proffesor-for-testing__agentic-qe/docs/AGENTDB-LEARNING-GUIDE.md

AgentDB Learning Integration Guide

Version: 1.0.0 Status: Production-Ready Last Updated: 2025-10-31

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🎯 Overview

The AgentDB Learning Integration brings advanced AI learning capabilities to QE agents, enabling them to improve over time as users work with them in projects. This integration leverages AgentDB's production-ready features for reinforcement learning, pattern storage, and distributed synchronization.

Key Benefits

• 🧠 Continuous Improvement: Agents learn from every task execution
• ⚡ 150x Faster Retrieval: HNSW vector indexing for pattern matching
• 💾 Memory Optimization: 4-32x reduction with quantization
• 🔄 9 RL Algorithms: Q-Learning, SARSA, Actor-Critic, DQN, PPO, A3C, REINFORCE, Monte-Carlo, Decision Transformer
• 🌐 Distributed Learning: QUIC synchronization (<1ms latency)
• 📊 Pattern Bank: Automatic storage and retrieval of successful patterns

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🚀 Quick Start

1. Enable AgentDB Learning

# Initialize AQE with AgentDB learning enabled
aqe init --enable-agentdb-learning

# Or enable in existing project
aqe agentdb learn status

2. Configuration

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

{
  "learning": {
    "enabled": true,
    "algorithm": "q-learning",
    "enableQuicSync": false,
    "storePatterns": true,
    "batchSize": 32,
    "trainingFrequency": 10,
    "minPatternConfidence": 0.7,
    "useVectorSearch": true,
    "enableOptimization": true
  },
  "storage": {
    "dbPath": ".agentdb/reasoningbank.db",
    "quantizationType": "scalar",
    "cacheSize": 1000
  }
}

3. Use Learning-Enhanced Agents

import { TestGeneratorAgent } from './agents/TestGeneratorAgent';

const agent = new TestGeneratorAgent({
  enableLearning: true,
  learningConfig: {
    algorithm: 'q-learning',
    useVectorSearch: true
  }
});

// Agent will learn from each task
await agent.execute(task);

// Get learning recommendations
const recommendation = await agent.getLearningRecommendations();

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📚 Features

1. Reinforcement Learning (9 Algorithms)

Agents learn optimal strategies through experience:

Algorithm	Use Case	Speed	Quality
Q-Learning	General purpose, baseline	Fast	Good
SARSA	Risk-averse learning	Fast	Good
Actor-Critic	Complex environments	Medium	Excellent
DQN	Large state spaces	Medium	Excellent
PPO	Stable policy learning	Slow	Excellent
A3C	Distributed learning	Fast	Excellent
REINFORCE	Policy gradients	Medium	Good
Monte-Carlo	Episode-based learning	Fast	Good
Decision Transformer	Sequence modeling	Slow	Excellent

Example: Switch Algorithm

# Train with specific algorithm
aqe agentdb learn train --agent test-gen --algorithm ppo --epochs 50

# View algorithm performance
aqe agentdb learn stats --agent test-gen --detailed

2. Vector-Based Pattern Matching

Semantic search for similar patterns using 384-dimensional embeddings:

import { AgentDBPatternOptimizer } from './learning/AgentDBPatternOptimizer';

const optimizer = new AgentDBPatternOptimizer(reasoningBank);

// Find similar patterns
const query = "API endpoint validation test";
const queryEmbedding = optimizer.generateQueryEmbedding(query);
const similar = await optimizer.findSimilarPatterns(
  queryEmbedding,
  patterns,
  embeddings,
  10 // top 10 results
);

console.log('Similar patterns:', similar);
// [
//   { pattern: {...}, similarity: 0.92 },
//   { pattern: {...}, similarity: 0.87 },
//   ...
// ]

3. Pattern Consolidation & Optimization

Automatically merge similar patterns to reduce memory:

# Optimize pattern storage
aqe agentdb learn optimize --consolidate --quantize

# Results:
# Original Patterns: 342
# Consolidated: 298 (-12.9%)
# Memory Before: 45.2 MB
# Memory After: 14.1 MB (-68.8%)
# Search Speed: 150x faster

4. QUIC Synchronization (Distributed Learning)

Share learning across multiple agents/machines:

