proffesor-for-testing__agentic-qe/docs/sona-lifecycle-integration.md

SONA Lifecycle Integration - Phase 2 Implementation

Overview

Phase 2 of SONA integration wires SONA's self-organizing capabilities into the agent execution lifecycle. This enables continuous learning and adaptation as agents execute tasks in the QE fleet.

Implementation Summary

Files Created

1. src/agents/SONALifecycleManager.ts (NEW)

   • Core lifecycle manager for SONA integration
   • Manages per-agent SONA contexts
   • Coordinates lifecycle hooks and learning consolidation
   • 717 lines of production code

2. tests/unit/agents/SONALifecycleManager.test.ts (NEW)

   • Comprehensive unit tests for lifecycle manager
   • 56 tests covering all public methods and error cases
   • Tests for LoRA adapter mapping, consolidation logic, singleton pattern

3. tests/integration/pipelines/AgentRegistry-SONA-Router.test.ts (NEW)

   • Integration tests for full AgentRegistry → SONA → Router pipeline
   • 16 tests covering end-to-end workflows

Files Modified

1. src/mcp/services/AgentRegistry.ts

   • Added SONA lifecycle integration
   • Wired hooks into spawn, task execution, and termination
   • Added methods for SONA metrics and training
   • ~100 lines of changes

2. src/agents/index.ts

   • Exported SONALifecycleManager and related types
   • ~15 lines of changes

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      AgentRegistry                           │
│  ┌───────────────────────────────────────────────────────┐  │
│  │  spawnAgent()                                         │  │
│  │    └─> onAgentSpawn() ──────────────────────┐       │  │
│  │                                               │       │  │
│  │  executeTask()                                │       │  │
│  │    └─> onTaskComplete() ─────────────┐       │       │  │
│  │                                       │       │       │  │
│  │  terminateAgent()                     │       │       │  │
│  │    └─> cleanupAgent() ────────┐      │       │       │  │
│  └────────────────────────────────┼──────┼───────┼───────┘  │
└─────────────────────────────────┼┼──────┼───────┼───────────┘
                                  ││      │       │
                                  ││      │       │
                           ┌──────▼▼──────▼───────▼──────────┐
                           │  SONALifecycleManager            │
                           │  ┌────────────────────────────┐  │
                           │  │ AgentSONAContext Map       │  │
                           │  │  - strategy                │  │
                           │  │  - feedbackLoop            │  │
                           │  │  - activeAdapter           │  │
                           │  │  - pendingPatterns         │  │
                           │  └────────────────────────────┘  │
                           │                                  │
                           │  Hooks:                          │
                           │  • onAgentSpawn()               │
                           │  • onTaskComplete()             │
                           │  • onFeedback()                 │
                           │  • cleanupAgent()               │
                           └──────────────────────────────────┘
                                         │
                   ┌─────────────────────┼─────────────────────┐
                   │                     │                     │
          ┌────────▼───────┐   ┌────────▼─────────┐  ┌───────▼──────┐
          │ SONALearning   │   │ SONAFeedback     │  │ LoRA Adapter │
          │ Strategy       │   │ Loop             │  │ Manager      │
          │  - MicroLoRA   │   │  - Analysis      │  │  - Activate  │
          │  - BaseLoRA    │   │  - Adaptation    │  │  - Switch    │
          │  - EWC++       │   │  - Consolidation │  │              │
          └────────────────┘   └──────────────────┘  └──────────────┘

Key Features Implemented

1. Agent Spawn Hook - onAgentSpawn()

Purpose: Initialize SONA state when agent spawns

Implementation:

• Creates SONALearningStrategy with adaptive configuration
• Initializes SONAFeedbackLoop for continuous learning
• Activates relevant LoRA adapter based on agent type
• Creates AgentSONAContext to track agent's learning state

LoRA Adapter Mapping:

TEST_GENERATOR → 'test-generation'
COVERAGE_ANALYZER → 'coverage-analysis'
PERFORMANCE_TESTER → 'performance-testing'
SECURITY_SCANNER → 'security-scanning'
... (20 agent types mapped)

2. Task Complete Hook - onTaskComplete()

Purpose: Collect feedback and trigger consolidation on successful patterns

Implementation:

• Records execution feedback via SONAFeedbackLoop
• Tracks success/failure metrics
• Checks consolidation triggers (every N successful tasks)
• Triggers EWC++ weight consolidation when threshold reached

Consolidation Logic:

// Consolidate every 100 successful tasks (configurable)
if (context.successfulTasks % consolidationInterval === 0) {
  // Store pending patterns
  // Trigger training
  // Update EWC weights
}

// Also consolidate if pending patterns >= threshold
if (context.pendingPatterns.length >= consolidationInterval) {
  consolidateWeights(context)
}

3. Feedback Hook - onFeedback()

Purpose: Generic feedback recording for any execution event

Implementation:

• Records FeedbackEvent via feedback loop or strategy
• Stores successful patterns for later consolidation
• Creates LearnedPattern objects from execution data
• Accumulates patterns in pendingPatterns queue

4. EWC Weight Consolidation

Purpose: Prevent catastrophic forgetting while learning new patterns

Implementation:

async consolidateWeights(context: AgentSONAContext) {
  // 1. Check success rate threshold (default: 70%)
  if (successRate < minSuccessRateForConsolidation) {
    return; // Skip if performance is poor
  }

  // 2. Store all pending patterns
  for (const pattern of context.pendingPatterns) {
    await context.strategy.storePattern(pattern);
  }

  // 3. Trigger training (5 iterations)
  const result = await context.strategy.train(5);

  // 4. Update consolidation timestamp
  context.lastConsolidation = new Date();
  context.pendingPatterns = [];
}

