Treat AI output as a fast draft, not a finished deliverable. A reliable merge decision starts with a named human owner, then six checks: behavior, tests, contracts, edge cases, security, and observability. If one check fails, you are not done.
Why This Checklist Exists
Most AI coding failures are not syntax problems. The code compiles. The happy path works. But hidden assumptions slip through: missing authorization checks, wrong timeout defaults, unhandled null states, brittle parsing, or no logging where incidents will happen.
The fix is not "trust AI less". The fix is owning a repeatable review loop. Use the same merge standard for AI-generated code that you use for any high-speed contribution: explicit checks, not gut feeling.
First, Name the Owner
The most important question is not whether AI wrote the code. It is who understands it well enough to defend it after merge. Every AI-assisted change needs one accountable human who can explain the behavior, approve the tradeoffs, and respond if the change causes an incident.
A good owner can answer three questions without rereading the diff from scratch: why this approach was chosen, what could go wrong, and how to roll it back. If nobody can answer those, the change is still a draft.
Before approval, ask the author to walk through the riskiest path in the diff. If the explanation relies on "the AI said so", pause the merge and reduce the change until it can be reviewed on its own terms.
The 6-Step Ownership Checklist
1. Behavior Check: Did it solve the real problem?
Re-state the requirement in one sentence, then compare it to what was actually implemented. Look for scope drift: extra features, omitted constraints, or a different interpretation of the task.
- Verify acceptance criteria, not just output shape.
- Check the code path that matters most to users.
- Reject "almost right" behavior before anything else.
2. Test Check: Can failure be reproduced automatically?
If a bug fix has no failing test before and passing test after, the learning value is low and regression risk stays high. For net-new features, include at least one success test and one failure-mode test.
- Add or update tests in the same PR.
- Cover one edge case that is easy to miss.
- Make sure test names describe intent, not implementation details.
3. Contract Check: Are interfaces still true?
AI often changes "working" code in ways that subtly violate contracts. Confirm API schemas, event payloads, database assumptions, and type guarantees stayed compatible.
- Validate request and response models.
- Confirm backwards compatibility where required.
- Check migration safety if data shape changed.
4. Edge Case Check: What happens at the boundary?
Ask what happens for empty input, timeouts, retries, duplicate submissions, partial data, and stale state. AI usually optimizes for the center of the problem. Production incidents start at the edges.
- Identify at least three boundary scenarios.
- Confirm user-visible errors are understandable.
- Ensure retries and idempotency are intentional.
5. Security Check: Did speed create a new risk?
Review auth gates, secrets handling, user input paths, and data exposure. AI-generated code can look clean while introducing obvious vulnerabilities.
- Check authorization at the action level, not only route level.
- Review input validation and output encoding.
- Scan logs and errors for accidental sensitive data leaks.
6. Observability Check: Can we debug this in production?
If this code fails at 03:00, what signal will you have? Add structured logs, metrics, and clear error context where it matters.
- Log identifiers and decision points, not noisy internals.
- Emit metrics for high-impact paths.
- Ensure alerting thresholds match business impact.
A Minimal PR Template You Can Reuse
- Requirement: Which acceptance criteria are now satisfied?
- Tests: Which tests fail before this change and pass after it?
- Contracts: Any API/schema/type changes? Are they backward compatible?
- Edge cases: Which boundary scenarios were tested?
- Security: What auth/input/logging checks were performed?
- Observability: What logs/metrics/alerts were added or updated?
- Rollback: How will this be disabled, reverted, or mitigated if production behavior is wrong?
If the author cannot answer these prompts clearly, the code is not merge-ready yet, regardless of how fast it was produced.
Red Flags That Should Stop the Merge
Some review signals are strong enough to stop immediately. Do not keep polishing around them. Reduce the diff, ask for a narrower implementation, or rewrite the risky part by hand.
- The change touches authentication, billing, permissions, or data deletion without focused tests.
- The implementation adds broad helpers or abstractions that are used only once.
- The diff changes public behavior but the PR description only summarizes files changed.
- Error handling hides failures instead of making them recoverable or observable.
- The reviewer cannot tell which parts are intentional and which parts are incidental AI output.
Conclusion
AI changes code velocity. It does not change ownership. The winning teams are not the ones who generate the most lines. They are the ones who preserve standards while moving faster. A simple checklist is often the difference between "fast" and "fast with confidence".
Continue with AI Code Review, Testing with AI, and Debugging with AI to turn this checklist into a full quality workflow.