% without retransmission

Interface

// File: src/core/agentdb/QUICService.ts

import { QuicStream, QuicConnection } from 'node:quic';

export interface QUICConfig {
  port: number;                        // QUIC server port (default: 4433)
  host: string;                        // Bind address (default: '0.0.0.0')
  peers: string[];                     // Peer addresses ['host:port']

  // TLS configuration (mandatory for QUIC)
  tlsCert: string;                     // Path to certificate
  tlsKey: string;                      // Path to private key
  tlsCA?: string;                      // Path to CA cert (for verification)

  // Synchronization
  syncInterval: number;                // Sync interval (default: 1000ms)
  batchSize: number;                   // Patterns per sync (default: 100)
  compression: boolean;                // Enable compression (default: true)

  // Reliability
  maxRetries: number;                  // Max retries (default: 3)
  retryBackoff: number;                // Backoff multiplier (default: 2)
  heartbeatInterval: number;           // Keep-alive interval (default: 5000ms)

  // Peer discovery
  enableMDNS: boolean;                 // Auto-discover local peers (default: false)
  mdnsServiceName: string;             // mDNS service name (default: '_aqe-quic._udp')
}

export interface SyncMessage {
  type: 'sync' | 'ack' | 'heartbeat' | 'discovery';
  agentId: string;                     // Sender agent ID
  timestamp: number;                   // Message timestamp
  patterns?: Pattern[];                // Patterns to sync
  patternIds?: string[];               // Pattern IDs for ACK
  vectorClock?: Record<string, number>; // Vector clock for conflict resolution
}

export interface PeerInfo {
  address: string;                     // Peer address 'host:port'
  agentId: string;                     // Peer agent ID
  lastSeen: number;                    // Last heartbeat timestamp
  latency: number;                     // Round-trip latency (ms)
  patternsReceived: number;            // Total patterns received
  patternsSent: number;                // Total patterns sent
  connected: boolean;                  // Connection status
}

export class QUICService {
  private config: QUICConfig;
  private server?: any;                // QUIC server instance
  private connections: Map<string, QuicConnection> = new Map();
  private peers: Map<string, PeerInfo> = new Map();
  private syncQueue: Pattern[] = [];   // Pending patterns to sync
  private vectorClock: Record<string, number> = {}; // For conflict resolution
  private agentDBService: AgentDBService;

  constructor(config: QUICConfig, agentDBService: AgentDBService);

  // Server lifecycle
  async start(): Promise<void> {
    // 1. Load TLS certificates
    // 2. Create QUIC server
    // 3. Start listening on port
    // 4. Start peer discovery (if enabled)
    // 5. Start sync worker
  }

  async stop(): Promise<void> {
    // 1. Close all peer connections
    // 2. Stop sync worker
    // 3. Close server
  }

  // Peer management
  async connectToPeer(address: string): Promise<void> {
    // 1. Establish QUIC connection
    // 2. TLS 1.3 handshake
    // 3. Send discovery message
    // 4. Add to peer list
  }

  async disconnectFromPeer(address: string): Promise<void> {
    // Gracefully close connection
  }

  async discoverPeers(): Promise<string[]> {
    // mDNS-based peer discovery
    // Returns list of peer addresses
  }

  // Synchronization
  async syncPattern(pattern: Pattern): Promise<void> {
    // Queue pattern for next sync batch
    this.syncQueue.push(pattern);
  }

  async syncBatch(patterns: Pattern[]): Promise<{ synced: number; failed: number }> {
    // 1. Serialize patterns (msgpack or JSON)
    // 2. Optionally compress (gzip)
    // 3. Broadcast to all connected peers
    // 4. Wait for ACKs (with timeout)
    // 5. Retry failed sends
    // 6. Update vector clock
  }

  private async syncWorker(): Promise<void> {
    // Background worker that runs every syncInterval
    // 1. Dequeue patterns from syncQueue
    // 2. Batch patterns (up to batchSize)
    // 3. Broadcast to peers
    // 4. Handle responses
  }

  // Message handling
  private async handleMessage(message: SyncMessage, peer: string): Promise<void> {
    switch (message.type) {
      case 'sync':
        // 1. Receive patterns
        // 2. Check for conflicts (vector clock)
        // 3. Resolve conflicts (last-write-wins or custom)
        // 4. Store patterns in local AgentDB
        // 5. Send ACK
        break;
      case 'ack':
        // Mark patterns as successfully synced
        break;
      case 'heartbeat':
        // Update peer last_seen timestamp
        break;
      case 'discovery':
        // Add peer to peer list
        break;
    }
  }

  // Conflict resolution
  private resolveConflict(local: Pattern, remote: Pattern): Pattern {
    // Vector clock-based conflict resolution
    // 1. Compare vector clocks
    // 2. If one dominates, take that version
    // 3. If concurrent, use last-write-wins
    // 4. Merge metadata (usageCount, successCount)
  }

  // Statistics
  getPeerStats(): PeerInfo[] {
    return Array.from(this.peers.values());
  }

  getSyncStats(): {
    queueSize: number;
    patternsSyncedTotal: number;
    patternsReceivedTotal: number;
    avgLatency: number;
    peersConnected: number;
  };
}

QUIC Peer Discovery Strategy:

Phase 1 (v1.2.0): Manual Configuration
- Peers configured in .agentic-qe/config/agentdb.json
- Simple and reliable
- Example:
  {
    "quic": {
      "peers": ["192.168.1.10:4433", "192.168.1.11:4433"]
    }
  }

Phase 2 (v1.3.0): mDNS Discovery
- Auto-discover peers on local network
- Use _aqe-quic._udp service name
- Requires mdns package

Phase 3 (v2.0.0): Central Registry
- Optional central coordination server
- DNS-SD for discovery
- Support for cloud deployments

---

2.5 NeuralTrainer (Reinforcement Learning)

Responsibility: Train neural models using AgentDB's 9 RL algorithms for strategy optimization.

Architecture Decision: Which RL Algorithms?

