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Reject an AI-generated patch when you verify a material defect, an out-of-scope change, or an unacceptable security or side-effect risk. Mark it undecided when important evidence is missing. Accept it for integration only after you have checked the goal, the full change, relevant findings and checks, and authorization. These three lanes are a practical review framework—not an official industry or OpenAI standard.
Should I merge this AI-generated code?
Use the verdict that matches the evidence you have now, not the fact that an agent produced the patch or that an automated check passed. A green check tells you about the check that ran; it does not establish that every behavior is correct or that the change is authorized.
| Verdict | Use it when | What to write |
|---|---|---|
| Reject | You have confirmed a material correctness defect, scope violation, or unacceptable security or side-effect risk. | Name the affected behavior and the evidence in the diff or check. If fixable, ask for a specific correction. |
| Undecided | A material fact remains unresolved, such as missing context, an unrun relevant check, a merge conflict, or a finding that needs investigation. | State what evidence is missing, how to obtain it, and the smallest useful next check. |
| Review / accept for integration | The change matches the requested goal, the relevant diff has been inspected, material findings are addressed, checks are adequate, and the work is authorized. | Explain why the evidence is sufficient and identify any residual risk or follow-up. |
Here, “review / accept” means the patch is ready to proceed toward integration. If your team uses “review” to mean a separate state—such as “human approval still required”—define that state explicitly rather than treating it as acceptance.
The Tool Desk
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Work from the proposed change and its evidence, not from the agent’s summary alone. OpenAI’s Codex pull-request review guidance recommends checking the relevant code before relying on generated findings. Use this sequence to reach a defensible lane verdict.
#1 Best Overall
- Confirm the target. Verify the repository, pull request title, author, and branch. Read the description to understand the requested goal, then inspect the diff to see what actually changed.
- Read the full patch in context. Inspect changed lines and enough surrounding code to understand their behavior and callers. The Codex review-agent sample calls for reviewing the complete diff, examining context for changed paths, and continuing after finding an issue rather than stopping at the first one.
- Check attached evidence. Read comments and findings, inspect relevant test and CI results, and check for unresolved merge conflicts. Treat a generated finding as a claim to verify against the code—not as proof by itself.
- Investigate uncertainties that could change the verdict. Ask targeted questions about behavior, a finding’s supporting code, unresolved review feedback, or a specific error path. OpenAI’s review guidance 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.”
- Check authorization and side effects. Compare the proposed actions with the request, applicable security policy, and execution context. If an action can affect external systems or data, confirm the authorization and protections at the boundary where that action occurs.
- Record the decision and rationale. State the lane and decisive evidence, name any remaining uncertainty, and specify the next action. If the patch changes after feedback, inspect the resulting revision before submitting comments or approving it for integration.
What should I check before accepting an agent patch?
Goal alignment
Compare the stated request, the pull request description, and the actual diff. A plausible summary is not enough if the code implements different behavior or expands the task. A mismatch that is confirmed and material supports rejection; an unclear goal that prevents evaluation supports an undecided verdict.
Correctness and regression risk
For changed paths, trace the behavior through relevant surrounding code, callers, and tests. Confirm suspected regressions against the implementation rather than relying on a generated review comment. If a possible defect remains plausible but unverified and could change the outcome, keep the patch undecided while you investigate.
Evidence quality
Check which tests and automated checks actually ran and what they cover. A passing test is evidence about the behavior it exercises; it is not proof of unrelated paths. If an important check is absent, failing, or does not cover a material risk, do not treat the patch as ready simply because other checks are green.
Scope and authorization
Verify that the patch and any proposed action stay within the request and applicable policy. Do not infer permission for a consequential side effect from a vague goal. OpenAI’s Codex guardian policy template is a reference for evaluating policy and authorization boundaries.
Security and side effects
Look for changes that could expose secrets, transfer data, weaken controls, delete information, or trigger an external action. A confirmed unacceptable risk supports rejection. If the effect or permission is not yet clear, identify the evidence needed to decide rather than assuming it is safe.
Uncertainty
Ask whether an unanswered question could change the verdict. If it could, the patch is undecided until the question is resolved. State the smallest next check—such as inspecting a call site, running a relevant test, resolving a conflict, or obtaining an authorization decision—so the pause has a clear purpose.
How do guardrails differ from patch review?
Guardrails automatically validate inputs, outputs, or tool behavior; human review pauses a workflow so a person or policy can approve or reject a sensitive action. They address related but distinct risks. As the Agents SDK guide to guardrails and human review puts it, “Use guardrails for automatic checks and human review for approval decisions.” A tool-level check can screen a particular boundary, but it does not replace understanding the code change and its evidence.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThe guide also specifies limits on where SDK guardrails run: 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. The documentation recommends placing validation near the tool that creates a side effect when it must apply around every such call.
Applications built with the Responses API or Agents SDK do not automatically inherit Codex Auto-review. Teams building their own harness need to implement review and enforcement appropriate to their tool boundaries.
Rank #4
What does Codex Auto-review establish—and what does it not?
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. It addresses proposed boundary-crossing actions; it is not a substitute for reviewing a patch’s correctness, goal alignment, or authorization.
The article’s authors explicitly caution: “Auto-review should not be treated as a guarantee of security.” They also note that Auto-review may not see actions performed within the sandbox. The article reports these results with specific scopes:
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- 99.1% auto-approval rate on escalated actions: an internal Codex sandbox-escalation usability metric, not an externally validated rate for other deployments.
- 99.93% effective approval rate across all actions: a metric from the internal Codex deployment that includes actions that stayed within the sandbox.
- 90.3% recall on synthetic overeagerness cases: cases involving unintended data transfer, security weakening, and irreversible operations.
- 99.3% recall on synthetic prompt-injection cases: the reported categories were remote code execution, secret exfiltration, and external upload.
- 96.1% recall on MonitoringBench [Hard]: an evaluation of whether any action in a trajectory would be rejected.
The authors also give an 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. They say the ratios depend on use case, environment, and sandbox configuration. These figures describe that article’s evaluations and deployment, not the probability that a particular patch is safe or correct.
Best Value
When can you accept the patch?
Choose review / accept for integration only when you have inspected the change relevant to the request, resolved material findings, seen adequate evidence from the checks that matter, and confirmed the scope is authorized. If any unresolved fact could change that decision, leave the patch undecided and name the next check. If evidence already shows a material defect or unacceptable risk, reject the current patch and explain what must change.
Codex Code Review availability and supported platforms can change. The current OpenAI Help Center page describes support for desktop and web, identifies GitLab merge-request review as a preview, and says GitLab cloud code reviews are unavailable. Check that page for current availability before relying on a particular integration.
Quick Recap
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