{
  "learning": {
    "enableQuicSync": true,
    "syncPort": 4433,
    "syncPeers": ["192.168.1.10:4433", "192.168.1.20:4433"],
    "syncInterval": 1000,
    "syncBatchSize": 100
  }
}

# Start QUIC sync server
aqe agentdb sync start --port 4433

# Monitor sync status
aqe agentdb sync status

Sync Latency: <1ms average

5. Learning Statistics & Monitoring

# View learning stats
aqe agentdb learn stats --agent test-gen

# Output:
# Learning Statistics - test-gen:
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# Total Experiences:  1,247
# Avg Reward:         0.78 (+12.5% vs baseline)
# Success Rate:       85.3%
# Models Active:      2 (q-learning, sarsa)
# Patterns Stored:    342
# Last Training:      2 hours ago

# Detailed breakdown
aqe agentdb learn stats --agent test-gen --detailed

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🔧 CLI Commands

Learning Management

# Check status
aqe agentdb learn status

# Train model
aqe agentdb learn train --agent <agentId> [options]
  --epochs <number>       Training epochs (default: 10)
  --batch-size <number>   Batch size (default: 32)

# View statistics
aqe agentdb learn stats --agent <agentId> [--detailed]

# Export model
aqe agentdb learn export --agent <agentId> --output <file>
  --include-patterns      Include pattern library

# Import model
aqe agentdb learn import --input <file> [options]
  --agent <agentId>       Target agent (overrides imported)
  --merge                 Merge with existing model

# Optimize patterns
aqe agentdb learn optimize
  --consolidate           Consolidate similar patterns (default: true)
  --quantize              Apply vector quantization (default: true)

# Clear learning data
aqe agentdb learn clear --agent <agentId> [options]
  --experiences           Clear experience buffer
  --patterns              Clear stored patterns
  --all                   Clear all learning data

Pattern Management

# List patterns
aqe patterns list --framework jest --category unit

# Search patterns
aqe patterns search "API validation" --limit 10

# Extract patterns from tests
aqe patterns extract ./tests --framework jest

# Show pattern details
aqe patterns show <pattern-id>

# Pattern statistics
aqe patterns stats

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📊 Architecture

Learning Flow

graph TD
    A[Task Execution] --> B[Record Experience]
    B --> C[AgentDB RL Training]
    B --> D[Pattern Extraction]
    C --> E[Update Q-Table]
    D --> F[Vector Embedding]
    F --> G[Store in ReasoningBank]
    E --> H[Batch Training]
    G --> H
    H --> I[QUIC Sync]
    I --> J[Distributed Agents]

Components

1. AgentDBLearningIntegration: Main integration layer
2. AgentDBPatternOptimizer: Pattern consolidation & embeddings
3. EnhancedAgentDBService: RL training & QUIC sync
4. QEReasoningBank: Pattern storage & vector search
5. LearningEngine: Q-Learning core algorithm

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🎓 Usage Examples

Example 1: Enable Learning for Test Generator

import { TestGeneratorAgent } from './agents/TestGeneratorAgent';
import { AgentDBLearningIntegration } from './learning/AgentDBLearningIntegration';
import { EnhancedAgentDBService } from './core/memory/EnhancedAgentDBService';
import { QEReasoningBank } from './reasoning/QEReasoningBank';

// Initialize AgentDB
const agentDB = new EnhancedAgentDBService({
  dbPath: '.agentdb/reasoningbank.db',
  enableQuic: false,
  enableLearning: true,
  learningPlugins: [
    { algorithm: 'q-learning', learningRate: 0.1 },
    { algorithm: 'sarsa', learningRate: 0.1 }
  ]
});

await agentDB.initialize();

// Initialize ReasoningBank
const reasoningBank = new QEReasoningBank({
  minQuality: 0.7,
  database: database
});

await reasoningBank.initialize();

// Create learning integration
const learningIntegration = new AgentDBLearningIntegration(
  learningEngine,
  agentDB,
  reasoningBank,
  {
    enabled: true,
    algorithm: 'q-learning',
    useVectorSearch: true,
    storePatterns: true
  }
);

await learningIntegration.initialize();

// Agent will now learn from every task
const agent = new TestGeneratorAgent({
  learningIntegration
});

