v3-qe-agentic-flow-integration skill

---
name: "V3 QE Agentic-Flow Integration"
description: "Deep integration with agentic-flow@alpha for SONA learning, Flash Attention (2.49x-7.47x speedup), and 9 RL algorithms."
version: "3.0.0"
---

# V3 QE Agentic-Flow Integration

## Purpose

Guide deep integration with agentic-flow@alpha for QE operations, leveraging SONA (Self-Optimizing Neural Architecture), Flash Attention for speedups, and 9 reinforcement learning algorithms for intelligent QE decisions.

## Activation

- When implementing SONA learning for QE
- When optimizing QE with Flash Attention
- When using RL algorithms for QE decisions
- When reducing code duplication with agentic-flow
- When implementing cross-domain knowledge transfer

## Architecture

```
┌─────────────────────────────────────────────────────────────┐
│              V3 QE Agentic-Flow Integration                  │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌─────────────────────────────────────────────────────┐    │
│  │                  SONA Layer                          │    │
│  │  • Self-optimizing neural routing                   │    │
│  │  • Pattern adaptation <0.05ms                       │    │
│  │  • Cross-domain knowledge transfer                  │    │
│  └─────────────────────────────────────────────────────┘    │
│                         │                                    │
│  ┌──────────────────────┼──────────────────────────────┐    │
│  │              Flash Attention Layer                   │    │
│  │  • 2.49x-7.47x speedup                              │    │
│  │  • Memory-efficient attention                       │    │
│  │  • QE-optimized patterns                            │    │
│  └─────────────────────────────────────────────────────┘    │
│                         │                                    │
│  ┌──────────────────────┼──────────────────────────────┐    │
│  │             RL Algorithm Suite                       │    │
│  │  • Decision Transformer  • Q-Learning               │    │
│  │  • SARSA                 • Actor-Critic             │    │
│  │  • Policy Gradient       • DQN/PPO/A2C              │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                              │
└─────────────────────────────────────────────────────────────┘
```

## Quick Start

```bash
# Enable SONA for QE
aqe-v3 sona enable --mode real-time --adaptation 0.05ms

# Enable Flash Attention
aqe-v3 flash-attention enable --backend wasm-simd

# Initialize RL algorithms
aqe-v3 rl init --algorithms decision-transformer,q-learning,ppo

# Check integration status
aqe-v3 agentic-flow status
```

## Agent Workflow

```typescript
// Initialize agentic-flow integration
Task("Enable SONA learning", `
  Initialize SONA for QE:
  - Configure real-time adaptation (<0.05ms)
  - Set up pattern storage
  - Enable cross-domain transfer
  - Connect to QE memory system
`, "v3-qe-learning-coordinator")

// Configure Flash Attention
Task("Optimize with Flash Attention", `
  Enable Flash Attention for QE workloads:
  - Configure test similarity patterns
  - Set up code embedding optimization
  - Enable defect pattern matching
  - Validate 2.49x-7.47x speedup
`, "v3-qe-performance-engineer")

// Initialize RL suite
Task("Configure RL algorithms", `
  Set up RL algorithms for QE decisions:
  - Decision Transformer for test prioritization
  - Q-Learning for coverage optimization
  - PPO for adaptive retry strategies
  - Connect to reward signals
`, "v3-qe-metrics-optimizer")
```

