How to review AI coding agent pull requests

how should I review pull requests from AI coding agents

Review them like any other change, but assume the first draft can be plausible and wrong. Start with the PR description, the issue, and the expected behavior, then compare that intent with the diff. GitHub’s pull request flow is built around discussion, file-level review, and checks, so use all three, not just the rendered diff.

The first question is simple: does this change solve the stated problem without widening the scope AI agents often add extra helpers, new abstractions, or unrelated cleanup because those patterns look tidy. Google’s review guidance says over-engineering is a real smell, and the reviewer’s job is to protect code health, not to reward volume of code changed.

Read the diff in the places where mistakes are expensive. That means authentication, authorization, payments, file handling, migrations, API boundaries, and any code that touches user data. When the change is large, review it in slices: one file, one behavior, one risk at a time. GitHub supports line comments and file discussions for exactly that kind of review.

Do not trust passing tests alone. Run the code yourself when the branch affects runtime behavior, then exercise the path the agent claims to have fixed. A green CI run can still miss a broken assumption, a bad feature flag default, or a change that only fails with real inputs. GitHub explicitly treats checks as one part of the review surface, not the whole decision.

Check that the tests actually prove the behavior, not just that they execute the new line once. Good AI-generated tests often mirror the implementation too closely, which gives false confidence. Look for assertion quality, negative cases, and boundary cases. If a change adds a new branch, ask what happens when the input is empty, malformed, duplicated, stale, or partial.

Inspect data and dependency changes with extra care. A model can update a package, add a schema field, or rewrite a query while missing the operational cost. GitHub recommends reviewing dependency changes specifically for vulnerabilities and impact before they reach production, and that same discipline applies to migrations, secrets handling, and generated code.

The part people get wrong is reviewing for style before correctness. Clean naming and neat structure are useful, but they are not evidence that the behavior is right. A polished AI patch can still introduce a silent failure, an insecure branch, or a bug that only appears under load. Review the behavior first, then the implementation shape, then the polish. Google’s guidance also says the style guide matters, but only after the substantive issues are clear.

A useful review sequence is: open the PR, read the summary, identify the user-facing promise, scan the tests, inspect the risky files, then check out the branch locally. GitHub documents local checkout as a normal part of review for resolving conflicts, testing changes, and understanding the branch in context. Use that step when the diff is too abstract to trust from the browser alone.

Leave comments that are actionable and specific. Say what is wrong, why it matters, and what evidence would satisfy you. Google’s reviewer guidance recommends comments that explain the reasoning behind a suggestion, and GitHub reviews let you attach that feedback to exact lines. That combination is better than broad “LGTM” pressure, because AI-generated code often needs precise correction, not vague approval.

Use request changes when the agent missed a requirement, introduced a bug, or left uncertainty in a critical path. Use approve only when the change is understandable, the tests match the behavior, and the remaining risks are acceptable for the repository’s standards. The reviewer’s job is to keep code health improving over time, not to move fast by ignoring small misses that add up later.

When the PR is large, ask for a smaller one. GitHub supports stacked pull requests for breaking large work into reviewable pieces, and that is often the right move for AI-generated changes too. Smaller PRs make it easier to confirm intent, isolate bugs, and tell whether the agent understood the task or just produced a convincing patch.

A practical checklist helps. Confirm the PR matches the task, inspect the core logic manually, run the relevant tests, check edge cases, verify any dependency or schema changes, and make sure the comments explain why the fix is correct. If the answer to any of those is “I did not check,” do not treat the review as complete. For shared workflow context, DevConnect keeps the testing exchange simple and free, and the same discipline applies to review: do the work, verify the work, then approve it. https://devconnectplatform.com

The inconvenient part is that AI can make a PR look finished before it is finished. That means reviewers have to spend more time on reasoning and less time on surface appearance. The goal is not to distrust every agent output, it is to verify the behavior that will ship. If the code is correct, the review will be boring in the right way. If it is not, the review is where you catch it.

If your team uses a template, make it ask for the task, the risky areas, the tests run, and the reason the author believes the change is safe. That forces the reviewer to compare claims with evidence instead of reading a polished story. In practice, the strongest AI PR reviews are the ones where the reviewer can answer one question: what would break if this patch were merged unchanged