Review an AI-generated pull request: intent, side effects and release evidence

A worked example: where the task becomes difficult

A fictional API change adds export filtering. The pull request includes a passing test but also broadens which records are returned to an authenticated user. The reviewer needs to inspect authorization, output format and pagination, not just confirm the new date parameter exists. Compare the patch with the approved request and check whether unrelated environment or dependency changes have been included.

Decisions to make before implementation

Separate implementation review, test evidence and release readiness. The author should explain what changed and why; the reviewer should independently check high-impact boundaries. Inspect removed lines as carefully as additions, especially deleted access checks or validation. Decide which evidence must be produced on a deployed environment and which can be established with controlled tests before merge.

A practical sequence for the work

Use the sequence below as a task boundary, not as a claim that the example has been executed. Work with approved inputs and the project’s actual architecture. If a required integration or permission is unavailable, keep that stage visibly incomplete rather than generating a plausible substitute result.

• Read the approved task and summarize the intended behavior independently.
• Inspect changed contracts, permissions, dependencies and external side effects.
• Check whether tests exercise failure paths and the original reported problem.
• Record blocking findings and release checks before approving the revision.

A detailed brief you can adapt for your agent

Replace the illustrative context with your approved facts and controlled inputs. Keep the stated boundaries when adapting the brief. The expected deliverable matters more than a particular tool name: ask for an explanation grounded in the inspected material and evidence for the requested outcome.

Review this export-filter pull request against the supplied acceptance criteria. Inspect authorization, pagination, output compatibility and deleted safeguards. Evaluate the tests without assuming their assertions are correct. Return file-specific findings with evidence and severity. Do not modify or merge the pull request during the review.

Failure modes that an attractive preview can hide

An AI-generated test can mirror the implementation so closely that both share the same mistaken assumption. Use a requirement-derived example with independently calculated expectations. Beware of large “cleanup” patches that obscure removed safeguards. A review performed before the last commit is not evidence for a materially changed final revision; tie approval and results to the reviewed commit.

Technical references: GitHub: pull request reviews

Acceptance checks and the evidence to retain

Keep the reviewed revision, actionable findings, evidence and unverified release conditions. An approval should identify the scope actually reviewed, not imply a complete security audit.

Specific answers

Common questions