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Reject an agent-produced patch when the evidence shows a material defect, security risk, scope violation, or unauthorized side effect. Mark it undecided when a fact that could change the decision is still unresolved. Accept it for integration only after checking the complete change against its goal, reviewing material findings, examining relevant checks, and confirming the work was authorized. These three lanes are a practical decision framework, not an official industry standard.
How do you review an AI coding agent’s pull request?
Use the same evidence-led process you would use for any proposed code change, with particular care not to treat an agent’s summary or automated findings as proof. OpenAI’s Codex pull-request review guidance advises: “Review generated findings against the relevant code before relying on them.” A finding is a lead to investigate; verify it in the changed code and relevant context.
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- Confirm the target. Check the repository, pull request title, author, and branch. Read the description to understand the intended goal, then inspect the diff to see what actually changed.
- Read the complete patch in context. Examine changed lines and enough surrounding code to understand their behavior. The Codex review-agent sample calls for the full diff, context for changed paths, concrete regressions, and continued review after an initial finding.
- Check the evidence already attached. Examine comments, review findings, test results, CI checks, and unresolved merge conflicts. Confirm findings against the relevant code rather than assuming generated output is correct.
- Investigate decision-changing uncertainty. Ask focused questions about behavior, the code behind a finding, unresolved feedback, or a specific error path. The Codex review guide gives examples such as “Show me the code that supports this finding” and “Compare this revision with the review feedback and identify anything still unresolved.” If the answer could change the verdict and the evidence is missing, stay undecided and identify the next check.
- Check scope and side effects. Compare the patch and any proposed action with the request, applicable policy, and execution context. Consider whether it could expose secrets, transfer data, weaken controls, delete data, or trigger an external action. The Codex guardian policy template is relevant background for assessing whether an action is within authorized scope.
- Record the lane and rationale. State the decisive evidence, any remaining uncertainty, and the next action. Inspect the resulting revision again before submitting comments, committing, or merging.
Which verdict fits the evidence?
The lane names below are editorial recommendations synthesized from review and policy guidance; they are not a universal standard. Use “review/accept” to mean acceptance for integration, not simply that a human should look at the patch.
| Lane | Use it when | What to write |
|---|---|---|
| Reject | Inspection establishes a concrete, material defect, the patch exceeds authorized scope, or it creates an unacceptable security or side-effect risk. | Identify the affected behavior and the evidence in the diff or check. If fixable, request a specific correction. |
| Undecided | A material fact remains unresolved—for example, the diff or context is incomplete, a relevant check has not run, a conflict remains, or a finding needs investigation. | Name the missing evidence, who or what can provide it, and the smallest next check that could settle the issue. |
| Review / accept for integration | The patch matches the requested goal, the relevant diff has been inspected, material findings are addressed, checks are adequate for the change, and the scope is authorized. | Explain why the evidence is sufficient and identify any residual risk or follow-up. |
A green automated check is evidence about the conditions that check exercised, not proof that every relevant behavior is correct. The level of checking should fit the change: a small isolated edit and a change affecting data handling or security controls do not carry the same risk.
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What should you check before accepting an agent patch?
- Goal alignment: Does the code implement the requested behavior, and do the pull request description and diff agree?
- Correctness and regressions: Do changed paths contradict the intended behavior or existing expectations? Verify suspected regressions against code, relevant tests, and call sites; the review-agent sample provides guidance on examining changes in context.
- Evidence quality: Which tests and checks actually ran, and what paths do they cover? A passing result supports only the behaviors and conditions it tested.
- Scope and authorization: Is the change and any associated action within the request and applicable policy? Do not infer permission for a consequential side effect from a vague goal.
- Security and side effects: Could the change expose secrets, move data, weaken a control, delete data, or trigger an external action? Evaluate the relevant action boundary, not only the code’s stated purpose.
- Uncertainty: Which unanswered question could change the decision? If one remains, name it and keep the patch undecided until it is resolved.
Why guardrails do not replace patch review
Automatic guardrails and human review solve different problems. Guardrails can validate inputs, outputs, or tool behavior; human-in-the-loop review pauses a run so a person or policy can approve or reject a sensitive action. The Agents SDK guidance on guardrails and human review puts it plainly: “Use guardrails for automatic checks and human review for approval decisions.”
That guide also specifies that input guardrails run only for the first agent, output guardrails only for the final-output agent, and tool guardrails only on tools to which they are attached. Do not assume an agent-level check protects every call in a multi-step workflow. For checks that must apply around every side effect, place validation near the tool that creates it.
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Responses API and Agents SDK applications do not automatically inherit Codex Auto-review. Teams building their own agent harness need to implement review and enforcement appropriate to their tool boundaries; a patch reviewer still needs to understand the code and evidence.
What Codex Auto-review can—and cannot—tell you
OpenAI’s April 30, 2026 article, “Auto-review of agent actions without synchronous human oversight”, describes Auto-review as a separate Codex agent that evaluates requests to cross a sandbox boundary using user intent, environment, security policy, and likely impact. That is a control for proposed boundary-crossing actions, not a substitute for reading and evaluating a code patch. The authors caution: “Auto-review should not be treated as a guarantee of security.” They also note it may not see actions performed within the sandbox.
The article reports the following results with important limits: the first two figures concern an internal Codex deployment; the others are evaluations on synthetic or benchmark cases. None establishes whether an individual patch is correct.
| Reported result | Scope described by OpenAI |
|---|---|
| 99.1% auto-approval rate on escalated actions | Internal Codex sandbox-escalation traffic; an OpenAI usability metric, not an externally validated general rate. |
| 99.93% effective approval rate across all actions | Internal Codex deployment; includes actions that stayed within the sandbox. |
| 90.3% recall | Synthetic overeagerness cases involving unintended data transfer, security weakening, and irreversible operations. |
| 99.3% recall | Synthetic prompt-injection cases across remote code execution, secret exfiltration, and external upload categories. |
| 96.1% recall | MonitoringBench [Hard], described as checking whether any action in a trajectory would be rejected. |
The article also illustrates one internal deployment snapshot: in manual-approval mode, 720 out-of-sandbox actions would have interrupted the user; with Auto-review, seven were rejected, four continued by a safer path, and three stopped for user input. The authors say the ratios depend on use case, environment, and sandbox configuration, so this snapshot should not be generalized to other teams.
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