AgentDB supports 9 RL algorithms:

Algorithm	Best For	Convergence	Sample Efficiency
Q-Learning	Discrete actions, simple	Fast	Low
SARSA	On-policy learning	Medium	Low
Actor-Critic	Continuous actions	Fast	Medium
DQN	Deep Q-networks	Medium	Medium
A3C	Parallel training	Fast	High
PPO	Stable policy gradient	Slow	High
DDPG	Continuous control	Medium	High
TD3	Improved DDPG	Medium	High
SAC	Entropy-regularized	Slow	Very High

Default for AQE: Start with PPO (Proximal Policy Optimization)

• Stable: Less sensitive to hyperparameters
• Sample efficient: Learns from less data
• Proven: Used by OpenAI, DeepMind for production systems
• Continuous: Works for strategy selection (not just discrete actions)

Interface

// File: src/core/agentdb/NeuralTrainer.ts

export interface TrainingConfig {
  algorithm: 'q-learning' | 'sarsa' | 'actor-critic' | 'dqn' | 'a3c' | 'ppo' | 'ddpg' | 'td3' | 'sac';

  // Training hyperparameters
  epochs: number;                      // Training epochs (default: 10)
  batchSize: number;                   // Batch size (default: 32)
  learningRate: number;                // Learning rate (default: 0.0003)
  discountFactor: number;              // Gamma (default: 0.99)

  // PPO-specific
  ppoEpsilon: number;                  // Clipping epsilon (default: 0.2)
  ppoEpochs: number;                   // PPO epochs (default: 4)
  entropyCoef: number;                 // Entropy coefficient (default: 0.01)

  // Model architecture
  hiddenLayers: number[];              // Hidden layer sizes (default: [128, 64])
  activation: 'relu' | 'tanh' | 'sigmoid';

  // Training control
  validationSplit: number;             // Validation split (default: 0.2)
  earlyStoppingPatience: number;       // Early stopping (default: 5)
  checkpointInterval: number;          // Save every N epochs (default: 10)

  // Experience replay
  replayBufferSize: number;            // Replay buffer (default: 10000)
  prioritizedReplay: boolean;          // Use prioritized replay (default: false)
}

export interface TrainingExperience {
  state: any;                          // Task state
  action: string;                      // Strategy/action taken
  reward: number;                      // Reward signal
  nextState: any;                      // Resulting state
  done: boolean;                       // Episode terminal flag
  metadata: {
    taskType: string;
    agentType: string;
    executionTime: number;
    success: boolean;
  };
}

export interface TrainingMetrics {
  algorithm: string;

  // Performance
  loss: number;                        // Training loss
  valLoss?: number;                    // Validation loss
  accuracy: number;                    // Model accuracy

  // Training stats
  epochs: number;                      // Epochs completed
  samplesProcessed: number;            // Training samples
  duration: number;                    // Training duration (ms)

  // RL-specific
  avgReward?: number;                  // Average episode reward
  avgEpisodeLength?: number;           // Average episode length
  explorationRate?: number;            // Current epsilon (for e-greedy)

  // Convergence
  converged: boolean;                  // Has model converged?
  convergenceEpoch?: number;           // Epoch when converged
}

export interface StrategyPrediction {
  strategy: string;                    // Recommended strategy
  confidence: number;                  // Confidence score (0-1)
  alternatives: Array<{
    strategy: string;
    confidence: number;
  }>;
  reasoning?: string;                  // Explanation (optional)
}

export class NeuralTrainer {
  private db: any;                     // AgentDB instance
  private config: TrainingConfig;
  private model?: any;                 // Trained model
  private replayBuffer: TrainingExperience[] = [];
  private trainingHistory: TrainingMetrics[] = [];

  constructor(db: any, config: TrainingConfig);

  // Training
  async train(experiences?: TrainingExperience[]): Promise<TrainingMetrics> {
    // 1. Load experiences from DB (or use provided)
    // 2. Prepare training data (state -> action pairs)
    // 3. Initialize/load model
    // 4. Train using selected RL algorithm
    // 5. Validate on held-out set
    // 6. Save checkpoint
    // 7. Return metrics
  }

  async trainIncremental(newExperiences: TrainingExperience[]): Promise<TrainingMetrics> {
    // Incremental training with new experiences
    // More efficient than full retraining
  }

  async continueTraining(additionalEpochs: number): Promise<TrainingMetrics> {
    // Continue training existing model
  }

  // Prediction
  async predict(state: any): Promise<StrategyPrediction> {
    // 1. Normalize state
    // 2. Forward pass through model
    // 3. Sample action from policy distribution
    // 4. Return strategy with confidence
  }

  async predictBatch(states: any[]): Promise<StrategyPrediction[]> {
    // Batch prediction (10x faster)
  }

  // Experience management
  addExperience(experience: TrainingExperience): void {
    // Add to replay buffer
    // If buffer full, remove oldest (FIFO)
  }

  addExperiences(experiences: TrainingExperience[]): void {
    // Batch addition
  }

  async loadExperiences(agentType?: string, limit?: number): Promise<TrainingExperience[]> {
    // Load experiences from AgentDB
    // Filter by agent type if specified
    // Apply limit for memory efficiency
  }

  // Model management
  async saveModel(path: string): Promise<void> {
    // Save model weights and config
  }

  async loadModel(path: string): Promise<void> {
    // Load model from checkpoint
  }

  async evaluateModel(testExperiences: TrainingExperience[]): Promise<{
    accuracy: number;
    avgReward: number;
    lossMetrics: any;
  }> {
    // Evaluate model on test set
  }

  // Statistics
  getTrainingHistory(): TrainingMetrics[] {
    return this.trainingHistory;
  }

  getModelInfo(): {
    algorithm: string;
    architecture: any;
    totalParameters: number;
    lastTrainedAt: number;
    trainingEpochs: number;
    accuracy: number;
  };
}

Reward Function Design:

// How to calculate reward for agent experiences
function calculateReward(experience: {
  taskType: string;
  success: boolean;
  executionTime: number;
  accuracy?: number;
  userFeedback?: number; // 0-1 score
}): number {
  // Multi-objective reward function

  // 1. Success reward (binary)
  const successReward = experience.success ? 1.0 : -0.5;

  // 2. Accuracy reward (if available)
  const accuracyReward = experience.accuracy ? experience.accuracy * 0.5 : 0;

  // 3. Efficiency reward (faster is better)
  const timeTarget = 5000; // 5 seconds target
  const efficiencyReward = Math.max(0, 1 - experience.executionTime / timeTarget) * 0.3;

  // 4. User feedback (if available)
  const feedbackReward = experience.userFeedback ? experience.userFeedback * 0.2 : 0;

  // Total reward (weighted sum)
  return successReward + accuracyReward + efficiencyReward + feedbackReward;
}

---

3. Integration with BaseAgent

3.1 Modified BaseAgent Lifecycle

Changes to BaseAgent.ts:

// File: src/agents/BaseAgent.ts (modifications)

import { AgentDBService } from '../core/agentdb/AgentDBService';

export interface BaseAgentConfig {
  // ... existing config ...

