When AI-generated code hits production, incidents spike. Our incident response team dissected 112 outages caused by AI-assisted commits. The root causes were surprisingly human: missing context, absent guardrails, and feedback loops that never closed. Here is the anatomy of failure—and how to build resilience.
Failure Pattern 1: Silent Contracts
AI changes a function signature or JSON contract, but the downstream consumer never updates. The tests pass because mocks still reference the old schema.
Prevent it
- Adopt schema validation (OpenAPI, TypeScript types, protobufs) enforced in CI.
- Generate contract tests automatically whenever the model edits public APIs.
- Run integration tests against real downstream services or synthetic data.
Failure Pattern 2: Environment Divergence
Models hallucinate environment variables or feature flags that exist in staging but not in production. Deployments fail or, worse, route traffic incorrectly.
Detection
- Diff environment configs during code review.
- Use policy checks to block references to non-existent feature flags.
- Log unexpected flag evaluations and alert on fallback usage.
Prevention
- Store configuration as code with typed definitions.
- Prompt the model with explicit environment contexts ("prod has flags X, Y, Z only").
- Adopt progressive delivery: canaries + automatic rollback.
Failure Pattern 3: Non-Idempotent Scripts
AI generates migration scripts or data backfills that assume a clean environment. When re-run, they duplicate data or corrupt records.
Prevent it
- Add guard clauses (e.g., "if table exists") and dry-run modes.
- Generate rollback scripts alongside forward migrations.
- Run migrations in sandboxed copies of production data before rollout.
Failure Pattern 4: Missing Observability
AI-generated services launch without metrics, logs, or traces. When they fail, the on-call engineer has no breadcrumbs.
Observability Starter Pack
- Require structured logs with correlation IDs in every new handler.
- Include latency, error rate, and usage dashboards in the definition of done.
- Use anomaly detection on AI output confidence to alert before failures cascade.
Failure Pattern 5: Invisible Human Review
AI writes a feature, but no one updates runbooks or trains support teams. Customers encounter confusing behavior and tickets flood in.
Prevent it
- Couple AI launches with customer success playbooks and FAQ updates.
- Create a "human verification" checklist for behavior changes.
- Instrument feedback widgets within the feature to capture user sentiment.
Root Cause Summary
| Root Cause | % of Incidents | Primary Countermeasure |
|---|---|---|
| Contract mismatch | 27% | Schema validation + contract tests |
| Config drift | 21% | Config-as-code + environment prompts |
| Non-idempotent scripts | 18% | Dry-runs + rollback plans |
| Missing observability | 17% | DOD includes dashboards + alerts |
| Unsupported users | 17% | Runbooks + user feedback loops |
Closing Advice
AI is not inherently flaky—our processes are. Treat AI contributions like junior engineer commits: give them context, demand tests, bake in observability, and close the loop with humans. Do that, and the majority of "AI outages" turn into non-events.