How to review AI coding agent code with Copilot

How do I review code from an AI coding agent using GitHub Copilot on GitHub

Open the pull request on GitHub, request Copilot in the Reviewers panel, then read its comments line by line before you merge. Copilot leaves comment reviews, not approvals, so it helps you inspect the code, it does not replace your judgment.

The basic flow is simple. Create or open the pull request, find Copilot under Reviewers in the right sidebar, click Request, then wait for the review to finish. GitHub says this usually takes less than 30 seconds, and the review appears as normal PR comments you can reply to, resolve, hide, or react to.

Treat Copilot’s output like a fast first pass, not like a final gate. Its comments are useful for spotting missed edge cases, unclear code, or refactors that the agent should have made, but the pull request still needs a human to decide whether the change is correct for the repository, the release, and the current issue.

The part people get wrong is scanning only the summary. The summary can be helpful, but the review lives in the individual comments and suggested edits. Read the exact lines Copilot points to, because that is where the practical work is: naming mismatches, missing tests, weak error handling, and changes that look safe but break a nearby path.

If Copilot suggests a fix, decide whether the change belongs in the agent’s branch, in your branch, or nowhere. You can apply a single suggestion, group several suggestions into one commit, or use Fix with Copilot if your repository has Copilot cloud agent enabled and you want Copilot to implement the feedback directly. That is useful for small, local corrections.

When the suggestion is correct but incomplete, add the missing context yourself. A good pattern is to accept the code shape Copilot proposed, then tighten the part it could not infer, such as a product-specific validation rule, a service boundary, or a test that encodes an internal invariant. The AI can speed up the cleanup, but it does not know the whole business rule set unless you tell it.

When the suggestion is wrong, leave the comment unresolved until you have a reasoned answer. A quick thumbs down helps train the review system, and a written reply helps future readers understand why the suggestion did not fit. GitHub supports reactions, replies, resolve, and hide, so you can keep the discussion attached to the exact line that caused the issue.

After the agent pushes new changes, request a re-review if you want Copilot to look again. Copilot does not automatically re-review every new push unless automatic review is configured for new pushes, so a stale review can sit there while the code has already changed. That is the inconvenient part, and it is easy to miss in a busy pull request.

Automatic review is worth enabling when your team wants every pull request checked by default. GitHub lets you configure automatic code review for your own pull requests, a repository, or an organization, and you can also set review behavior for new pushes. Manual review still matters, because automatic review only changes when Copilot runs, not how carefully you read the result.

Custom instructions are the most useful way to make Copilot review the way your team works. Put repository-wide review guidance in .github/copilot-instructions.md, or add path-specific instructions for special areas like security, migrations, or UI code. GitHub says Copilot reads these instructions from the head branch, so you can test review guidance in the same pull request without merging it first.

If your repository uses agent skills or MCP servers, Copilot review can use that context too. GitHub says the review is more likely to use those tools when the skill directory is clearly review-focused, when instructions point Copilot at specific context, or when the pull request description references relevant IDs. That means the review can improve, but it also means the review session may depend on repository setup, not just the diff.

For a practical review, use the same checklist you would use on a human-authored pull request. Check correctness first, then tests, then edge cases, then readability. Look for behavior changes in error paths, missing null checks, overbroad refactors, and suggested edits that make one case better while quietly making another case worse. Copilot is good at obvious cleanup, and less reliable where the code meets product rules.

A concrete example helps. Suppose an AI coding agent adds a payment validation helper and Copilot flags that the helper assumes every currency uses the same decimal precision. Accepting the suggestion without checking would be a mistake, because the fix is not just “add a test,” it is “confirm the rule for each currency and then encode that rule in the test suite.” That is the kind of review that saves time later.

If you want to work faster, keep the conversation local to the pull request. Ask Copilot to review the branch, read the line comments, fix the branch, and re-review the same pull request until the discussion is quiet. That keeps the review history attached to the change, which makes it easier for the next human reviewer to see what was already checked and what still needs attention.

GitHub also exposes this workflow through the REST API by requesting copilot-pull-request-reviewer[bot] as a reviewer. That matters if you want to automate review requests from your own tooling on GitHub, but it does not change the core rule: someone still has to read the comments and decide whether the code is ready.

If you are reviewing an AI coding agent’s output, the goal is not to let Copilot “approve” the work for you. The goal is to use Copilot to surface problems quickly, then do the human part well, which is understanding the change in context, checking the behavior that matters, and making the merge decision with the full diff in front of you.