  // New: AgentDB integration
  agentDBConfig?: {
    enabled: boolean;
    dbPath: string;
    enableQUICSync: boolean;
    enableNeuralTraining: boolean;
    // ... other AgentDB config options ...
  };
}

export abstract class BaseAgent extends EventEmitter {
  // ... existing properties ...

  private agentDBService?: AgentDBService;
  private embeddingGenerator?: EmbeddingGenerator;

  constructor(config: BaseAgentConfig) {
    // ... existing initialization ...

    // Initialize AgentDB if configured
    if (config.agentDBConfig?.enabled) {
      this.agentDBService = new AgentDBService(config.agentDBConfig);
      this.embeddingGenerator = new EmbeddingGenerator({
        model: 'all-MiniLM-L6-v2',
        dimensions: 384,
        normalize: true
      });
    }
  }

  async initialize(): Promise<void> {
    // ... existing initialization ...

    // Initialize AgentDB components
    if (this.agentDBService) {
      await this.embeddingGenerator?.initialize();
      await this.agentDBService.initialize();

      // Start QUIC server if enabled
      if (this.agentDBConfig?.enableQUICSync) {
        await this.agentDBService.startQUICServer();
      }
    }
  }

  protected async onPreTask(data: PreTaskData): Promise<void> {
    // ... existing pre-task logic ...

    // AgentDB: Load relevant context via vector search
    if (this.agentDBService && this.embeddingGenerator) {
      try {
        // 1. Generate query embedding
        const queryText = this.extractQueryText(data.assignment.task);
        const queryEmbedding = await this.embeddingGenerator.generateEmbedding(queryText);

        // 2. Search for similar patterns
        const retrievalResult = await this.agentDBService.searchSimilar(queryEmbedding, {
          k: 10,
          minConfidence: 0.6,
          agentTypes: [this.agentId.type],
          patternTypes: ['experience', 'success'],
          useMMR: true,
          synthesizeContext: true
        });

        // 3. Enrich task context
        if (retrievalResult.patterns.length > 0) {
          data.context.agentDBContext = {
            synthesizedContext: retrievalResult.synthesizedContext,
            similarPatterns: retrievalResult.patterns.map(p => ({
              taskType: p.data.taskType,
              result: p.data.result,
              similarity: p.similarity,
              confidence: p.confidence
            })),
            queryTime: retrievalResult.stats.queryTime
          };

          console.info(`[${this.agentId.id}] Loaded ${retrievalResult.patterns.length} patterns from AgentDB (${retrievalResult.stats.queryTime}ms)`);
        }

        // 4. Get neural strategy recommendation (if training enabled)
        if (this.agentDBConfig?.enableNeuralTraining) {
          const prediction = await this.agentDBService.predictStrategy(data.assignment.task);
          data.context.recommendedStrategy = prediction;
          console.info(`[${this.agentId.id}] Neural recommendation: ${prediction.strategy} (confidence: ${prediction.confidence.toFixed(2)})`);
        }
      } catch (error) {
        console.warn(`[${this.agentId.id}] AgentDB context loading failed:`, error);
        // Continue execution without AgentDB context (graceful degradation)
      }
    }
  }

  protected async onPostTask(data: PostTaskData): Promise<void> {
    // ... existing post-task logic ...

    // AgentDB: Store execution pattern
    if (this.agentDBService && this.embeddingGenerator) {
      try {
        // 1. Create pattern object
        const patternData = {
          taskType: data.assignment.task.type,
          taskDescription: data.assignment.task.description || '',
          result: data.result,
          context: data.assignment.task.context || {},
          metrics: {
            executionTime: Date.now() - (this.taskStartTime || Date.now()),
            success: data.result?.success || true,
            accuracy: data.result?.accuracy,
            confidence: this.calculatePatternConfidence(data.result)
          }
        };

        // 2. Generate embedding
        const embeddingText = this.extractEmbeddingText(patternData);
        const embedding = await this.embeddingGenerator.generateEmbedding(embeddingText);

        // 3. Create pattern
        const pattern: Pattern = {
          id: `${this.agentId.id}-${data.assignment.id}`,
          type: patternData.metrics.success ? 'success' : 'error',
          agentId: this.agentId.id,
          agentType: this.agentId.type,
          data: patternData,
          embedding,
          confidence: patternData.metrics.confidence,
          usageCount: 0,
          successCount: patternData.metrics.success ? 1 : 0,
          createdAt: Date.now(),
          lastUsed: Date.now(),
          tags: this.extractTags(patternData)
        };

        // 4. Store pattern
        const patternId = await this.agentDBService.storePattern(pattern);
        console.info(`[${this.agentId.id}] Stored pattern in AgentDB: ${patternId}`);

        // 5. Sync with peers (if QUIC enabled)
        if (this.agentDBConfig?.enableQUICSync) {
          await this.agentDBService.syncWithPeers([pattern]);
        }

        // 6. Trigger neural training (if enabled and threshold reached)
        if (this.agentDBConfig?.enableNeuralTraining) {
          const stats = await this.agentDBService.getStats();
          if (stats.totalPatterns % 100 === 0) {
            console.info(`[${this.agentId.id}] Triggering neural training (${stats.totalPatterns} patterns)`);
            const trainingMetrics = await this.agentDBService.train();
            console.info(
              `[${this.agentId.id}] Training complete: ` +
              `loss=${trainingMetrics.loss.toFixed(4)}, ` +
              `accuracy=${trainingMetrics.accuracy?.toFixed(2)}, ` +
              `duration=${trainingMetrics.duration}ms`
            );
          }
        }
      } catch (error) {
        console.warn(`[${this.agentId.id}] AgentDB pattern storage failed:`, error);
        // Continue execution without AgentDB storage (graceful degradation)
      }
    }
  }

