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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Make an agent-written pull request easier to trust by pairing a clear account of the change with verifiable evidence: what it was meant to do, which parts the agent changed, what checks actually ran, and what risks remain. An AI reviewer can help find issues, but it cannot replace an accountable human engineer. Review the diff against the stated intent, verify that tests and security controls were not weakened, and raise the bar for changes to security-sensitive code.
What should a trustworthy pull request tell reviewers?
A useful review packet is concise, specific to the change, and easy to check against the repository. It is not a claim that the code is correct. Ask the author to cover these points in the pull request description:
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- Intent: State the user need or engineering problem and the behavior the change is supposed to produce.
- Scope and ownership: Identify the files or components changed, what the agent generated or modified, and the human owner who understands and stands behind the change.
- Approach: Explain implementation decisions and any alternatives that affect architecture, compatibility, or maintainability.
- Validation evidence: List the commands, tests, builds, and security checks actually run, with their results. Explicitly identify checks that were not run. Do not report a check as passing unless its result is known.
- Risk and reviewer focus: Point to sensitive data handling, permissions, security-sensitive paths, failure modes, edge cases, and areas that need local system knowledge or human judgment.
- Change integrity: Confirm that tests, linting, builds, and security controls were not removed, disabled, or bypassed to obtain a green result.
Compare the packet with the actual diff. A description can clarify intent, but it cannot establish that the implementation matches that intent.
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How should a human review an agent-written change?
- Start with the intended behavior. Identify the expected outcome and the conditions under which the code will run. Use that to decide which production paths, interfaces, and failure cases deserve attention.
- Trace the diff through the system. Inspect changed code and its callers, data flows, permissions, and error handling. Look for behavior beyond the stated scope, unexpected dependencies, and assumptions that are not documented in the packet.
- Inspect the tests as code. Check that tests exercise the relevant behavior and meaningful edge cases. Confirm that existing tests or checks were not deleted, relaxed, skipped, or reconfigured in ways that make the result look successful without validating the change.
- Match every validation claim to evidence. Verify which checks ran and whether their results apply to the current commit. Treat “not run” as an open risk, not as a pass.
- Decide whether the evidence is sufficient for the risk. A routine change and a change to authorization logic do not warrant the same review depth. Request additional tests, security review, or another reviewer when the consequences of an error justify it.
GitHub’s practical guidance also recommends that an author inspect agent-generated changes before requesting review and calls out CI gaming, such as removing tests or disabling checks, as a risk to look for. GitHub’s guidance on reviewing agent pull requests discusses those checks.
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Which changes need stronger security review?
Raise the review bar when a pull request changes security boundaries or controls. OWASP’s AISVS 1.0 Appendix C names authentication, authorization, cryptography, IAM policy, CI/CD workflows, deployment manifests, and sandbox or network policy artifacts as areas needing elevated attention.
- Route sensitive changes for security sign-off or two-person review, according to the repository’s risk and approval policy.
- For critical security behavior, consider differential fuzzing or property-based tests that cover input validation, authorization logic, and deserialization safety.
- Make automated security testing part of the pull-request workflow. AISVS lists static and interactive application security testing (SAST and IAST), dynamic application security testing (DAST), secret scanning, infrastructure-as-code scanning, and software composition analysis.
- Block critical findings unless an authorized human approves a written exception. AISVS gives CVSS 9.0 or the organization’s equivalent severity threshold as an example; it is not a universal severity policy.
These are controls described in OWASP AISVS Appendix C. Their practical value depends on configuring checks and approvals to cover the repository and the change.
Why can’t an AI review replace a human reviewer?
An AI review is supporting evidence, not accountable approval. OWASP AISVS AC.4.1 calls for review by a qualified human engineer distinct from the person who requested generation, and explicitly says the AI agent does not count as the human reviewer. A tool can surface possible defects; a human still needs to assess the change in its system context and take responsibility for the merge decision.
OpenAI’s 2025 account of its own code-verification system illustrates why interaction metrics should be read narrowly: 46% of comments on fully Codex-generated pull requests resulted in an author making a code change, compared with 53% of comments on human-generated pull requests in that reported deployment. Those figures describe author responses, not the correctness of comments, defect reduction, or a guarantee that a clean review is safe. OpenAI also warns against relying on a clean review as proof of safety. OpenAI’s account of code verification at scale explains its system and the limits of its evaluation.
How can repository instructions make reviews more useful?
Put review expectations where agents and human contributors can find them. GitHub documents repository-wide and path-specific review instructions, which teams can use to explain coding standards, architecture context, testing expectations, and areas that need closer scrutiny. For example, instructions for a deployment-manifest path can call out required security checks without imposing the same checklist on unrelated changes.
Instructions provide context; they do not prove that a change followed it. Reviewers should still compare the diff and validation evidence with the relevant repository expectations. See GitHub’s documentation on using Copilot code review for the product’s documented review and instruction options.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What changes when a team uses an AI review tool?
Tool behavior is product-specific, so confirm how reviews are requested and repeated in the configuration your team actually uses. For example, GitHub documents manually requested Copilot code review and configurable automatic reviews; a review is not necessarily repeated after every new push unless the relevant setting is enabled. Its documentation also describes review-effort options and approval controls. Those are GitHub product details, not guarantees about AI reviewers generally.
GitHub announced on June 9, 2026, that security validation for third-party coding agents was generally available. The announcement describes CodeQL analysis, dependency checks against the GitHub Advisory Database, and secret scanning, with an agent attempting to resolve issues found before finalizing a pull request. GitHub says the validations are on by default and follow repository Copilot settings. Treat this as a description of that GitHub feature as of the announcement date, not a substitute for confirming the checks on your repository or a general property of coding agents. Read GitHub’s announcement on third-party coding-agent security validation.
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What traceability is useful for agent-written code?
For workflows that need an audit trail, OWASP AISVS AC.5 recommends stable identifiers linking the prompt and response to the commit, build, and deployment, along with tamper-evident records for explainability reports, AI events, and citations. These controls can help teams investigate how a change moved through development. They are guidance for traceability, not a universal legal requirement for every pull request.
NIST SP 800-218A, published July 26, 2024, augments the Secure Software Development Framework with practices for generative AI and dual-use foundation models. It is useful background for integrating AI considerations into secure software development, but its stated scope is model development throughout the software development life cycle, not a prescribed pull-request template. See the NIST publication.
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