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How do you review an AI agent pull request quickly without rubber-stamping it? Use a short, time-boxed first pass: understand the requested behavior, identify risk, trace the changed code, and check what the tests and automated reviewers actually covered. Ten minutes is an organizing target, not a proven average or a guarantee. If a change is high-risk, unfamiliar, broad, or unclear, take longer or involve a specialist.
What this first pass can—and cannot—do
AI-authored code still has to meet the requirement, fit repository conventions, and behave safely. The fact that an agent wrote or substantially changed a pull request does not establish that it is correct—or that it is insecure. Treat the authoring method as context, not as a verdict.
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Use the checklist below to decide whether a change is understandable enough for a routine review. It is a triage process, not a validated ten-minute method or a substitute for deeper review where the consequences warrant it.
1. Orient to the change
Read the pull request description and the linked issue or task. Before examining implementation details, write down the intended behavior in one sentence. For example: “When a user with the editor role saves a draft, the app stores it without publishing it.”
- Does the description explain the problem and expected outcome?
- Is the issue or other source of requirements available and consistent with the PR?
- Does the diff appear limited to that purpose?
If you cannot state what the change is supposed to do, ask for clarification rather than inferring intent from the code.
2. Set the risk level
Check whether the change touches a boundary where a mistake could expose data, grant access, lose information, or trigger an unwanted action. Look for changes involving:
- Authentication, authorization, or account recovery
- Secrets, credentials, or sensitive user data
- Input handling, validation, or deserialization
- Payments, billing, or other money-related behavior
- Database migrations or irreversible data changes
- Public APIs or compatibility guarantees
- External calls, notifications, or other side effects
A high-risk area, unclear scope, or weak explanation is a reason to widen the review. Do not try to make consequential code fit a fixed time budget; bring in the relevant code owner or security specialist when appropriate.
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3. Read the diff, not just the summary
Inspect the actual changed files. An agent’s summary is useful orientation, but it is not evidence that every edit is necessary or that the summary is complete.
- Look for unrelated edits, unexpected files, or broad rewrites that make the intended change harder to isolate.
- Check changed defaults, fallback behavior, and configuration that may alter behavior outside the obvious path.
- Notice generated files and dependency changes; verify they are expected and consistent with project practice.
- Inspect instruction or configuration files that could change how future agents operate, including repository guidance.
4. Trace the behavior through the code
Follow the changed code from its entry point through callers, data transformations, and effects. Compare what it does with the requirement you wrote down—not merely with the agent’s explanation.
- Check relevant edge cases, error paths, and behavior when inputs or dependencies are missing or invalid.
- Confirm permission checks apply at the point where protected data or actions are reached.
- Consider compatibility with existing behavior, callers, and stored data.
- Look for side effects that happen earlier, more often, or under broader conditions than intended.
If you cannot follow the data flow or determine what a branch does, that uncertainty is itself a reason to ask for explanation or deeper review.
5. Check tests and other evidence
Look for tests that exercise the changed behavior as well as relevant failure cases. Review CI results, and run project-required checks when appropriate. A passing suite is evidence that tested conditions passed; it does not prove that the tests cover the requirement or that the implementation is correct.
- Do tests assert the intended outcome, rather than merely exercising the new code?
- Are important negative cases covered, such as denied access or invalid input?
- Do the project’s required checks pass, and are any failures explained?
6. Inspect security-sensitive paths
For changes involving access, data, secrets, input, or external services, verify validation, authorization, secret handling, and what information can leave the system. The empirical study “Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub” treats security in agentic pull requests as a substantive research concern; that is not evidence that every agent PR is insecure, and this checklist cannot guarantee security.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Check what automated review covered
Automated feedback can help, but check its scope and role instead of treating it as the human decision. GitHub says: “By default, Copilot leaves a ‘Comment’ review, not an ‘Approve’ review or a ‘Request changes’ review.” See GitHub Docs on using Copilot code review for its review behavior and workflows.
Coverage can also have gaps. GitHub’s documentation lists dependency-management files, log files, and SVG files among file types Copilot code review excludes. Check relevant excluded changes yourself. Copilot can use applicable repository instructions and skills, and configured MCP context; GitHub says it reads applicable instructions and skills from the pull request’s head branch, the branch containing the proposed changes. See GitHub Docs on Copilot code review coverage and context.
Teams can put shared review expectations in repository-wide guidance and apply different standards with path-specific instructions. An AGENTS.md file can also describe architecture boundaries and review priorities. GitHub explains these instruction approaches in its guide to using Copilot code review across the pull request lifecycle. Instructions improve context; they do not replace checking the diff or following your team’s approval rules.
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Approve only if you understand the intended behavior and risk and the project’s requirements are met. Otherwise, leave a specific question or request changes. Point to the behavior or file that needs attention and explain what needs clarification or verification. For consequential or unfamiliar code, ask the relevant code owner for a deeper review.
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