Review an AI agent’s code change the same way you would any consequential pull request: verify the intended behavior, inspect every changed file, check the tests and run the repository’s own checks. Do not accept the agent’s summary or a green test suite as proof that the change is correct. The workflow below helps you decide what changed, whether it meets the request, and where a human or security review needs to go deeper.
Start by establishing what the change is supposed to do
Before opening individual code hunks, verify that you are reviewing the right repository, pull request, author, and branch. Read the original request alongside the pull request description. The request sets the intended behavior and boundaries; the description explains the proposed change, but neither substitutes for examining the diff. OpenAI’s Codex pull request review guidance recommends understanding the task and proposed change before assessing review findings.
- Write down the expected behavior in concrete terms: what should happen, for which inputs or conditions, and what should remain unchanged.
- Note explicit constraints, such as files or systems that should not be touched, compatibility requirements, or error behavior.
- Compare the pull request’s stated scope with the request. If they differ, resolve the discrepancy before treating the implementation as complete.
Inspect the complete diff, not just the agent’s summary
Read every changed file, preferably one at a time. An agent’s explanation or automated review can help you navigate, but it may omit changes or mischaracterize them. OWASP’s Secure Coding with AI Cheat Sheet advises reviewing the full diff and looking for changes outside the requested scope.
Routine-looking files can have consequential effects. Check lockfiles and dependency manifests, tests, formatting-only edits, CI workflows, build and install scripts, and deployment configuration. Ask why any edit is needed if it does not support the requested behavior; a large or unrelated change should not pass merely because it appears alongside a valid fix.
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Check for scope drift and hidden behavior
- Look for dependency additions, removals, or version changes, and verify they are necessary.
- Review test edits as carefully as production code; deleted tests or weakened assertions may conceal a regression.
- Inspect workflow, build, install, and deployment changes for new commands, permissions, network access, or execution of external code.
- Confirm formatting and generated-file changes are expected and do not obscure substantive edits.
Trace the implementation against the intended behavior
For each important change, follow the code into its surrounding functions, callers, and data flow. Check whether the implementation preserves existing contracts: inputs accepted, outputs produced, errors raised, state changed, and resources cleaned up. OpenAI’s review guidance illustrates tracing effects through related behavior, including sign-in effects and connection cleanup on an error path.
Do not review only the happy path. Consider invalid or missing input, boundary values, failures from dependencies, retries, timeouts, and concurrent operations when relevant to the code. Verify that error paths leave the system in a safe and consistent state. If an automated reviewer reports a possible issue, locate the supporting code and validate the finding in context rather than accepting it on authority.
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Review the tests as code
Ask whether each test establishes the required behavior, not merely whether it mirrors the implementation. Tests written or changed by the same agent may encode the same mistaken assumption as the code. OWASP states: “A passing test suite generated by the same agent that produced the code provides no independent assurance.”
Look for weakened or misleading coverage
- Check whether relevant tests were deleted, disabled, or made less precise.
- Inspect assertions for meaningful expected values and outcomes, rather than checks that only confirm execution did not crash.
- Watch for mocks or stubs that bypass the behavior the test is supposed to exercise.
- Ask whether the test would fail if the implementation violated the requested contract, and whether it might instead preserve an existing bug.
Add cases the agent may have missed
Use the change’s risk and contract to choose additional cases. Examples include malformed input, expired credentials, empty or maximum-size values, permission failures, and concurrent requests. These are prompts, not a universal checklist: test the boundary and failure conditions that matter to this change.
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Run the repository’s checks and interpret the results
Use the project’s documented test and validation commands rather than assuming one command works for every language or repository. Inspect the pull request’s automated checks and any unresolved merge conflicts, then run relevant checks in the environment the project supports. Record what ran and what did not; a skipped or unavailable check is not a passing check.
A green suite means only that the checks actually executed passed. It does not establish that the tests cover the requirement, that their assertions are correct, or that generated tests independently verify generated code. Combine test results with diff review and behavior-based reasoning.
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Apply stronger scrutiny to security-sensitive changes
Raise the review bar when changes affect authentication, authorization, cryptography, CI/CD, build or install scripts, deployment manifests, sandboxing, or network policy. Review permissions and trust boundaries closely, and use appropriate automated security checks alongside qualified human review. OWASP recommends heightened scrutiny for security-critical AI-generated code.
- Check whether the change adds network access or executes downloaded or otherwise external code.
- Inspect CI and deployment permissions, secrets exposure, and changes to what code is trusted to run.
- Verify authentication and authorization behavior on both success and failure paths.
- Consider whether security tests cover misuse and adversarial inputs, not just expected use.
AI-assisted review tools can help surface issues, but they do not transfer responsibility. If a review bot processes pull request descriptions, diffs, comments, or repository files, treat that content as untrusted input and keep the bot’s privileges appropriately limited. OWASP AISVS 1.0, Appendix C, includes controls for review bots and their privileges.
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Decide whether the change is ready to accept
Accept only when the code’s behavior matches the request, the full diff is understood, relevant tests and checks have been reviewed, and unresolved risks are addressed. If an important point remains unclear, ask for an explanation or revision rather than treating the agent’s confidence as evidence. Keep the final decision with a human reviewer.
Optional pull request review software may help organize findings and test results, but compare tools by whether they expose the complete diff and repository context, show findings alongside checks and conflicts, fit the team’s repository and policy setup, and use appropriate permissions. Product access and configuration vary; consult the current GitHub Copilot code review documentation or Codex documentation for the relevant availability and setup conditions.
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