when-optimizing-agent-learning-use-reasoningbank-intelligence

# ReasoningBank Intelligence - Adaptive Agent Learning ## Overview Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing decision-making, or implementing meta-cognitive systems. ## When to Use - Agent performance needs improvement - Repetitive tasks require optimization - Need pattern recognition from experience - Strategy refinement through learning - Building self-improving systems - Meta-cognitive capabilities needed ## Theoretical Foundation ### ReasoningBank Architecture 1. **Trajectory Tracking**: Record decision paths and outcomes 2. **Verdict Judgment**: Evaluate success/failure of strategies 3. **Memory Distillation**: Extract patterns from experience 4. **Pattern Recognition**: Identify successful approaches 5. **Strategy Optimization**: Apply learned patterns to new situations ### AgentDB Integration (Optional) - 150x faster vector operations - HNSW indexing for similarity search - Quantization for memory efficiency - Batch operations for performance ## Phase 1: Initialize Learning System (10 min) ### Objective Set up ReasoningBank with trajectory tracking ### Agent: ML-Developer **Step 1.1: Initialize ReasoningBank** ```javascript const ReasoningBank = require('reasoningbank'); const learningSystem = new ReasoningBank({ storage: { type: 'agentdb', // Or 'memory', 'disk' path: './reasoning-bank-data', quantization: 'int...