How to review pull requests from an AI coding agent

How do I review pull requests from an AI coding agent now

Review AI-authored pull requests the same way you review human code, then add an AI pass for a baseline check. GitHub’s current guidance says to review with context, run the checks that matter, and decide what earns its way into main, whether the code came from you, a teammate, or an AI agent.

Start with the shape of the change. If the pull request is huge, split it before you argue about line-by-line details. GitHub’s code review guidance explicitly praises smaller dependency-ordered pieces because review happens in smaller logical chunks and becomes more accurate. That matters more with AI agents, because they can produce broad edits quickly, and broad edits are harder to reason about.

Then read the PR as a story, not as a checklist of files. Ask what problem it solves, what parts of the system it touches, and what assumptions it makes. The inconvenient part is that an AI agent may produce code that looks tidy while hiding broken behavior across files, missing edge cases, or a mismatch between the implementation and the issue it was supposed to solve. OpenAI’s guidance on agent safety says to keep the agent inside clear technical boundaries and preserve logs so you can audit what it did. That is a good model for review too: constrain the blast radius, then inspect what happened.

Do not start with comments. Start with verification. Run the tests that cover the changed path, and if the repo has lint, type checks, security scans, or build checks, run those too. GitHub’s review page says to run the checks that matter. OpenAI’s engineering guidance says AI code review gives confidence that you are not shipping major bugs, but it does not make the process automatically faster when it finds real issues.

Review the riskiest parts first. If the agent touched authentication, billing, permissions, migrations, deletes, retries, concurrency, or anything that can silently fail, inspect those diffs before style changes. GitLab’s review guidance makes the same practical distinction when it routes logic-heavy changes to the right reviewers. That is the part people get wrong: they spend time on naming and formatting because those are easy to see, then miss the actual failure mode.

Use the AI agent as a second pass, not as the authority. On GitHub, Copilot code review can be requested from the pull request sidebar, and it can also be set to review automatically. Its comments are visible to humans, but Copilot does not see human comments back in that thread. That means the workflow is one-way: let the agent give you a first sweep, then you decide whether its feedback is useful.

If you use GitHub, request Copilot like any other reviewer, then read its comments as prompts, not verdicts. GitHub’s docs show the review request in the sidebar and the GitHub CLI path with gh pr create --reviewer @copilot. GitHub’s public code review page also frames Copilot as another set of eyes, alongside your team’s judgment.

If you use GitLab or Bitbucket, the mechanics are different, but the review posture is the same. GitLab lets you request reviews on merge requests and choose whether comments are published immediately or as part of a review. Bitbucket supports code suggestions, tasks, and a Viewed state for files, and it resets Viewed when new commits change reviewed files. That last behavior is useful, because it forces you to re-open files that changed after you marked them done.

The part people get wrong is trusting the agent on the surface and skipping the merge risk. AI review can catch obvious problems, but it will not own the final merge decision, and it will not understand your product constraints unless they are already encoded in tests or checks. OpenAI’s engineering guidance is explicit: engineers can delegate the initial code review to an agent, but they still own the final review and merge process.

A practical sequence looks like this:

1. Open the pull request and read the summary.
2. Check the changed files first, then the tests, then the dependency or config changes.
3. Run the relevant checks locally or in CI.
4. Ask the AI reviewer for a first pass if your platform supports it.
5. Verify every comment against the code and the product requirement.
6. Leave human comments where the design, risk, or missing test coverage matters.
7. Approve only when you can explain the change yourself.

If something goes wrong, prefer small corrective commits over trying to rescue a giant AI-generated PR in place. GitHub’s review model supports requested changes, comments, and approvals. Bitbucket also supports tasks that can act as a checklist for the author. In practice, explicit action items work better than vague feedback like “looks off,” because the next person who reads the review can see exactly what still blocks merge.

Keep one more thing in mind: AI-generated code can still introduce vulnerable patterns, secrets, and dependency issues. GitHub’s Copilot coding agent materials say this directly. So even if the change passed an AI review, you still need a real review for security, data handling, and integration behavior. That is not extra ceremony, it is the part that prevents a clean-looking diff from becoming a bad deploy.

If your team is new to this, set a simple rule: AI can draft, pre-review, and summarize, but humans approve. That keeps the workflow fast without pretending the agent is accountable. OpenAI’s guidance on coding agents says to preserve clear technical boundaries and hand control back to the user. That is the right default here too.

For teams that want a baseline checklist, the shortest version is this: read the diff, run the checks, inspect the risky files, ask an AI reviewer for a first pass, then make the merge decision yourself. Anything less is usually just reviewing the parts that are easiest to review.