  // Helper methods
  private extractQueryText(task: QETask): string {
    return `${task.type}: ${task.description || ''}`;
  }

  private extractEmbeddingText(patternData: any): string {
    return JSON.stringify({
      taskType: patternData.taskType,
      taskDescription: patternData.taskDescription,
      success: patternData.metrics.success,
      executionTime: patternData.metrics.executionTime
    });
  }

  private extractTags(patternData: any): string[] {
    const tags = [
      patternData.taskType,
      patternData.metrics.success ? 'success' : 'error'
    ];

    // Add framework tags if available
    if (patternData.context?.framework) {
      tags.push(patternData.context.framework);
    }

    return tags;
  }

  private calculatePatternConfidence(result: any): number {
    // Simple confidence calculation
    // In production, use more sophisticated logic
    if (!result?.success) return 0.3;

    const baseConfidence = 0.8;
    const accuracyBonus = (result.accuracy || 0.8) * 0.2;

    return Math.min(1.0, baseConfidence + accuracyBonus);
  }
}

3.2 Backward Compatibility Strategy

Goal: Zero breaking changes for v1.1.0 users.

Approach: Feature flags with graceful degradation.

// File: .agentic-qe/config/agentdb.json (user configuration)

{
  "enabled": false,  // Opt-in (default: false)

  // If enabled=true, configure AgentDB
  "dbPath": ".agentic-qe/agentdb/patterns.db",

  // Vector search
  "embedding": {
    "model": "all-MiniLM-L6-v2",
    "dimensions": 384
  },

  // QUIC synchronization
  "quic": {
    "enabled": false,  // Opt-in
    "port": 4433,
    "peers": [],
    "tlsCert": ".agentic-qe/certs/cert.pem",
    "tlsKey": ".agentic-qe/certs/key.pem"
  },

  // Neural training
  "neural": {
    "enabled": false,  // Opt-in
    "algorithm": "ppo",
    "trainingInterval": 100
  },

  // Fallback behavior
  "fallback": {
    "enableJSONStorage": true,  // Fallback to JSON if AgentDB fails
    "logErrors": true
  }
}

Fallback Logic:

// Graceful degradation in AgentDBService

async storePattern(pattern: Pattern): Promise<string> {
  try {
    // Try AgentDB first
    return await this.agentDB.storePattern(pattern);
  } catch (error) {
    if (this.config.fallback.enableJSONStorage) {
      // Fall back to JSON storage (v1.1.0 behavior)
      console.warn('AgentDB storage failed, falling back to JSON', error);
      return await this.fallbackStorage.store(pattern);
    } else {
      throw error;
    }
  }
}

async searchSimilar(queryEmbedding: number[], options: RetrievalOptions): Promise<RetrievalResult> {
  try {
    // Try vector search
    return await this.vectorStore.search(queryEmbedding, options);
  } catch (error) {
    if (this.config.fallback.enableJSONStorage) {
      // Fall back to JSON pattern matching (slower, less accurate)
      console.warn('Vector search failed, falling back to JSON search', error);
      return await this.fallbackStorage.searchPatterns(options);
    } else {
      throw error;
    }
  }
}

---

4. Implementation Phases

Phase 1: Core Infrastructure (Week 1)

Goal: AgentDB service layer with embedding generation and vector storage.

Tasks:

1. Implement EmbeddingGenerator with all-MiniLM-L6-v2
2. Implement VectorStore with HNSW indexing
3. Implement AgentDBService facade with basic CRUD
4. Add fallback to JSON storage
5. Unit tests for all components

Deliverables:

• src/core/agentdb/EmbeddingGenerator.ts
• src/core/agentdb/VectorStore.ts
• src/core/agentdb/AgentDBService.ts
• tests/unit/agentdb/embedding-generator.test.ts
• tests/unit/agentdb/vector-store.test.ts
• tests/unit/agentdb/agentdb-service.test.ts

Success Criteria:

• ✅ Generate 384-dim embeddings in <5ms
• ✅ Vector search completes in <100µs
• ✅ Patterns stored in SQLite with embeddings
• ✅ Graceful fallback to JSON works
• ✅ 100% test coverage

---

Phase 2: Agent Integration (Week 2)

Goal: Integrate AgentDB into BaseAgent lifecycle hooks.

Tasks:

1. Modify BaseAgent.onPreTask() to load context from vector search
2. Modify BaseAgent.onPostTask() to store patterns with embeddings
3. Modify BaseAgent.onTaskError() to store error patterns
4. Add configuration loading from .agentic-qe/config/agentdb.json
5. Integration tests with real agents

Deliverables:

• Updated src/agents/BaseAgent.ts
• tests/integration/agentdb/base-agent-integration.test.ts
• tests/integration/agentdb/test-generator-integration.test.ts
• Example config: .agentic-qe/config/agentdb-example.json

Success Criteria:

• ✅ Agents load context in onPreTask (<2ms overhead)
• ✅ Agents store patterns in onPostTask (<5ms overhead)
• ✅ Error patterns captured and stored
• ✅ Zero breaking changes for existing agents
• ✅ E2E test passes with AgentDB enabled

---

Phase 3: QUIC Synchronization (Week 3)

Goal: Cross-agent pattern synchronization with QUIC protocol.

Tasks:

1. Implement QUICService with TLS 1.3
2. Implement peer discovery (manual + mDNS)
3. Implement sync worker with batching
4. Implement vector clock conflict resolution
5. Add QUIC monitoring and statistics

Deliverables:

• src/core/agentdb/QUICService.ts
• src/core/agentdb/ConflictResolver.ts
• tests/integration/agentdb/quic-sync.test.ts
• TLS certificate generation script: scripts/generate-tls-certs.sh

Success Criteria:

• ✅ QUIC server starts on port 4433
• ✅ Peers connect with TLS 1.3
• ✅ Pattern sync completes in <1ms (local network)
• ✅ Conflicts resolved correctly
• ✅ No data loss during network failures

---

Phase 4: Neural Training (Week 4)

Goal: RL-based strategy optimization using AgentDB's learning plugins.

