
shift-right-testing
by proffesor-for-testing
Agentic QE Fleet is an open-source AI-powered quality engineering platform designed for use with Claude Code, featuring specialized agents and skills to support testing activities for a product at any stage of the SDLC. Free to use, fork, build, and contribute. Based on the Agentic QE Framework created by Dragan Spiridonov.
SKILL.md
name: shift-right-testing description: "Testing in production with feature flags, canary deployments, synthetic monitoring, and chaos engineering. Use when implementing production observability or progressive delivery." category: testing-methodologies priority: high tokenEstimate: 1000 agents: [qe-production-intelligence, qe-chaos-engineer, qe-performance-tester, qe-quality-analyzer] implementation_status: optimized optimization_version: 1.0 last_optimized: 2025-12-02 dependencies: [] quick_reference_card: true tags: [shift-right, production-testing, canary, feature-flags, chaos-engineering, monitoring]
Shift-Right Testing
<default_to_action> When testing in production or implementing progressive delivery:
- IMPLEMENT feature flags for progressive rollout (1% → 10% → 50% → 100%)
- DEPLOY with canary releases (compare metrics before full rollout)
- MONITOR with synthetic tests (proactive) + RUM (reactive)
- INJECT failures with chaos engineering (build resilience)
- ANALYZE production data to improve pre-production testing
Quick Shift-Right Techniques:
- Feature flags → Control who sees what, instant rollback
- Canary deployment → 5% traffic, compare error rates
- Synthetic monitoring → Simulate users 24/7, catch issues before users
- Chaos engineering → Netflix-style failure injection
- RUM (Real User Monitoring) → Actual user experience data
Critical Success Factors:
- Production is the ultimate test environment
- Ship fast with safety nets, not slow with certainty
- Use production data to improve shift-left testing </default_to_action>
Quick Reference Card
When to Use
- Progressive feature rollouts
- Production reliability validation
- Performance monitoring at scale
- Learning from real user behavior
Shift-Right Techniques
| Technique | Purpose | When |
|---|---|---|
| Feature Flags | Controlled rollout | Every feature |
| Canary | Compare new vs old | Every deployment |
| Synthetic Monitoring | Proactive detection | 24/7 |
| RUM | Real user metrics | Always on |
| Chaos Engineering | Resilience validation | Regularly |
| A/B Testing | User behavior validation | Feature decisions |
Progressive Rollout Pattern
1% → 10% → 25% → 50% → 100%
↓ ↓ ↓ ↓
Check Check Check Monitor
Key Metrics to Monitor
| Metric | SLO Target | Alert Threshold |
|---|---|---|
| Error rate | < 0.1% | > 1% |
| p95 latency | < 200ms | > 500ms |
| Availability | 99.9% | < 99.5% |
| Apdex | > 0.95 | < 0.8 |
Feature Flags
// Progressive rollout with LaunchDarkly/Unleash pattern
const newCheckout = featureFlags.isEnabled('new-checkout', {
userId: user.id,
percentage: 10, // 10% of users
allowlist: ['beta-testers']
});
if (newCheckout) {
return <NewCheckoutFlow />;
} else {
return <LegacyCheckoutFlow />;
}
// Instant rollback on issues
await featureFlags.disable('new-checkout');
Canary Deployment
# Flagger canary config
apiVersion: flagger.app/v1beta1
kind: Canary
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: checkout-service
progressDeadlineSeconds: 60
analysis:
interval: 1m
threshold: 5 # Max failed checks
maxWeight: 50 # Max traffic to canary
stepWeight: 10 # Increment per interval
metrics:
- name: request-success-rate
threshold: 99
- name: request-duration
threshold: 500
Synthetic Monitoring
// Continuous production validation
await Task("Synthetic Tests", {
endpoints: [
{ path: '/health', expected: 200, interval: '30s' },
{ path: '/api/products', expected: 200, interval: '1m' },
{ path: '/checkout', flow: 'full-purchase', interval: '5m' }
],
locations: ['us-east', 'eu-west', 'ap-south'],
alertOn: {
statusCode: '!= 200',
latency: '> 500ms',
contentMismatch: true
}
}, "qe-production-intelligence");
Chaos Engineering
// Controlled failure injection
await Task("Chaos Experiment", {
hypothesis: 'System handles database latency gracefully',
steadyState: {
metric: 'error_rate',
expected: '< 0.1%'
},
experiment: {
type: 'network-latency',
target: 'database',
delay: '500ms',
duration: '5m'
},
rollback: {
automatic: true,
trigger: 'error_rate > 5%'
}
}, "qe-chaos-engineer");
Production → Pre-Production Feedback Loop
// Convert production incidents to regression tests
await Task("Incident Replay", {
incident: {
id: 'INC-2024-001',
type: 'performance-degradation',
conditions: { concurrent_users: 500, cart_items: 10 }
},
generateTests: true,
addToRegression: true
}, "qe-production-intelligence");
// Output: New test added to prevent recurrence
Agent Coordination Hints
Memory Namespace
aqe/shift-right/
├── canary-results/* - Canary deployment metrics
├── synthetic-tests/* - Monitoring configurations
├── chaos-experiments/* - Experiment results
├── production-insights/* - Issues → test conversions
└── rum-analysis/* - Real user data patterns
Fleet Coordination
const shiftRightFleet = await FleetManager.coordinate({
strategy: 'shift-right-testing',
agents: [
'qe-production-intelligence', // RUM, incident replay
'qe-chaos-engineer', // Resilience testing
'qe-performance-tester', // Synthetic monitoring
'qe-quality-analyzer' // Metrics analysis
],
topology: 'mesh'
});
Related Skills
- shift-left-testing - Pre-production testing
- chaos-engineering-resilience - Failure injection deep dive
- performance-testing - Load testing
- agentic-quality-engineering - Agent coordination
Remember
Production is the ultimate test environment. Feature flags enable instant rollback. Canary catches issues before 100% rollout. Synthetic monitoring detects problems before users. Chaos engineering builds resilience. RUM shows real user experience.
With Agents: Agents monitor production, replay incidents as tests, run chaos experiments, and convert production insights to pre-production tests. Use agents to maintain continuous production quality.
Score
Total Score
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