name: chaos-engineering-resilience
description: "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."
category: specialized-testing
priority: high
tokenEstimate: 900
agents: [qe-chaos-engineer, qe-performance-tester, qe-production-intelligence]
implementation_status: optimized
optimization_version: 1.0
last_optimized: 2025-12-02
dependencies: []
quick_reference_card: true
tags: [chaos, resilience, fault-injection, distributed-systems, recovery, netflix]

Chaos Engineering & Resilience Testing

When testing system resilience or injecting failures:
1. DEFINE steady state (normal metrics: error rate, latency, throughput)
2. HYPOTHESIZE system continues in steady state during failure
3. INJECT real-world failures (network, instance, disk, CPU)
4. OBSERVE and measure deviation from steady state
5. FIX weaknesses discovered, document runbooks, repeat

Quick Chaos Steps:
- Start small: Dev → Staging → 1% prod → gradual rollout
- Define clear rollback triggers (error_rate > 5%)
- Measure blast radius, never exceed planned scope
- Document findings → runbooks → improved resilience

Critical Success Factors:
- Controlled experiments with automatic rollback
- Steady state must be measurable
- Start in non-production, graduate to production

Quick Reference Card

When to Use

• Distributed systems validation
• Disaster recovery testing
• Building confidence in fault tolerance
• Pre-production resilience verification

Failure Types to Inject

Blast Radius Progression

Dev (safe) → Staging → 1% prod → 10% → 50% → 100%
     ↓           ↓         ↓        ↓
  Learn      Validate   Careful   Full confidence

Steady State Metrics

Chaos Experiment Structure

// Chaos experiment definition
const experiment = {
  name: 'Database latency injection',
  hypothesis: 'System handles 500ms DB latency gracefully',
  steadyState: {
    errorRate: '< 0.1%',
    p99Latency: '< 300ms'
  },
  method: {
    type: 'network-latency',
    target: 'database',
    delay: '500ms',
    duration: '5m'
  },
  rollback: {
    automatic: true,
    trigger: 'errorRate > 5%'
  }
};

Agent-Driven Chaos

// qe-chaos-engineer runs controlled experiments
await Task("Chaos Experiment", {
  target: 'payment-service',
  failure: 'terminate-random-instance',
  blastRadius: '10%',
  duration: '5m',
  steadyStateHypothesis: {
    metric: 'success-rate',
    threshold: 0.99
  },
  autoRollback: true
}, "qe-chaos-engineer");

// Validates:
// - System recovers automatically
// - Error rate stays within threshold
// - No data loss
// - Alerts triggered appropriately

Agent Coordination Hints

Memory Namespace

aqe/chaos-engineering/
├── experiments/*       - Experiment definitions & results
├── steady-states/*     - Baseline measurements
├── runbooks/*          - Generated recovery procedures
└── blast-radius/*      - Impact analysis

Fleet Coordination

const chaosFleet = await FleetManager.coordinate({
  strategy: 'chaos-engineering',
  agents: [
    'qe-chaos-engineer',          // Experiment execution
    'qe-performance-tester',      // Baseline metrics
    'qe-production-intelligence'  // Production monitoring
  ],
  topology: 'sequential'
});

Related Skills

• shift-right-testing - Production testing
• performance-testing - Load testing
• test-environment-management - Environment stability

Remember

Break things on purpose to prevent unplanned outages. Find weaknesses before users do. Define steady state, inject failures, measure impact, fix weaknesses, create runbooks. Start small, increase blast radius gradually.

With Agents: qe-chaos-engineer automates chaos experiments with blast radius control, automatic rollback, and comprehensive resilience validation. Generates runbooks from experiment results.