Tasks:

1. Implement NeuralTrainer with PPO algorithm
2. Define reward function for AQE tasks
3. Implement experience collection from patterns
4. Add incremental training (every 100 patterns)
5. Add strategy prediction in onPreTask

Deliverables:

• src/core/agentdb/NeuralTrainer.ts
• src/core/agentdb/RewardCalculator.ts
• tests/integration/agentdb/neural-training.test.ts
• Training metrics dashboard: scripts/show-training-metrics.sh

Success Criteria:

• ✅ Model trains on historical patterns
• ✅ Training completes in <30s for 1000 patterns
• ✅ Strategy predictions improve accuracy by 20%+
• ✅ Model checkpoints saved correctly
• ✅ Reward function correlates with task success

---

Phase 5: Performance Optimization (Week 5)

Goal: Achieve claimed performance benchmarks.

Tasks:

1. Implement quantization (scalar, binary, product)
2. Optimize HNSW parameters (M, efConstruction, efSearch)
3. Add in-memory caching with LRU eviction
4. Optimize batch operations (10x faster than sequential)
5. Run performance benchmarks vs baseline

Deliverables:

• Updated VectorStore with quantization
• Benchmark suite: tests/benchmarks/agentdb-performance.bench.ts
• Performance report: docs/reports/AGENTDB-PERFORMANCE-REPORT.md

Success Criteria:

• ✅ Vector search: <100µs (150x faster than baseline)
• ✅ QUIC sync: <1ms latency (84% faster than 6.23ms)
• ✅ Memory usage: 4-32x reduction with quantization
• ✅ Pattern retrieval: <2ms end-to-end
• ✅ Throughput: 10k+ patterns/sec

---

Phase 6: Testing & Documentation (Week 6)

Goal: Comprehensive testing and production-ready documentation.

Tasks:

1. Unit tests (100% coverage)
2. Integration tests (all components)
3. E2E tests (full agent workflows)
4. Performance benchmarks
5. Update README with accurate claims
6. Write migration guide (v1.1.0 → v1.2.0)
7. API documentation

Deliverables:

• Complete test suite (100% coverage)
• Updated README.md with verified claims
• docs/guides/AGENTDB-MIGRATION-GUIDE.md
• docs/api/AGENTDB-API.md
• Example project: examples/agentdb-integration/

Success Criteria:

• ✅ All tests pass
• ✅ Benchmarks verify claimed performance
• ✅ Documentation accurate and complete
• ✅ Migration guide tested
• ✅ Example project runs successfully

---

5. Error Handling & Resilience

5.1 Failure Scenarios

Failure	Detection	Recovery	Impact
AgentDB package missing	Import error	Use JSON fallback	No vector search
SQLite DB corrupted	Open error	Create new DB	Lose historical patterns
Embedding model load fails	Download error	Use simple hash embedding	Lower quality search
HNSW index build fails	Memory error	Reduce maxElements, retry	Slower build
QUIC peer unreachable	Connection timeout	Retry with backoff	Delayed sync
TLS certificate invalid	Handshake error	Skip peer, log warning	Skip that peer
Neural training fails	Training error	Skip training, log	No strategy prediction
Disk full	Write error	Clear old patterns, retry	Lose old data
Out of memory	Allocation error	Reduce cache size	Slower perf

5.2 Graceful Degradation

Degradation Levels:

Level 0: Full functionality
  - AgentDB enabled
  - Vector search with HNSW
  - QUIC synchronization
  - Neural training active

Level 1: Vector search only
  - AgentDB enabled
  - Vector search with HNSW
  - No QUIC sync (peers unreachable)
  - No neural training (disabled)

Level 2: Basic storage
  - AgentDB enabled
  - No vector search (index failed)
  - No QUIC sync
  - No neural training
  - Store patterns in SQLite (no embeddings)

Level 3: JSON fallback
  - AgentDB disabled/failed
  - JSON file storage (v1.1.0 behavior)
  - No vector search
  - No QUIC sync
  - No neural training
  - All agents continue to work

Level 4: No storage
  - All storage failed
  - Agents run without memory
  - No pattern learning
  - Log errors

Implementation:

class AgentDBService {
  private degradationLevel: number = 0;

  async initialize(): Promise<void> {
    try {
      // Try Level 0: Full functionality
      await this.initializeAgentDB();
      await this.initializeVectorSearch();
      await this.initializeQUICSync();
      await this.initializeNeuralTraining();
      this.degradationLevel = 0;
    } catch (error) {
      console.warn('Full AgentDB initialization failed, degrading...', error);

      try {
        // Try Level 1: Vector search only
        await this.initializeAgentDB();
        await this.initializeVectorSearch();
        this.degradationLevel = 1;
      } catch (error) {
        console.warn('Vector search initialization failed, degrading...', error);

        try {
          // Try Level 2: Basic storage
          await this.initializeAgentDB();
          this.degradationLevel = 2;
        } catch (error) {
          console.warn('AgentDB initialization failed, using JSON fallback...', error);

          try {
            // Level 3: JSON fallback
            await this.initializeJSONFallback();
            this.degradationLevel = 3;
          } catch (error) {
            console.error('All storage failed, running without memory', error);
            this.degradationLevel = 4;
          }
        }
      }
    }

    console.info(`AgentDB initialized at degradation level ${this.degradationLevel}`);
  }
}

---

6. Security Considerations

6.1 QUIC/TLS Security

Requirements:

• TLS 1.3 mandatory (no fallback to older versions)
• Certificate-based authentication
• Peer verification (no self-signed certs in production)
• Encrypted payload (all pattern data encrypted)

Implementation:

// Generate TLS certificates
// scripts/generate-tls-certs.sh

#!/bin/bash
# Generate self-signed certificates for development
openssl req -x509 -newkey rsa:4096 -nodes \
  -keyout .agentic-qe/certs/key.pem \
  -out .agentic-qe/certs/cert.pem \
  -days 365 \
  -subj "/CN=aqe-agent"

# For production, use Let's Encrypt or internal CA

QUIC Configuration:

const quicConfig = {
  // TLS 1.3 only
  minVersion: 'TLSv1.3',
  maxVersion: 'TLSv1.3',

  // Certificate paths
  cert: fs.readFileSync('.agentic-qe/certs/cert.pem'),
  key: fs.readFileSync('.agentic-qe/certs/key.pem'),
  ca: fs.readFileSync('.agentic-qe/certs/ca.pem'),

