Short answer
Review AI coding agent work by checking scope first, then behavior. Confirm the agent changed only relevant files, preserved product intent, ran typecheck/build/tests, handled edge cases, avoided secrets, and left a clear rollback path. Use Cursor, Codex, Claude Code, Copilot, Replit, or app builders only with explicit acceptance criteria and verification commands.
AI coding agents can move quickly through a repository, but speed increases the importance of review discipline. A product team should not accept a change because it compiles once or because the diff looks plausible. The review should confirm the task boundary, changed files, user-facing behavior, tests, accessibility, security, data impact, and rollback path.
Review scope before implementation quality
A clean-looking diff can still solve the wrong problem. Start by confirming the agent understood the user goal, touched the right files, and did not silently refactor unrelated areas.
- - Compare the diff with the original task.
- - Reject unrelated rewrites and hidden product changes.
- - Check that generated abstractions are actually needed.
Require evidence, not confidence
The agent should provide command output, screenshots, or direct runtime evidence for the changed behavior. Explanations are not a substitute for verification.
Inspect user-facing and operational risk
Product teams should review accessibility, mobile behavior, empty states, error states, privacy, security, and deployment impact before merging AI-generated changes.
Decision matrix
| Criterion | Choose when | Avoid when |
|---|---|---|
| Task scope | The change maps directly to the requested behavior. | The agent rewrites unrelated code or changes product strategy. |
| Verification | Typecheck, build, tests, and key smoke checks are run. | The answer only says the code should work. |
| Risk | Security, privacy, data, and rollback impact are understood. | Generated code touches auth, payments, or data without extra review. |
| Maintainability | The code matches existing project patterns. | The agent introduces unnecessary frameworks or abstractions. |
Alternatives
Manual implementation
Use when: The change touches security, payments, data migration, or core architecture.
Tradeoff: Slower, but gives tighter control over risk.
Agent implementation with narrow acceptance criteria
Use when: The task is scoped and has clear verification commands.
Tradeoff: Fast, but still requires human review and rollback thinking.
Prototype in an app builder first
Use when: The team is validating UX before committing repo changes.
Tradeoff: Good for exploration, but production hardening still remains.
FAQ
Can AI coding agents merge changes without review?
They should not for product code. Even when tests pass, a human should review scope, behavior, risk, and whether the change matches product intent.
What is the minimum evidence for AI-generated code?
At minimum: diff review, typecheck or build output, relevant tests or smoke checks, and a clear explanation of affected behavior.
Methodology
This checklist is based on software review practice adapted for AI agents: scope control, behavioral verification, risk review, test evidence, maintainability, and rollback readiness.