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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsTo review code written by an AI agent, first establish what the change is supposed to do; then inspect the patch and surrounding code, verify tests and other checks, assess security and project conventions, and make a human responsible for the approval. The agent’s summary is a claim to check—not evidence that its change is correct.
Start with the intended outcome, not the agent’s summary
Before opening individual lines, confirm the repository, pull request title, author, and branch. Read the request and description to understand the outcome the change is meant to achieve. This gives you a standard against which to judge the implementation; a fluent summary does not prove the change meets it.
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Make the review question specific. For example: “Explain how this change affects sign-in,” or “Check whether the new error path releases the database connection.” If a reviewer or automated tool reports a problem, ask which code supports the finding, then inspect that code yourself. OpenAI’s Codex pull-request review guidance gives examples of these focused questions and recommends checking findings against the relevant code.
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Inspect the patch and trace its effects
Read the changed files and relevant lines in the diff. For consequential edits, follow the behavior into surrounding code: a change to a function, data flow, or error path can affect callers and services that are not obvious from the patch alone. Check whether the implementation matches the requested behavior, whether unrelated files changed, and whether the code follows the repository’s conventions.
#1 Best Overall
Review comments and AI-generated findings as leads, not verdicts. Validate each important claim against the current source. If an agent proposes a fix, inspect the new diff too; a plausible explanation or a previously valid finding may no longer match the latest revision.
Verify tests, checks, and the revision you are approving
Look at the test results and other required checks, and check for unresolved merge conflicts. Passing tests are useful execution evidence, but consider what they actually exercise: a test that only confirms a narrow or incidental condition may not demonstrate the behavior the request requires. Where coverage is missing or ambiguous, use the project’s normal workflow to request clarification or additional evidence.
Rank #2
Make sure checks and review feedback apply to the revision you intend to approve. GitHub documents that a new review may need to be requested after new commits unless the repository is configured to review new pushes. Its documentation also describes review-effort settings and repository-level instructions; availability and configuration can change. See GitHub’s Copilot code review documentation for current details.
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Scale the review to the change’s risk and complexity. Spend particular attention on security-sensitive behavior, complex logic, and changes that cross service boundaries. Check access control, input and output handling, error paths, data handling, and dependency changes against your organization’s secure-coding standards. Record and triage issues through the usual process rather than treating an AI-generated fix as a substitute for that process.
NIST’s July 2024 Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile recommends code review and/or analysis against organizational standards, with AI-specific considerations included in secure-coding practices. It says automated methods can reduce the effort and resources needed to detect vulnerabilities, but does not provide a defect-rate or effectiveness figure for reviewing AI-agent diffs.
When the agent can take actions
If the agent can do more than propose code, review the action as well as the patch. Check the intended target, action, tool arguments, identity, and whether the action stays within its approved scope. OpenAI’s guardrails and human review guidance identifies out-of-scope hosts, credential theft, persistence, data exfiltration, destructive changes, production access, and attempts to bypass policy as actions to deny; ambiguous or high-risk actions should pause for human approval. Applications built with the Responses API or Agents SDK do not automatically inherit Codex Auto-review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use automated review as a second pass
Automated review can help surface issues, but it does not sign off on the change. GitHub describes “Lite” review effort as targeted feedback on conspicuous issues such as bugs, vulnerabilities, and style inconsistencies, and “Balanced” as deeper analysis for complex logic, security-sensitive code, and cross-service changes. Repository-specific instructions can add context, but the findings still need to be checked against the actual patch and source.
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Keep approval with a named human
A developer should own the decision to accept the change and remain accountable for its security and maintainability. OWASP’s Secure Coding with AI Cheat Sheet states, “AI tools do not accept responsibility for the code they generate.” It adds that the developer who accepts and commits the code does.
NIST’s NCCoE DevSecOps reference model similarly says AI-generated corrective actions should not change software, configurations, or system state without review and approval through established processes. Treat the agent as a contributor whose work needs review, not as the person accountable for the merge.
Quick Recap
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