  // Require client certificates (mutual TLS)
  requestCert: true,
  rejectUnauthorized: true,

  // Strong ciphers only
  ciphers: 'TLS_AES_256_GCM_SHA384:TLS_CHACHA20_POLY1305_SHA256',

  // Security flags
  honorCipherOrder: true,
  ecdhCurve: 'prime256v1'
};

6.2 Data Privacy

Concerns:

• Pattern data may contain sensitive information (code, credentials)
• Embeddings preserve semantic meaning
• QUIC sync broadcasts patterns to peers

Mitigations:

1. Scrub sensitive data before embedding:

   function sanitizeForEmbedding(text: string): string {
     // Remove API keys, passwords, tokens
     text = text.replace(/api[_-]?key\s*[:=]\s*['"]?[\w-]+['"]?/gi, 'API_KEY_REDACTED');
     text = text.replace(/password\s*[:=]\s*['"]?[\w-]+['"]?/gi, 'PASSWORD_REDACTED');
     text = text.replace(/token\s*[:=]\s*['"]?[\w-]+['"]?/gi, 'TOKEN_REDACTED');
   
     // Remove potential credentials
     text = text.replace(/[\w.-]+@[\w.-]+\.\w+/g, 'EMAIL_REDACTED');
     text = text.replace(/\d{3}-\d{2}-\d{4}/g, 'SSN_REDACTED');
   
     return text;
   }
   

2. Encrypt patterns before QUIC sync:

   // Payload encryption with AES-256-GCM
   async function encryptPattern(pattern: Pattern, peerPublicKey: string): Promise<Buffer> {
     const cipher = crypto.createCipheriv('aes-256-gcm', sharedSecret, iv);
     const encrypted = Buffer.concat([
       cipher.update(JSON.stringify(pattern)),
       cipher.final()
     ]);
     const tag = cipher.getAuthTag();
     return Buffer.concat([encrypted, tag]);
   }
   

3. Peer allowlist:

   // Only sync with trusted peers
   const trustedPeers = [
     '192.168.1.10:4433', // Development machine
     '192.168.1.11:4433'  // CI/CD server
   ];
   
   // Reject unknown peers
   quicService.onConnection((peer) => {
     if (!trustedPeers.includes(peer.address)) {
       console.warn(`Rejecting untrusted peer: ${peer.address}`);
       peer.close();
     }
   });
   

---

7. Monitoring & Observability

7.1 Metrics

AgentDB Metrics to Track:

interface AgentDBMetrics {
  // Vector search
  vectorSearchLatency: number[];      // Histogram of search times
  vectorSearchThroughput: number;     // Queries per second
  vectorIndexSize: number;            // HNSW index size in bytes
  vectorCacheHitRate: number;         // Cache hit percentage

  // Pattern storage
  patternsStored: number;             // Total patterns in DB
  patternsStoredPerMinute: number;    // Storage rate
  avgPatternSize: number;             // Average pattern size

  // QUIC synchronization
  quicPeersConnected: number;         // Active peer count
  quicSyncLatency: number[];          // Histogram of sync times
  quicPatternsSynced: number;         // Total synced patterns
  quicSyncErrors: number;             // Sync error count

  // Neural training
  neuralTrainingCount: number;        // Training iterations
  neuralTrainingLatency: number[];    // Training duration histogram
  neuralModelAccuracy: number;        // Current model accuracy
  neuralPredictionLatency: number[];  // Prediction time histogram

  // Errors
  embeddingErrors: number;            // Embedding generation failures
  storageErrors: number;              // Storage failures
  syncErrors: number;                 // Sync failures
  trainingErrors: number;             // Training failures
}

Metrics Collection:

// File: src/core/agentdb/MetricsCollector.ts

import { StatsD } from 'node-statsd';

export class MetricsCollector {
  private statsd: StatsD;

  constructor(config: { host: string; port: number }) {
    this.statsd = new StatsD(config);
  }

  recordVectorSearch(latency: number, cacheHit: boolean): void {
    this.statsd.timing('agentdb.vector_search.latency', latency);
    this.statsd.increment('agentdb.vector_search.count');
    if (cacheHit) {
      this.statsd.increment('agentdb.vector_search.cache_hit');
    }
  }

  recordPatternStore(latency: number, patternSize: number): void {
    this.statsd.timing('agentdb.pattern_store.latency', latency);
    this.statsd.gauge('agentdb.pattern_store.size', patternSize);
    this.statsd.increment('agentdb.pattern_store.count');
  }

  recordQuicSync(latency: number, patternCount: number, success: boolean): void {
    this.statsd.timing('agentdb.quic_sync.latency', latency);
    this.statsd.gauge('agentdb.quic_sync.pattern_count', patternCount);
    if (success) {
      this.statsd.increment('agentdb.quic_sync.success');
    } else {
      this.statsd.increment('agentdb.quic_sync.error');
    }
  }

  recordNeuralTraining(latency: number, loss: number, accuracy: number): void {
    this.statsd.timing('agentdb.neural_training.latency', latency);
    this.statsd.gauge('agentdb.neural_training.loss', loss);
    this.statsd.gauge('agentdb.neural_training.accuracy', accuracy);
    this.statsd.increment('agentdb.neural_training.count');
  }
}

7.2 Logging

Structured Logging:

// Use Winston for structured logging

import winston from 'winston';

const logger = winston.createLogger({
  level: 'info',
  format: winston.format.combine(
    winston.format.timestamp(),
    winston.format.json()
  ),
  defaultMeta: { service: 'agentdb' },
  transports: [
    new winston.transports.File({
      filename: '.agentic-qe/logs/agentdb-error.log',
      level: 'error'
    }),
    new winston.transports.File({
      filename: '.agentic-qe/logs/agentdb.log'
    })
  ]
});

// Example log entries
logger.info('Vector search completed', {
  agentId: 'test-generator-123',
  queryTime: 87,
  resultsCount: 10,
  cacheHit: true
});

logger.error('QUIC sync failed', {
  agentId: 'test-generator-123',
  peer: '192.168.1.10:4433',
  error: 'Connection timeout',
  retryAttempt: 3
});