## SONA Integration

### 1. QE SONA Module

```typescript
// v3/src/integration/sona/QESONAModule.ts
import { SONAModule, PatternAdapter } from '@anthropic/agentic-flow';

export class QESONAModule extends SONAModule {
  readonly domains = [
    'test-generation',
    'coverage-analysis',
    'defect-intelligence',
    'quality-assessment',
    'learning-optimization'
  ];

  private readonly patternStore: QEPatternStore;

  constructor(config: QESONAConfig) {
    super({
      adaptationTimeMs: config.adaptationTimeMs || 0.05,
      mode: config.mode || 'real-time'
    });

    this.patternStore = new QEPatternStore(config.memoryProvider);
  }

  // Adapt patterns for QE domain
  async adaptPattern(pattern: QEPattern): Promise<void> {
    await super.adaptPattern(pattern);

    // QE-specific adaptations
    switch (pattern.domain) {
      case 'test-generation':
        await this.adaptTestPattern(pattern);
        break;
      case 'defect-intelligence':
        await this.adaptDefectPattern(pattern);
        break;
      case 'coverage-analysis':
        await this.adaptCoveragePattern(pattern);
        break;
    }
  }

  private async adaptTestPattern(pattern: QEPattern): Promise<void> {
    // Learn test generation patterns
    const testPatterns = await this.extractTestPatterns(pattern);
    await this.patternStore.store('test-patterns', testPatterns);

    // Update test generation model
    await this.updateModel('test-generator', testPatterns);
  }

  private async adaptDefectPattern(pattern: QEPattern): Promise<void> {
    // Learn defect prediction patterns
    const defectPatterns = await this.extractDefectPatterns(pattern);
    await this.patternStore.store('defect-patterns', defectPatterns);

    // Update defect prediction model
    await this.updateModel('defect-predictor', defectPatterns);
  }

  // Cross-domain knowledge transfer
  async transferKnowledge(
    sourceDomain: string,
    targetDomain: string
  ): Promise<TransferResult> {
    const sourcePatterns = await this.patternStore.get(sourceDomain);

    // Adapt patterns for target domain
    const adaptedPatterns = await this.adaptForDomain(
      sourcePatterns,
      targetDomain
    );

    // Store in target domain
    await this.patternStore.store(targetDomain, adaptedPatterns);

    return {
      transferred: adaptedPatterns.length,
      accuracy: await this.validateTransfer(adaptedPatterns, targetDomain)
    };
  }
}
```

### 2. Flash Attention for QE

```typescript
// v3/src/integration/attention/QEFlashAttention.ts
import { FlashAttention, AttentionConfig } from '@anthropic/agentic-flow';

export class QEFlashAttention extends FlashAttention {
  private readonly qePatterns: Map<string, AttentionPattern>;

  constructor(config: QEFlashAttentionConfig) {
    super({
      backend: config.backend || 'wasm-simd',
      blockSize: 64,
      numBlocks: 128
    });

    this.qePatterns = this.initializeQEPatterns();
  }

  private initializeQEPatterns(): Map<string, AttentionPattern> {
    return new Map([
      ['test-similarity', {
        headsPerBlock: 8,
        queryChunkSize: 512,
        useCase: 'Finding similar test cases'
      }],
      ['code-embedding', {
        headsPerBlock: 4,
        queryChunkSize: 1024,
        useCase: 'Code semantic analysis'
      }],
      ['defect-matching', {
        headsPerBlock: 12,
        queryChunkSize: 256,
        useCase: 'Matching defect patterns'
      }],
      ['coverage-gap', {
        headsPerBlock: 6,
        queryChunkSize: 512,
        useCase: 'Identifying coverage gaps'
      }]
    ]);
  }

  // Optimize attention for QE workload
  async computeQEAttention(
    query: Float32Array,
    keys: Float32Array[],
    pattern: string
  ): Promise<AttentionResult> {
    const config = this.qePatterns.get(pattern);
    if (!config) {
      throw new Error(`Unknown QE attention pattern: ${pattern}`);
    }

    // Apply Flash Attention with QE-specific config
    return await this.compute(query, keys, {
      headsPerBlock: config.headsPerBlock,
      queryChunkSize: config.queryChunkSize
    });
  }

  // Batch compute for multiple queries (parallelized)
  async batchComputeQE(
    queries: Float32Array[],
    keys: Float32Array[],
    pattern: string
  ): Promise<AttentionResult[]> {
    const config = this.qePatterns.get(pattern);

    return await Promise.all(
      queries.map(query =>
        this.compute(query, keys, {
          headsPerBlock: config.headsPerBlock,
          queryChunkSize: config.queryChunkSize
        })
      )
    );
  }

  // Get performance metrics
  getPerformanceMetrics(): FlashAttentionMetrics {
    return {
      speedup: this.calculateSpeedup(), // 2.49x-7.47x
      memoryReduction: this.calculateMemoryReduction(),
      throughput: this.calculateThroughput()
    };
  }
}
```

