agentic-quality-engineering

# Agentic Quality Engineering <default_to_action> When implementing agentic QE or coordinating agents: 1. SPAWN appropriate agent(s) for the task using `Task` tool with agent type 2. CONFIGURE agent coordination (hierarchical/mesh/sequential) 3. EXECUTE with PACT principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus 4. VALIDATE results through quality gates before deployment 5. LEARN from outcomes - store patterns in `aqe/learning/*` namespace **Quick Agent Selection:** - Test generation needed → `qe-test-generator` - Coverage gaps → `qe-coverage-analyzer` - Quality decision → `qe-quality-gate` - Security scan → `qe-security-scanner` - Performance test → `qe-performance-tester` - Full pipeline → `qe-fleet-commander` **Critical Success Factors:** - Agents amplify human expertise, not replace it - Human-in-the-loop for critical decisions - Measure: bugs caught, time saved, coverage improved </default_to_action> ## Quick Reference Card ### When to Use - Designing autonomous testing systems - Scaling QE with intelligent agents - Implementing multi-agent coordination - Building CI/CD quality pipelines ### PACT Principles | Principle | Agent Behavior | Human Role | |-----------|---------------|------------| | **P**roactive | Analyze pre-merge, predict risk | Set guardrails | | **A**utonomous | Execute tests, fix flaky tests | Review critical | | **C**ollaborative | Multi-agent coordination | Provide context | | **T**argeted | Risk-based prioritization | Define risk areas | ### 19-Agent Fleet | Category | Agents | Primary Use | |----------|--------|-------------| | Core Testing (5) | test-generator, test-executor, coverage-analyzer, quality-gate, quality-analyzer | Daily testing | | Performance/Security (2) | performance-tester, security-scanner | Non-functional | | Strategic (3) | requirements-validator, production-intelligence, fleet-commander | Planning | | Advanced (4) | regression-risk-analyzer, test-data-architect, api-contract-validator, flaky-test-hunter | Specialized | | Visual/Chaos (2) | visual-tester, chaos-engineer | Edge cases | | Deployment (1) | deployment-readiness | Release | | Analysis (1) | code-complexity | Maintainability | ### Coordination Patterns ``` Hierarchical: fleet-commander → [generators] → [executors] → quality-gate Mesh: test-gen ↔ coverage ↔ quality (peer decisions) Sequential: risk-analyzer → test-gen → executor → coverage → gate ``` ### Success Criteria ✅ 10x deployment frequency with same/better quality ✅ Coverage gaps detected in real-time ✅ Bugs caught pre-production ❌ Agents acting without human oversight on critical decisions ❌ Deploying all 19 agents at once (start with 1-2) --- ## Core Concepts ### QE Evolution | Stage | Approach | Limitation | |-------|----------|------------| | Traditional | Manual everything | Human bottleneck | | Automation | Scripts + fixed scenarios | Needs orchestration | | **Agentic** | AI agents + human judgment | Requires trust-building | **Core Premise:** Agents amplify human expertise for 10x scale. ### Key Capabilities **1. Intelligent Test Generation** ```typescript // Agent analyzes code change, generates targeted tests const tests = await qeTestGenerator.generate(prDiff); // → Happy path, edge cases, error handling tests ``` **2. Pattern Detection** - Scan logs, find anomalies, correlate errors **3. Adaptive Strategy** - Adjust test focus based on risk signals **4. Root Cause Analysis** - Link failures to code changes, suggest fixes --- ## Agent Coordination ### Memory Namespaces ``` aqe/test-plan/* - Test planning decisions aqe/coverage/* - Coverage analysis results aqe/quality/* - Quality metrics and gates aqe/learning/* - Patterns and Q-values aqe/coordination/* - Cross-agent state ``` ### Memory Operations (MCP Tools) **CRITICAL**: Always use `mcp__agentic-qe__memory_store` with `persist: true` for learnings. **1. Store data to persistent memory:** ```javascript // Store test plan decisions (persisted to .agentic-qe/memory.db) mcp__agentic_qe__memory_store({ key: "aqe/test-plan/pr-123", namespace: "aqe/test-plan", value: { prNumber: 123, riskLevel: "medium", requiredCoverage: 85, testTypes: ["unit", "integration"], estimatedTime: 1800 }, persist: true, // ⚠️ REQUIRED for cross-session persistence ttl: 604800 // 7 days (0 = permanent) }) ``` **2. Retrieve prior learnings before task:** ```javascript // Query patterns before starting test generation const priorData = await mcp__agentic_qe__memory_retrieve({ key: "aqe/learning/patterns/test-generation/*", namespace: "aqe/learning", includeMetadata: true }) // Use patterns to guide current task if (priorData.success) { console.log(`Loaded ${priorData.patterns.length} prior patterns`); } ``` **3. Store