---

8. Testing Strategy

8.1 Unit Tests

Coverage targets: 100% for all components

Test suites:

tests/unit/agentdb/
├── embedding-generator.test.ts    # Embedding generation
├── vector-store.test.ts           # HNSW indexing and search
├── quic-service.test.ts           # QUIC protocol
├── neural-trainer.test.ts         # RL training
├── agentdb-service.test.ts        # Facade layer
└── metrics-collector.test.ts      # Metrics collection

Example test:

// tests/unit/agentdb/embedding-generator.test.ts

describe('EmbeddingGenerator', () => {
  let generator: EmbeddingGenerator;

  beforeEach(async () => {
    generator = new EmbeddingGenerator({
      model: 'all-MiniLM-L6-v2',
      dimensions: 384,
      normalize: true
    });
    await generator.initialize();
  });

  it('should generate 384-dim embeddings', async () => {
    const embedding = await generator.generateEmbedding('test text');
    expect(embedding).toHaveLength(384);
  });

  it('should generate embeddings in <5ms', async () => {
    const start = Date.now();
    await generator.generateEmbedding('test text');
    const duration = Date.now() - start;
    expect(duration).toBeLessThan(5);
  });

  it('should normalize embeddings to unit vectors', async () => {
    const embedding = await generator.generateEmbedding('test text');
    const magnitude = Math.sqrt(embedding.reduce((sum, v) => sum + v * v, 0));
    expect(magnitude).toBeCloseTo(1.0, 5);
  });

  it('should generate similar embeddings for similar text', async () => {
    const emb1 = await generator.generateEmbedding('unit test generation');
    const emb2 = await generator.generateEmbedding('generate unit tests');
    const similarity = await generator.computeSimilarity(emb1, emb2);
    expect(similarity).toBeGreaterThan(0.8);
  });
});

8.2 Integration Tests

Test scenarios:

tests/integration/agentdb/
├── base-agent-integration.test.ts       # BaseAgent + AgentDB
├── test-generator-integration.test.ts   # TestGeneratorAgent + AgentDB
├── quic-sync-integration.test.ts        # Multi-agent QUIC sync
├── neural-training-integration.test.ts  # End-to-end training
└── fallback-integration.test.ts         # Graceful degradation

Example E2E test:

// tests/integration/agentdb/test-generator-integration.test.ts

describe('TestGeneratorAgent + AgentDB Integration', () => {
  let agent: TestGeneratorAgent;
  let agentDBService: AgentDBService;

  beforeEach(async () => {
    // Setup AgentDB
    agentDBService = new AgentDBService({
      dbPath: ':memory:',
      enableQUICSync: false,
      enableNeuralTraining: false
    });
    await agentDBService.initialize();

    // Setup agent with AgentDB
    agent = new TestGeneratorAgent({
      agentDBConfig: {
        enabled: true,
        service: agentDBService
      }
    });
    await agent.initialize();
  });

  it('should store patterns after task execution', async () => {
    // Execute task
    const task: QETask = {
      type: 'test-generation',
      description: 'Generate unit tests for UserService',
      requirements: {}
    };

    await agent.assignTask(task);

    // Verify pattern stored
    const stats = await agentDBService.getStats();
    expect(stats.totalPatterns).toBe(1);
  });

  it('should load context before task execution', async () => {
    // Store a pattern first
    await agentDBService.storePattern({
      id: 'pattern-1',
      type: 'success',
      agentId: 'test-gen-1',
      agentType: 'test-generator',
      data: {
        taskType: 'test-generation',
        taskDescription: 'Generate unit tests',
        result: { success: true },
        context: {},
        metrics: {
          executionTime: 5000,
          success: true,
          confidence: 0.9
        }
      },
      embedding: new Array(384).fill(0.1),
      confidence: 0.9,
      usageCount: 0,
      successCount: 1,
      createdAt: Date.now(),
      lastUsed: Date.now(),
      tags: ['test-generation', 'success']
    });

    // Execute similar task
    const task: QETask = {
      type: 'test-generation',
      description: 'Generate unit tests for ProductService',
      requirements: {}
    };

    const spy = jest.spyOn(agentDBService, 'searchSimilar');
    await agent.assignTask(task);

    // Verify context loaded
    expect(spy).toHaveBeenCalled();
  });
});

8.3 Performance Benchmarks

Benchmark suite:

// tests/benchmarks/agentdb-performance.bench.ts

import Benchmark from 'benchmark';

const suite = new Benchmark.Suite();

// Vector search benchmark
suite.add('Vector search (k=10)', async () => {
  const queryEmbedding = new Array(384).fill(Math.random());
  await vectorStore.search(queryEmbedding, 10);
});

// Pattern storage benchmark
suite.add('Pattern storage', async () => {
  const pattern = createTestPattern();
  await agentDBService.storePattern(pattern);
});

// QUIC sync benchmark
suite.add('QUIC sync (100 patterns)', async () => {
  const patterns = Array.from({ length: 100 }, createTestPattern);
  await quicService.syncBatch(patterns);
});

// Neural training benchmark
suite.add('Neural training (1000 patterns)', async () => {
  await neuralTrainer.train();
});

suite
  .on('cycle', (event: any) => {
    console.log(String(event.target));
  })
  .on('complete', function() {
    console.log('Fastest is ' + this.filter('fastest').map('name'));
  })
  .run({ async: true });

Performance targets:

Operation	Target	Baseline (JSON)	Speedup
Vector search (k=10)	<100µs	15ms	150x
Pattern storage	<5ms	8ms	1.6x
QUIC sync (100 patterns)	<1ms	6.23ms	6.2x
Neural training (1000 patterns)	<30s	5min	10x
Pattern retrieval	<2ms	5ms	2.5x

---

9. API Reference

9.1 AgentDBService API

Full API documentation: docs/api/AGENTDB-API.md

Quick reference:

// Initialize
const service = new AgentDBService(config);
await service.initialize();

// Store pattern
const patternId = await service.storePattern(pattern);

// Search similar patterns
const result = await service.searchSimilar(queryEmbedding, {
  k: 10,
  minConfidence: 0.6,
  useMMR: true
});

// Neural training
const metrics = await service.train();

// QUIC sync
await service.syncWithPeers([pattern]);

// Statistics
const stats = await service.getStats();

// Health check
const health = await service.healthCheck();

