--- 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 { 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 { \/\/ 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 { \/\/ 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 { 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; constructor(config: QEFlashAttentionConfig) { super({ backend: config.backend || 'wasm-simd', blockSize: 64, numBlocks: 128 }); this.qePatterns = this.initializeQEPatterns(); } private initializeQEPatterns(): Map { 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 { 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 { 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; private readonly rewardSignals: QERewardSignals; constructor(config: QERLConfig) { this.algorithms = this.initializeAlgorithms(config.algorithms); this.rewardSignals = new QERewardSignals(config.rewards); } private initializeAlgorithms(algorithms: string[]): Map { const map = new Map(); const implementations: Record 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 { 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 { 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 { 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 { 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 { 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