Injects controlled faults (network partition, latency, process kill, disk pressure) into distributed systems and validates recovery behavior. Use when testing circuit breakers, failover paths, retry logic, or building confidence in system resilience through chaos engineering.
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Guide the use of v3's chaos engineering capabilities including controlled fault injection, load/stress testing, resilience validation, and disaster recovery testing.
# Run chaos experiment
aqe chaos run --experiment network-latency --target api-service
# Load test
aqe chaos load --scenario peak-traffic --duration 30m
# Stress test to breaking point
aqe chaos stress --endpoint /api/users --max-users 10000
# Test circuit breaker
aqe chaos circuit-breaker --service payment-service
// Chaos experiment
Task("Run chaos experiment", `
Execute controlled chaos on api-service:
- Inject 500ms network latency
- Monitor service health metrics
- Verify circuit breaker activation
- Measure recovery time
- Document findings
`, "qe-chaos-engineer")
// Load testing
Task("Performance load test", `
Run load test simulating Black Friday traffic:
- Ramp up to 10,000 concurrent users
- Maintain load for 30 minutes
- Monitor response times and error rates
- Identify bottlenecks
- Compare against SLAs
`, "qe-load-tester")
await chaosEngineer.injectFault({
target: 'api-service',
fault: {
type: 'latency',
parameters: {
delay: '500ms',
jitter: '100ms',
percentage: 50
}
},
duration: '5m',
monitoring: {
metrics: ['response_time', 'error_rate', 'throughput'],
alerts: true
},
rollback: {
automatic: true,
trigger: 'error_rate > 10%'
}
});
await loadTester.execute({
scenario: 'peak-traffic',
profile: {
rampUp: '5m',
steadyState: '30m',
rampDown: '5m'
},
users: {
initial: 100,
target: 5000,
pattern: 'linear'
},
assertions: {
p95_latency: '<500ms',
error_rate: '<1%',
throughput: '>1000rps'
}
});
await loadTester.stressTest({
endpoint: '/api/checkout',
strategy: 'step-increase',
steps: [100, 500, 1000, 2000, 5000],
stepDuration: '5m',
findBreakingPoint: true,
monitoring: {
resourceUtilization: true,
databaseConnections: true,
memoryUsage: true
}
});
await resilienceTester.validate({
scenarios: [
'database-failover',
'cache-failure',
'external-service-timeout',
'pod-termination'
],
expectations: {
gracefulDegradation: true,
automaticRecovery: true,
dataIntegrity: true,
recoveryTime: '<30s'
}
});
| Fault | Description | Use Case |
|---|---|---|
| Latency | Add network delay | Test timeouts |
| Packet Loss | Drop network packets | Test retry logic |
| CPU Stress | Consume CPU | Test resource limits |
| Memory Pressure | Consume memory | Test OOM handling |
| Disk Full | Fill disk space | Test disk errors |
| Process Kill | Terminate process | Test recovery |
interface ChaosReport {
experiment: {
name: string;
target: string;
fault: FaultConfig;
duration: number;
};
results: {
hypothesis: string;
validated: boolean;
metrics: {
before: MetricSnapshot;
during: MetricSnapshot;
after: MetricSnapshot;
};
events: ChaosEvent[];
recovery: {
detected: boolean;
time: number;
automatic: boolean;
};
};
findings: {
severity: 'critical' | 'high' | 'medium' | 'low';
description: string;
recommendation: string;
}[];
artifacts: {
logs: string;
metrics: string;
traces: string;
};
}
safety:
blast_radius:
max_affected_pods: 1
max_affected_percentage: 10
abort_conditions:
- error_rate > 50%
- p99_latency > 10s
- service_unavailable
excluded_environments:
- production-critical
required_approvals:
production: 2
staging: 0
await resilienceTester.validateSLA({
slas: {
availability: 99.9,
p95_latency: 500,
error_rate: 0.1
},
period: '30d',
report: {
breaches: true,
trends: true,
projections: true
}
});
Primary Agents: qe-chaos-engineer, qe-load-tester, qe-resilience-tester Coordinator: qe-chaos-coordinator Related Skills: qe-performance, security-testing
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
The English summary is being prepared. Showing the original description.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
The English summary is being prepared. Showing the original description.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
The English summary is being prepared. Showing the original description.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
The English summary is being prepared. Showing the original description.