### 3. RL Algorithm Suite

```typescript
// v3/src/integration/rl/QERLSuite.ts
import {
  DecisionTransformer,
  QLearning,
  SARSA,
  ActorCritic,
  PolicyGradient,
  DQN,
  PPO,
  A2C
} from '@anthropic/agentic-flow/rl';

export class QERLSuite {
  private readonly algorithms: Map<string, RLAlgorithm>;
  private readonly rewardSignals: QERewardSignals;

  constructor(config: QERLConfig) {
    this.algorithms = this.initializeAlgorithms(config.algorithms);
    this.rewardSignals = new QERewardSignals(config.rewards);
  }

  private initializeAlgorithms(algorithms: string[]): Map<string, RLAlgorithm> {
    const map = new Map();

    const implementations: Record<string, () => RLAlgorithm> = {
      'decision-transformer': () => new DecisionTransformer({
        contextLength: 1024,
        numLayers: 6,
        numHeads: 8
      }),
      'q-learning': () => new QLearning({
        learningRate: 0.01,
        discountFactor: 0.99,
        epsilon: 0.1
      }),
      'sarsa': () => new SARSA({
        learningRate: 0.01,
        discountFactor: 0.99,
        epsilon: 0.1
      }),
      'actor-critic': () => new ActorCritic({
        actorLearningRate: 0.001,
        criticLearningRate: 0.01
      }),
      'policy-gradient': () => new PolicyGradient({
        learningRate: 0.001,
        baseline: true
      }),
      'dqn': () => new DQN({
        learningRate: 0.001,
        batchSize: 32,
        targetUpdateFreq: 100
      }),
      'ppo': () => new PPO({
        learningRate: 0.0003,
        clipRatio: 0.2,
        epochs: 10
      }),
      'a2c': () => new A2C({
        learningRate: 0.001,
        entropyCoef: 0.01
      })
    };

    for (const name of algorithms) {
      if (implementations[name]) {
        map.set(name, implementations[name]());
      }
    }

    return map;
  }

  // QE-specific RL applications
  async prioritizeTests(
    tests: TestCase[],
    context: ExecutionContext
  ): Promise<TestCase[]> {
    const transformer = this.algorithms.get('decision-transformer');
    if (!transformer) throw new Error('Decision Transformer not initialized');

    const priorities = await transformer.predict(
      this.encodeTestContext(tests, context)
    );

    return tests
      .map((test, i) => ({ test, priority: priorities[i] }))
      .sort((a, b) => b.priority - a.priority)
      .map(item => item.test);
  }

  async optimizeCoveragePath(
    currentCoverage: CoverageReport,
    targetFiles: string[]
  ): Promise<CoveragePath> {
    const qLearning = this.algorithms.get('q-learning');
    if (!qLearning) throw new Error('Q-Learning not initialized');

    const state = this.encodeCoverageState(currentCoverage);
    const actions = await qLearning.selectActions(state, targetFiles.length);

    return {
      order: this.decodeCoveragePath(actions, targetFiles),
      expectedCoverage: this.estimateCoverage(actions, currentCoverage)
    };
  }

  async tuneQualityThresholds(
    metrics: QualityMetrics,
    history: QualityHistory
  ): Promise<Thresholds> {
    const actorCritic = this.algorithms.get('actor-critic');
    if (!actorCritic) throw new Error('Actor-Critic not initialized');

    const action = await actorCritic.selectAction(
      this.encodeQualityState(metrics, history)
    );

    return this.decodeThresholds(action);
  }

  async optimizeRetryStrategy(
    failedTests: FailedTest[],
    resourceConstraints: ResourceConstraints
  ): Promise<RetryStrategy> {
    const ppo = this.algorithms.get('ppo');
    if (!ppo) throw new Error('PPO not initialized');

    const action = await ppo.selectAction(
      this.encodeRetryState(failedTests, resourceConstraints)
    );

    return this.decodeRetryStrategy(action);
  }

  // Train from QE feedback
  async train(experience: QEExperience): Promise<void> {
    const reward = this.rewardSignals.calculate(experience);

    for (const [name, algorithm] of this.algorithms) {
      await algorithm.update({
        state: experience.state,
        action: experience.action,
        reward,
        nextState: experience.nextState,
        done: experience.done
      });
    }
  }
}
```