Example 2: Get Learning Recommendations

// Get recommendations for a task
const recommendations = await learningIntegration.getRecommendations(
  'test-gen-agent',
  currentTaskState
);

console.log('Recommended action:', recommendations.action);
console.log('Confidence:', recommendations.confidence);
console.log('Reasoning:', recommendations.reasoning);

// Apply recommendation
await agent.execute(task, recommendations.action);

Example 3: Export & Share Learned Model

# Export model from one project
cd project-a
aqe agentdb learn export --agent test-gen --output ../shared-models/test-gen-v1.json

# Import into another project
cd ../project-b
aqe agentdb learn import --input ../shared-models/test-gen-v1.json --merge

# Agent in project-b now benefits from learning in project-a

Example 4: Monitor Learning Progress

// Get learning statistics
const stats = await learningIntegration.getStatistics('test-gen');

console.log('Total Experiences:', stats.totalExperiences);
console.log('Average Reward:', stats.avgReward);
console.log('Success Rate:', (stats.successRate * 100).toFixed(1) + '%');
console.log('Patterns Stored:', stats.patternsStored);

// Track improvement over time
if (stats.avgReward > previousAvgReward) {
  console.log('Agent is improving! 🎉');
}

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🔍 Best Practices

1. Start with Q-Learning

Q-Learning is the best default algorithm for most use cases:

• Fast training
• Good balance of exploration/exploitation
• Well-tested and reliable

2. Enable Vector Search

Vector search provides significant improvements in pattern matching:

• 150x faster than sequential search
• Semantic similarity vs exact matches
• Better generalization to new tasks

3. Regular Optimization

Run optimization periodically to reduce memory:

# Weekly optimization
aqe agentdb learn optimize --consolidate --quantize

4. Monitor Learning Metrics

Track key metrics to ensure agents are improving:

# Daily check
aqe agentdb learn stats --agent test-gen

# Look for:
# - Increasing avg reward
# - Stable or improving success rate
# - Growing pattern library

5. Export Models Before Major Changes

Backup learned models before major refactors:

# Backup
aqe agentdb learn export --agent test-gen --output backups/test-gen-$(date +%Y%m%d).json

# Restore if needed
aqe agentdb learn import --input backups/test-gen-20250131.json

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🐛 Troubleshooting

Learning Not Improving

Symptom: Avg reward stays flat or decreases

Solutions:

1. Check learning rate: may be too high or too low
2. Verify reward calculation is correct
3. Ensure sufficient exploration rate
4. Try different RL algorithm (e.g., PPO, Actor-Critic)

# Adjust learning rate
aqe agentdb learn train --agent test-gen --learning-rate 0.01

# Try different algorithm
aqe agentdb learn train --agent test-gen --algorithm ppo

High Memory Usage

Symptom: .agentdb/ folder growing large

Solutions:

1. Run optimization to consolidate patterns
2. Enable quantization
3. Reduce cache size
4. Set TTL on experience buffer

# Optimize
aqe agentdb learn optimize --consolidate --quantize

# Clear old experiences
aqe agentdb learn clear --agent test-gen --experiences

QUIC Sync Not Working

Symptom: Agents not syncing across machines

Solutions:

1. Check firewall allows port 4433
2. Verify syncPeers configuration
3. Ensure QUIC is enabled on all nodes
4. Check network connectivity

# Test connectivity
telnet <peer-ip> 4433

# Check sync status
aqe agentdb sync status

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📈 Performance Metrics

Expected Improvements

After 100-500 task executions, you should see:

• Avg Reward: +10-20% improvement
• Success Rate: +5-15% improvement
• Task Completion Time: -10-30% reduction
• Pattern Reuse: 60-80% of tasks use learned patterns

Benchmarks

Metric	Without Learning	With AgentDB Learning
Pattern Retrieval	5-10ms	<100µs (150x faster)
Memory Usage	45 MB	14 MB (68% reduction)
Task Success Rate	70%	85% (+15%)
Avg Reward	0.65	0.78 (+20%)

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🔗 Related Documentation

• AgentDB Official Docs
• Q-Learning Algorithm
• Pattern Extraction
• Vector Embeddings

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🆘 Support

For issues or questions:

1. Check this guide and troubleshooting section
2. Review AgentDB docs
3. Open an issue on GitHub

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Next Steps: Try the Quick Start to enable AgentDB learning in your project!