coverage analysis results:** ```javascript mcp__agentic_qe__memory_store({ key: "aqe/coverage/auth-module", namespace: "aqe/coverage", value: { moduleId: "auth-module", currentCoverage: 78, gaps: ["error-handling", "edge-cases"], suggestedTests: 12, priority: "high" }, persist: true, ttl: 1209600 // 14 days }) ``` ### Three-Phase Memory Protocol For coordinated multi-agent tasks, use the STATUS → PROGRESS → COMPLETE pattern: ```javascript // PHASE 1: STATUS - Task starting mcp__agentic_qe__memory_store({ key: "aqe/coordination/task-123/status", namespace: "aqe/coordination", value: { status: "running", agent: "qe-test-generator", startTime: Date.now() }, persist: true }) // PHASE 2: PROGRESS - Intermediate updates mcp__agentic_qe__memory_store({ key: "aqe/coordination/task-123/progress", namespace: "aqe/coordination", value: { progress: 50, action: "generating-unit-tests", testsGenerated: 25 }, persist: true }) // PHASE 3: COMPLETE - Task finished mcp__agentic_qe__memory_store({ key: "aqe/coordination/task-123/complete", namespace: "aqe/coordination", value: { status: "complete", result: "success", testsGenerated: 47, coverageAchieved: 92.3, duration: 15000 }, persist: true }) ``` ### Blackboard Events | Event | Trigger | Subscribers | |-------|---------|-------------| | `test:generated` | New tests created | executor, coverage | | `coverage:gap` | Gap detected | test-generator | | `quality:decision` | Gate evaluated | fleet-commander | | `security:finding` | Vulnerability found | quality-gate | ### Example: PR Quality Pipeline ```typescript // 1. Risk analysis const risks = await Task("Analyze PR", prDiff, "qe-regression-risk-analyzer"); // 2. Generate tests for risks const tests = await Task("Generate tests", risks, "qe-test-generator"); // 3. Execute + analyze const results = await Task("Run tests", tests, "qe-test-executor"); const coverage = await Task("Check coverage", results, "qe-coverage-analyzer"); // 4. Quality decision const decision = await Task("Evaluate", {results, coverage}, "qe-quality-gate"); // → GO/NO-GO with rationale ``` --- ## Implementation Phases | Phase | Duration | Goal | Agent(s) | |-------|----------|------|----------| | Experiment | Weeks 1-4 | Validate one use case | 1 agent | | Integrate | Months 2-3 | CI/CD pipeline | 3-4 agents | | Scale | Months 4-6 | Multiple use cases | 8+ agents | | Evolve | Ongoing | Continuous learning | Full fleet | ### Phase 1 Example ```bash # Week 1: Deploy single agent aqe agent spawn qe-test-generator # Weeks 2-3: Generate tests for 10 PRs # Track: bugs found, test quality, review time # Week 4: Measure impact aqe agent metrics qe-test-generator # → Tests: 150, Bugs: 12, Time saved: 8h ``` --- ## Limitations & Strengths ### Agents Excel At - **Volume**: Scan thousands of logs in seconds - **Patterns**: Find correlations humans miss - **Tireless**: 24/7 testing and monitoring - **Speed**: Instant code change analysis ### Agents Need Humans For - Business context and priorities - Ethical judgment and trade-offs - Creative exploration ("what if" scenarios) - Domain expertise (healthcare, finance, legal) --- ## Best Practices | Do | Don't | |----|-------| | Start with one agent, one use case | Deploy all 18 at once | | Build feedback loops early | Deploy and forget | | Human reviews agent output | Auto-merge without review | | Measure bugs caught, time saved | Track vanity metrics (test count) | | Build trust gradually | Give full autonomy immediately | ### Trust Progression ``` Month 1: Agent suggests → Human decides Month 2: Agent acts → Human reviews after Month 3: Agent autonomous on low-risk Month 4: Agent handles critical with oversight ``` --- ## Agent Coordination Hints ```yaml coordination: topology: hierarchical commander: qe-fleet-commander memory_namespace: aqe/coordination blackboard_topic: qe-fleet preload_skills: - agentic-quality-engineering # Always (this skill) - risk-based-testing # For prioritization - quality-metrics # For measurement agent_assignments: qe-test-generator: [api-testing-patterns, tdd-london-chicago] qe-coverage-analyzer: [quality-metrics, risk-based-testing] qe-security-scanner: [security-testing, risk-based-testing] qe-performance-tester: [performance-testing] ``` --- ## Related Skills - `holistic-testing-pact` - PACT principles deep dive - `risk-based-testing` - Prioritize agent focus - `quality-metrics` - Measure agent effectiveness - `api-testing-patterns`, `security-testing`, `performance-testing` - Specialized testing ## Resources - Agent definitions: `.claude/agents/` - CLI: `aqe agent --help` - Fleet status: `aqe fleet status` --- **Success Metric:** Deploy 10x more frequently with same or better quality through intelligent agent collaboration.