## QE-Specific RL Applications

| Algorithm | QE Application | Domain |
|-----------|----------------|--------|
| Decision Transformer | Test case prioritization | test-execution |
| Q-Learning | Coverage path optimization | coverage-analysis |
| SARSA | Defect prediction sequencing | defect-intelligence |
| Actor-Critic | Quality gate threshold tuning | quality-assessment |
| Policy Gradient | Resource allocation | coordination |
| DQN | Parallel execution scheduling | test-execution |
| PPO | Adaptive retry strategies | test-execution |
| A2C | Fleet coordination | coordination |

## Performance Targets

| Metric | Current | Target | Via |
|--------|---------|--------|-----|
| Test embedding | ~50ms | <15ms | Flash Attention |
| Pattern adaptation | ~2ms | <0.05ms | SONA |
| Coverage search | ~100ms | <1ms | HNSW + Flash |
| RL decision | ~150ms | <20ms | Optimized models |
| Memory usage | ~200MB | ~80MB | Shared modules |

## Flash Attention Configuration

```typescript
const QE_FLASH_ATTENTION_CONFIG = {
  // Memory-efficient attention for large test suites
  blockSize: 64,
  numBlocks: 128,

  // QE workload patterns
  patterns: {
    testSimilarity: {
      headsPerBlock: 8,
      queryChunkSize: 512
    },
    codeEmbedding: {
      headsPerBlock: 4,
      queryChunkSize: 1024
    },
    defectMatching: {
      headsPerBlock: 12,
      queryChunkSize: 256
    }
  },

  // Backend configuration
  backend: 'wasm-simd', // or 'native', 'webgpu'

  // Performance targets
  targets: {
    speedup: '2.49x-7.47x',
    memoryReduction: '50%'
  }
};
```

## CLI Commands

```bash
# SONA operations
aqe-v3 sona enable --mode real-time
aqe-v3 sona status
aqe-v3 sona adapt --pattern test-generation
aqe-v3 sona transfer --from defect-patterns --to test-generation

# Flash Attention
aqe-v3 flash-attention enable --backend wasm-simd
aqe-v3 flash-attention status
aqe-v3 flash-attention benchmark
aqe-v3 flash-attention configure --pattern test-similarity

# RL Suite
aqe-v3 rl init --algorithms decision-transformer,q-learning,ppo
aqe-v3 rl status
aqe-v3 rl train --experience ./experiences/
aqe-v3 rl predict --algorithm decision-transformer --input tests.json

# Integration status
aqe-v3 agentic-flow status
aqe-v3 agentic-flow sync
aqe-v3 agentic-flow benchmark
```

## Shared Utilities

```typescript
// Shared between QE and claude-flow
const sharedUtils = {
  vectorOps: '@agentic-flow/vectors',
  embeddingCache: '@agentic-flow/embeddings',
  hnswIndex: '@agentic-flow/hnsw'
};

// QE-specific extensions
const qeExtensions = {
  testEmbeddings: './embeddings/test-embedding',
  coverageVectors: './embeddings/coverage-vectors',
  defectPatterns: './embeddings/defect-patterns'
};
```

## Implementation Checklist

- [ ] Implement QESONAModule extending base SONA
- [ ] Configure Flash Attention for QE patterns
- [ ] Initialize RL algorithm suite
- [ ] Create test prioritization with Decision Transformer
- [ ] Implement coverage optimization with Q-Learning
- [ ] Add quality threshold tuning with Actor-Critic
- [ ] Build retry strategy optimization with PPO
- [ ] Set up training from QE feedback
- [ ] Write performance benchmarks
- [ ] Add CLI commands

## Related Skills

- v3-qe-learning-optimization - Transfer learning
- v3-qe-memory-unification - SONA pattern storage
- v3-integration-deep (claude-flow) - Reference implementation

## Related ADRs

- ADR-040: V3 QE Agentic-Flow Deep Integration
- ADR-038: V3 QE Memory Unification