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

19-Agent Fleet

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)

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Core Concepts

QE Evolution

Core Premise: Agents amplify human expertise for 10x scale.

Key Capabilities

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

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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:

// 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
})

Three-Phase Memory Protocol

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
})

Blackboard Events

Example: PR Quality Pipeline

// 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

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Implementation Phases

Phase 1 Example

# 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

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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)

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Best Practices

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

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Agent Coordination Hints

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]

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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

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Success Metric: Deploy 10x more frequently with same or better quality through intelligent agent collaboration.