/agentic-quality-engineering

name: agentic-quality-engineering
description: "Use when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work."
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: 1.0
last_optimized: 2025-12-02
dependencies: []
quick_reference_card: true
tags: [pacts, agents, fleet, coordination, autonomous, structured, foundational]
trust_tier: 1
validation:
  schema_path: schemas/output.json

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 PACTS principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus, Structured governance (observability and explainability of agent behavior) 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

PACTS 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 | | **S**tructured | Governance, observability, explainable decisions (measure confidence, not trust) | Audit behavior, set policy |

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**

// 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 `aqe memory store` with `persist: true` for learnings.

**1. Store data to persistent memory:**

// Store test plan decisions (persisted to .agentic-qe/memory.db)
aqe memory store \
  --key "aqe/test-plan/pr-123" \
  --namespace "aqe/test-plan" \
  --value '{...}' \
  --json

**2. Retrieve prior learnings before task:**

// Query patterns before starting test generation
const priorData = await aqe memory get --key "aqe/learning/patterns/test-generation/*" --namespace "aqe/learning" --json

// Use patterns to guide current task
if (priorData.success) {
  console.log(`Loaded ${priorData.patterns.length} prior patterns`);
}

**3. Store coverage analysis results:**

aqe memory store \
  --key "aqe/coverage/auth-module" \
  --namespace "aqe/coverage" \
  --value '{...}' \
  --json

Three-Phase Memory Protocol

For coordinated multi-agent tasks, use the STATUS → PROGRESS → COMPLETE pattern:

// PHASE