An agent-authored pull request is reviewable when it tells you what was meant to change, what actually changed, how that result was checked, what remains uncertain, and what decision it needs from you. Treat its description as an evidence packet—not as proof that the code is correct. Use the contract below to make the work legible, then verify important claims against the diff, tests, repository guidance, and your team’s context.
What the agent should put in the pull request
Ask for a compact, consistent description that connects the requested outcome to the implementation and its verification. Repository guidance can supply project conventions, organizational context, and test commands; the pull request should still state what this particular change did and what the agent actually checked. OpenAI’s Codex introduction describes repository instructions as a way to give agents relevant context and guidance.
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- Goal: State the intended user-visible or system behavior. Link to the task or issue when available.
- Scope: Name the affected components and any deliberate exclusions.
- Change summary: Explain the important behavior changes, rather than paraphrasing files or listing every edit.
- Verification: Name each command or check actually run and its result. Identify relevant checks that were not run; do not imply otherwise.
- Risk and impact: Call out relevant data, security, compatibility, migration, operational, and rollback implications.
- Known gaps: Surface assumptions, unresolved questions, shortcuts, or behavior the agent could not verify.
- Reviewer request: Specify the decision, domain knowledge, or follow-up needed from the human.
This is a practical synthesis of documented workflows, not a formal standard published by OpenAI or GitHub. Its purpose is traceability: a reviewer should be able to connect the task, the changed code, and the claimed outcome without treating a polished summary as a substitute for inspection.
How to review the pull request
Use the description to focus your review, then check its claims against the source and the repository’s actual state. OpenAI’s current Codex review guidance similarly directs reviewers to examine the description, changed files and relevant diff lines, generated findings, tests and checks, and unresolved merge conflicts, and to validate findings against the code (Review pull requests with Codex; accessed October 7, 2026).
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- Confirm identity and intent. Check the repository, PR title, author, branch, and task reference. Make sure the stated goal matches the change you were asked to review.
- Read the summary, then inspect the relevant diff. Review changed files and surrounding code where needed. A generated explanation can help you navigate, but it cannot establish that the implementation matches the intended behavior.
- Verify findings in context. Check review comments or generated findings against the actual code and related behavior. Resolve whether each finding is real, already handled, or still an open risk.
- Check verification evidence. Inspect the named commands and results, status checks, and any tests the agent says it did not run. Confirm the checks apply to this revision rather than assuming that a successful result covers untested behavior.
- Check merge state and repository guidance. Look for unresolved conflicts and compare the change with trusted project conventions and instructions.
- Record a decision. Approve, request changes, or defer. State any unresolved decision or expertise needed so the next step is clear.
What passing tests do—and do not—tell you
A passing check is evidence that the named check ran successfully under its conditions. It does not, by itself, show that the change satisfies unstated product requirements, handles operational realities, or is safe for data and users. Those questions require context beyond the check result.
Read a verification claim narrowly: what command or status check ran, on which change, and with what result? If the PR omits a result or says a check was not run, treat that as missing evidence rather than silently filling the gap. Decide whether the omission matters for this change and ask for the needed check or review before approving.
Keep agent permissions and review policy inside clear boundaries
The review contract is only one part of safe agent use. OpenAI’s May 8, 2026 article Running Codex safely at OpenAI states: “Security teams need ways to govern how agents operate: what they can access, when human approval is required, which systems they can interact with, and what telemetry exists to explain their behavior.” In practice, teams should set clear access boundaries, require explicit human approval for higher-risk actions, and retain telemetry that helps explain what the agent did.
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Notice changes to the instructions that govern review
There is a specific trust-boundary issue for GitHub Copilot code review: GitHub’s documentation says it reads custom instructions and agent instructions from the pull-request head branch (Using GitHub Copilot code review; accessed October 7, 2026). That is a documented Copilot behavior, not a universal rule for every coding agent. When a PR changes those instructions, inspect the proposed change and decide which revision your team trusts to define review policy. Do not let a contribution silently redefine the rules by which it is assessed.
Do not treat the authoring agent’s self-review as independent evidence
OpenAI describes an internal engineering loop in which Codex reviews its own changes, receives additional specific reviews, responds to human or agent feedback, and iterates (Harness engineering: leveraging Codex in an agent-first world). That account illustrates self-review as one iteration in a larger workflow; it does not establish correctness. Ask for additional review where the risk warrants it, and keep the human decision distinct from the authoring agent’s own assessment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the decision explicit
Once you have checked intent, implementation, verification, and risk, leave a review outcome that tells the team what happens next:
- Approve when the change meets the goal and the remaining risk is acceptable under your team’s standards.
- Request changes when a concrete defect, missing check, or unresolved requirement must be addressed.
- Defer when the PR needs a decision, context, or specialist review you do not have. Name what is missing.
GitHub reported in 2026 that Copilot code review had processed more than 60 million reviews, growing tenfold in less than a year (GitHub Blog, “Agent pull requests are everywhere. Here’s how to review them.”). That is GitHub’s reported product figure, not evidence that any particular agent review is accurate or that review volume guarantees quality. No general defect-rate or review-time figure established here can tell an individual team whether an agent-authored PR is safe to merge; the decision still depends on the change and its evidence.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteRecent preprints examine acceptance and review effort and AI-to-AI review, but their reported findings should not be turned into universal rules for a team’s process. One study reports that approximately 1.6% of identified agent-authored PRs in its dataset received cross-product AI-to-AI review, and that this volume rose by more than two orders of magnitude between 2025-Q1 and 2025-Q3 (AI-to-AI Code Reviews of GitHub Pull Requests, 2026). The figure describes that study’s dataset, not a recommended review rate or a measure of review quality. A separate preprint studies predictors of acceptance and review effort (Predicting Acceptance and Review Effort in Human and Agent Pull Requests, July 13, 2026); its existence does not establish a universal causal rule for how a team should approve changes.
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