5. LoRA Adapter Management

Purpose: Activate task-specific adapters for efficient learning

Implementation:

• Determines adapter based on agent type
• Activates adapter during agent spawn
• Future: Adaptive adapter switching based on performance

Adapter Types:

• MicroLoRA (rank 1-2): Instant adaptation for hot paths
• BaseLoRA (rank 4-16): Long-term pattern consolidation

Configuration

interface SONALifecycleConfig {
  /** Enable SONA lifecycle integration */
  enabled?: boolean; // default: true

  /** Enable automatic EWC consolidation */
  autoConsolidate?: boolean; // default: true

  /** Consolidation interval (successful tasks) */
  consolidationInterval?: number; // default: 100

  /** Enable LoRA adapter management */
  enableLoraAdapters?: boolean; // default: true

  /** SONA learning configuration */
  sonaConfig?: SONALearningConfig;

  /** Feedback loop configuration */
  feedbackConfig?: FeedbackLoopConfig;

  /** Minimum success rate for consolidation */
  minSuccessRateForConsolidation?: number; // default: 0.7

  /** Enable performance-based adapter selection */
  enableAdaptiveAdapters?: boolean; // default: true
}

Usage

Basic Setup

import { AgentRegistry } from './mcp/services/AgentRegistry';

// Create registry with SONA lifecycle enabled
const registry = new AgentRegistry({
  enableSONALifecycle: true,
  sonaLifecycleConfig: {
    consolidationInterval: 100,
    minSuccessRateForConsolidation: 0.7,
  }
});

// Spawn agent (automatically initializes SONA)
const { id, agent } = await registry.spawnAgent('test-generator', {
  name: 'Test Generator 1',
});

// Execute task (automatically records feedback)
const result = await registry.executeTask(id, {
  taskType: 'generate-unit-tests',
  payload: { file: 'UserService.ts' }
});

// Get SONA metrics
const metrics = await registry.getSONAMetrics(id);
console.log(metrics);
// {
//   microLoraAdaptations: 45,
//   baseLoraConsolidations: 2,
//   ewcRetentionRate: 0.95,
//   trajectoriesRecorded: 100,
//   ...
// }

Manual Feedback Recording

import { FeedbackEvent } from './learning/SONAFeedbackLoop';

// Record custom feedback
const event: FeedbackEvent = {
  task: myTask,
  success: true,
  duration: 1500,
  quality: 0.92,
  patternsUsed: ['pattern-123', 'pattern-456'],
  timestamp: new Date(),
};

await registry.recordFeedback(agentId, event);

Manual Training

// Trigger training manually
const result = await registry.trainAgent(agentId, 10);
console.log(result);
// {
//   iterations: 10,
//   improvement: 0.15,
//   patternsLearned: 23,
//   duration: 2345,
//   metrics: { accuracy: 0.89, loss: 0.11, ... }
// }

Get Statistics

// Get lifecycle statistics
const stats = registry.getSONAStatistics();
console.log(stats);
// {
//   enabled: true,
//   totalAgents: 15,
//   totalSuccessfulTasks: 1523,
//   totalFailedTasks: 47,
//   averageSuccessRate: 0.97,
//   totalConsolidations: 15,
//   ruvLLMAvailable: true
// }

Fallback Mode

When ruvLLM is not available, SONA operates in fallback mode:

• Reduced LoRA ranks (MicroLoRA: 1, BaseLoRA: 4)
• More frequent consolidation (interval: 50 instead of 100)
• Lower pattern limit (5000 instead of 10000)
• Core pattern learning still functional

Performance Characteristics

Time Complexity

• onAgentSpawn(): O(1) - Initialization
• onTaskComplete(): O(1) amortized - Most calls just record
• consolidateWeights(): O(n) where n = pending patterns - Triggered periodically
• onFeedback(): O(1) - Fast recording

Space Complexity

• Per-agent context: O(1) fixed overhead
• Pending patterns: O(n) where n = consolidation interval
• Total: O(agents * consolidation_interval)

Consolidation Triggers

• Every N successful tasks (default: 100)
• When pending patterns >= threshold
• Manual via trainAgent() call
• Only when success rate >= threshold (default: 70%)

Testing

Unit Tests

• Test each lifecycle hook independently
• Mock SONA components
• Verify consolidation logic
• Test fallback mode

Integration Tests

• Spawn agents and execute tasks
• Verify SONA contexts created
• Verify feedback recorded
• Verify consolidation triggered
• Verify metrics collected

Future Enhancements

1. Adaptive Adapter Selection

   • Switch adapters based on performance
   • Monitor quality trends
   • Automatic adapter optimization

2. Cross-Agent Learning

   • Share successful patterns between agents
   • Collaborative learning
   • Fleet-wide consolidation

3. Advanced Consolidation

   • More sophisticated triggers
   • Multi-level consolidation
   • Hierarchical pattern storage

4. Performance Monitoring

   • Detailed metrics dashboard
   • Learning rate visualization
   • Adapter performance tracking

Related Files

• Core SONA: src/core/strategies/SONALearningStrategy.ts
• Feedback Loop: src/learning/SONAFeedbackLoop.ts
• Integration Helper: src/agents/SONAIntegration.ts
• Agent Registry: src/mcp/services/AgentRegistry.ts
• Base Agent: src/agents/BaseAgent.ts

References

• SONA Learning Strategy Documentation
• Feedback Loop Documentation
• Agent Registry Documentation
• GitHub Issue #144 - Phase 2

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Generated by: Agentic QE Fleet v2.5.4 Implementation Date: 2025-12-15 Author: Claude Opus 4.5 (Code Implementation Agent)