// AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles.
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
Comprehensive API testing patterns including contract testing, REST/GraphQL testing, and integration testing. Use when testing APIs or designing API test strategies.
Unvarnished technical criticism combining Linus Torvalds' precision, Gordon Ramsay's standards, and James Bach's BS-detection. Use when code/tests need harsh reality checks, certification schemes smell fishy, or technical decisions lack rigor. No sugar-coating, just surgical truth about what's broken and why.
Write high-quality bug reports that get fixed quickly. Use when reporting bugs, training teams on bug reporting, or establishing bug report standards.
Chaos engineering principles, controlled failure injection, resilience testing, and system recovery validation. Use when testing distributed systems, building confidence in fault tolerance, or validating disaster recovery.
Orchestrate quality engineering across CI/CD pipeline phases. Use when designing test strategies, planning quality gates, or implementing shift-left/shift-right testing.
| name | agentic-quality-engineering |
| description | AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles. |
| category | qe-core |
| priority | critical |
| tokenEstimate | 1400 |
| agents | ["qe-test-generator","qe-test-executor","qe-coverage-analyzer","qe-quality-gate","qe-quality-analyzer","qe-performance-tester","qe-security-scanner","qe-requirements-validator","qe-production-intelligence","qe-fleet-commander","qe-deployment-readiness","qe-regression-risk-analyzer","qe-test-data-architect","qe-api-contract-validator","qe-flaky-test-hunter","qe-visual-tester","qe-chaos-engineer","qe-code-complexity","qx-partner"] |
| implementation_status | optimized |
| optimization_version | 1 |
| last_optimized | "2025-12-02T00:00:00.000Z" |
| dependencies | [] |
| quick_reference_card | true |
| tags | ["pact","agents","fleet","coordination","autonomous","foundational"] |
<default_to_action> When implementing agentic QE or coordinating agents:
Task tool with agent typeaqe/learning/* namespaceQuick Agent Selection:
qe-test-generatorqe-coverage-analyzerqe-quality-gateqe-security-scannerqe-performance-testerqe-fleet-commanderCritical Success Factors:
| Principle | Agent Behavior | Human Role |
|---|---|---|
| Proactive | Analyze pre-merge, predict risk | Set guardrails |
| Autonomous | Execute tests, fix flaky tests | Review critical |
| Collaborative | Multi-agent coordination | Provide context |
| Targeted | Risk-based prioritization | Define risk areas |
| 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 |
Hierarchical: fleet-commander → [generators] → [executors] → quality-gate
Mesh: test-gen ↔ coverage ↔ quality (peer decisions)
Sequential: risk-analyzer → test-gen → executor → coverage → gate
✅ 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)
| 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.
1. Intelligent Test Generation
// 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
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
CRITICAL: Always use mcp__agentic-qe__memory_store with persist: true for learnings.
1. Store data to persistent memory:
// 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:
// 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:
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
})
For coordinated multi-agent tasks, use the STATUS → PROGRESS → COMPLETE pattern:
// 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
})
| 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 |
// 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
| 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 |
# 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
| 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 |
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
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]
holistic-testing-pact - PACT principles deep diverisk-based-testing - Prioritize agent focusquality-metrics - Measure agent effectivenessapi-testing-patterns, security-testing, performance-testing - Specialized testing.claude/agents/aqe agent --helpaqe fleet statusSuccess Metric: Deploy 10x more frequently with same or better quality through intelligent agent collaboration.