// Cleanup
await service.close();

---

10. Deployment & Operations

10.1 Installation

# Install dependencies
npm install @xenova/transformers hnswlib-node

# Initialize AgentDB
npx aqe init --enable-agentdb

# Generate TLS certificates (development)
npm run agentdb:generate-certs

# Start QUIC server
npm run agentdb:start-quic

10.2 Configuration

Minimal configuration (.agentic-qe/config/agentdb.json):

{
  "enabled": true,
  "dbPath": ".agentic-qe/agentdb/patterns.db"
}

Production configuration:

{
  "enabled": true,
  "dbPath": ".agentic-qe/agentdb/patterns.db",

  "embedding": {
    "model": "all-MiniLM-L6-v2",
    "dimensions": 384
  },

  "vectorSearch": {
    "hnswM": 16,
    "hnswEfConstruction": 200,
    "hnswEfSearch": 100,
    "cacheSize": 1000,
    "quantizationType": "scalar"
  },

  "quic": {
    "enabled": true,
    "port": 4433,
    "peers": ["agent-1.local:4433", "agent-2.local:4433"],
    "tlsCert": ".agentic-qe/certs/cert.pem",
    "tlsKey": ".agentic-qe/certs/key.pem",
    "syncInterval": 1000,
    "batchSize": 100
  },

  "neural": {
    "enabled": true,
    "algorithm": "ppo",
    "trainingInterval": 100,
    "epochs": 10,
    "batchSize": 32
  },

  "fallback": {
    "enableJSONStorage": true,
    "logErrors": true
  }
}

10.3 Operations

Monitoring:

# View AgentDB status
npx aqe agentdb status

# View QUIC peers
npx aqe agentdb peers

# View training metrics
npx aqe agentdb training

# View performance stats
npx aqe agentdb stats

Maintenance:

# Rebuild HNSW index
npx aqe agentdb rebuild-index

# Backup database
npx aqe agentdb backup

# Restore from backup
npx aqe agentdb restore <backup-file>

# Prune old patterns (>30 days)
npx aqe agentdb prune --days 30

---

11. Migration Guide (v1.1.0 → v1.2.0)

Step 1: Update dependencies

npm install agentic-qe@1.2.0
npm install @xenova/transformers hnswlib-node

Step 2: Enable AgentDB

npx aqe init --enable-agentdb

Step 3: Configure AgentDB

Edit .agentic-qe/config/agentdb.json:

{
  "enabled": true,
  "quic": { "enabled": false },
  "neural": { "enabled": false }
}

Step 4: Run agents

npx aqe run

Step 5: Verify data

# Check patterns stored
sqlite3 .agentic-qe/agentdb/patterns.db "SELECT COUNT(*) FROM patterns"

# Should show: > 0

Step 6: (Optional) Enable QUIC sync

# Generate certificates
npm run agentdb:generate-certs

# Configure peers
# Edit .agentic-qe/config/agentdb.json:
{
  "quic": {
    "enabled": true,
    "peers": ["peer-host:4433"]
  }
}

Step 7: (Optional) Enable neural training

# Edit .agentic-qe/config/agentdb.json:
{
  "neural": {
    "enabled": true,
    "algorithm": "ppo"
  }
}

Rollback (if issues):

# Disable AgentDB
# Edit .agentic-qe/config/agentdb.json:
{
  "enabled": false,
  "fallback": { "enableJSONStorage": true }
}

# Agents will use v1.1.0 JSON storage

---

12. Appendix

A. Design Decisions Summary

Decision	Options Considered	Choice	Rationale
Embedding model	OpenAI, Cohere, local BERT	all-MiniLM-L6-v2	Balance of quality, speed, offline support
Vector database	Pinecone, Milvus, HNSW	AgentDB + HNSW	Embedded, no external dependencies
QUIC library	node:quic, quic-native	node:quic	Built-in Node.js support (v16+)
RL algorithm	Q-learning, PPO, SAC	PPO	Stability and sample efficiency
Conflict resolution	CRDTs, OT, vector clock	Vector clock	Simple, proven for key-value stores
Fallback strategy	Fail hard, partial, full	Full graceful degradation	Zero breaking changes

B. Performance Targets

Metric	Target	v1.1.0 Baseline	Improvement
Vector search	<100µs	15ms	150x
QUIC sync	<1ms	6.23ms	6.2x (84%)
Pattern retrieval	<2ms	5ms	2.5x
Memory usage	4-32x reduction	N/A	Quantization
Neural training	<30s	5min	10x

C. Dependencies

{
  "dependencies": {
    "@xenova/transformers": "^2.6.0",
    "hnswlib-node": "^1.4.2",
    "sqlite3": "^5.1.6",
    "msgpack": "^1.0.3",
    "winston": "^3.10.0"
  },
  "optionalDependencies": {
    "node-statsd": "^0.1.1"
  }
}

D. File Structure

src/core/agentdb/
├── AgentDBService.ts           # Main facade (500 lines)
├── EmbeddingGenerator.ts       # Embedding generation (300 lines)
├── VectorStore.ts              # HNSW indexing (400 lines)
├── QUICService.ts              # QUIC sync (600 lines)
├── NeuralTrainer.ts            # RL training (700 lines)
├── MetricsCollector.ts         # Monitoring (200 lines)
├── ConflictResolver.ts         # Vector clock (150 lines)
└── index.ts                    # Exports

tests/
├── unit/agentdb/               # Unit tests
├── integration/agentdb/        # Integration tests
└── benchmarks/                 # Performance benchmarks

docs/
├── architecture/
│   └── AGENTDB-INTEGRATION-ARCHITECTURE.md (this file)
├── api/
│   └── AGENTDB-API.md
└── guides/
    └── AGENTDB-MIGRATION-GUIDE.md

---

Conclusion

This architecture provides a complete blueprint for integrating AgentDB into AQE agents with:

1. Clear component boundaries and interfaces
2. Graceful degradation for backward compatibility
3. Production-ready security (TLS 1.3, encryption)
4. Comprehensive error handling and resilience
5. Performance targets with benchmarking strategy
6. Phased implementation plan (6 weeks)

Next Steps:

1. Review and approve architecture
2. Assign implementation teams
3. Begin Phase 1 (Core Infrastructure)

Ready for implementation: ✅