PROMPTreliabilityerror-handlingresilienceobservabilitydevops

Error Handling & Resilience Audit

Reviews code for missing error handling, resilience patterns, and failure modes. Identifies gaps in try/catch coverage, retry logic, circuit breakers, and graceful degradation.

June 17, 2026BurmCode

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Act as a senior reliability engineer and resilience architect. Review the provided codebase for error handling completeness, failure mode coverage, and resilience patterns. Identify where the system can fail silently, crash, or behave unpredictably under load, errors, or edge cases.

Focus areas:

1. Error handling coverage
- Missing try/catch or error boundaries around async operations, external calls, or file I/O
- Unhandled promise rejections, uncaught exceptions, or missing error event listeners
- Error paths that are not tested (happy-path only coverage)
- Silent failures where errors are swallowed or logged but not handled
- Missing validation of inputs, external responses, or configuration values before use

2. External dependency resilience
- Missing timeouts on network requests, database calls, or API calls
- Missing retry logic with exponential backoff and circuit breaker patterns
- No graceful degradation when external services are down
- Missing health checks, readiness probes, or liveness checks
- No fallback values or cached responses for critical dependencies

3. Resource and load safety
- Missing rate limiting, request throttling, or concurrency limits
- Unbounded queue growth, memory leaks, or connection pool exhaustion
- Missing backpressure handling in streaming or event-driven systems
- No protection against slowloris, large payloads, or DDoS patterns

4. State and data consistency
- Race conditions in shared state, caching, or concurrent updates
- Missing transactional boundaries or partial update risks
- Inconsistent state across distributed services or async jobs
- Missing idempotency on retried operations
- No compensation logic for failed multi-step operations

5. Observability and recovery
- Missing structured logging, error context, or correlation IDs
- Alerts that are too noisy or missing critical failure signals
- Missing runbooks, incident response steps, or self-healing mechanisms
- No automatic rollback, kill switch, or feature flag for risky changes
- Insufficient metrics for error rates, latency spikes, or saturation

For each gap, provide:
- Gap title and severity (Critical/High/Medium/Low)
- Affected file/function/endpoint/integration
- Failure scenario: what goes wrong and when
- User impact: what the user sees or experiences
- Recommended pattern: specific code changes or architecture adjustments
- Effort to implement (Quick/Moderate/Substantial)

If requested, implement fixes directly. Rules:
- Preserve existing functionality; fail safe, not open
- Add retry logic only where idempotency is guaranteed or handled
- Keep error messages useful for debugging but safe for production (no sensitive data leaks)
- Add or update tests that simulate failures, timeouts, and retries
- Document new failure modes and how operators should respond

Return:
1. Executive summary: resilience score and top 5 failure risks
2. Gap analysis: table with severity, location, failure scenario, user impact, fix, effort
3. Quick wins: high-impact fixes under 30 minutes
4. Architecture recommendations: patterns to adopt (circuit breaker, bulkhead, retry, timeout, fallback)
5. Testing plan: how to simulate failures and validate resilience
6. Observability improvements: logging, metrics, and alerting gaps

EXPECTED OUTPUT

What It Should Produce

Resilience score, gap analysis with failure scenarios and user impact, quick wins, architecture recommendations (circuit breaker, retry, etc.), testing plan, and observability improvements.

Published by BurmCode

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