The Tool Desk
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A coding agent should work inside clear technical and permission boundaries, and its changes should be reviewed before they are kept or merged. A dependable loop is: describe the task, govern what the agent may do, inspect its changes, validate behavior, then decide whether to revise, integrate, or discard the work. Approval to perform an action and approval of the resulting code are separate decisions.
1. Describe the task so the result can be checked
Give the agent the intended behavior, relevant context, constraints, and a definition of done. The clearer the target, the easier it is to compare the request with the actual diff.
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- For a feature, state the expected behavior and any compatibility or scope limits.
- For a bug investigation, identify the behavior or review criterion and ask for the code that supports a finding.
- For a fix, bound the requested change instead of inviting unrelated cleanup.
OpenAI’s Codex pull-request review guide gives examples such as asking how a change affects sign-in, whether an error path releases a database connection, and which code supports a finding. These prompts make the agent’s response easier to verify against the relevant implementation.
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Set both a technical boundary and an approval policy appropriate to the task. A sandbox determines where an agent can write and whether it can access the network; approval policy determines when it must ask before taking an action. OpenAI describes these as complementary controls in its Codex safety guidance.
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Approval is most useful before a consequential action occurs. Reviewing a diff afterward cannot undo an external action that has already happened. For custom agent implementations, the OpenAI Agents SDK guidance notes that agent-level guardrails do not run around every custom tool call; checks for side effects belong next to the tool that performs them. See Guardrails and human review.
3. Inspect what changed, not just the agent’s summary
Review the artifact and its evidence. For a pull request, confirm the repository, branch, purpose, and author. Then examine changed files and relevant diff lines, read findings and comments, and check test results, other checks, and unresolved merge conflicts.
- Compare the actual changes with the task, constraints, and project conventions.
- Check whether the diff includes unrelated files or unexpected scope.
- When an automated finding is unclear, ask for the supporting code and inspect it yourself.
Codex’s guide cautions: “Review generated findings against the relevant code before relying on them.” Automated findings are prompts for examination, not proof that a defect exists or that the rest of the change is correct.
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4. Validate behavior alongside the diff
Run the relevant tests and use debugging or other editor tools when needed to check the behavior the task requires. Test results inform the review; they do not replace reading the diff or understanding what the code is meant to do.
Microsoft’s VS Code overview of building with AI describes an agent reading related code, editing, running tests, inspecting results, revising, and handing the diff and validation results to the user. That workflow makes the evidence available for a human decision, but it does not guarantee correctness. VS Code states: “AI can produce incorrect code or misunderstand your intent.”
5. Decide what happens to the work
After inspection and validation, choose whether to request a targeted revision, keep or apply the changes, commit or merge them, or discard them. The exact controls depend on the tool and whether the work is in a local workspace, an editor session, a worktree, or a hosted pull request. VS Code’s agent changes guidance covers reviewing diffs, testing, giving feedback, integrating changes, and discarding them. As it puts it, “You remain responsible for deciding which changes reach your codebase.”
Keep three meanings of approval distinct:
- Action approval: permission for the agent to perform a particular action.
- Code review: a human’s assessment of whether the proposed change is acceptable.
- Merge approval: a repository’s configured requirement for merging a pull request.
For example, GitHub documents that Copilot’s review assessment alone does not count toward merge requirements. Configured Copilot approvals are a separate feature, described as public preview in the documentation accessed for this article; new commits after an approval can dismiss it. Check GitHub’s current Copilot code-review documentation before relying on a particular setting, since product controls and availability can change.
Give sensitive workflow changes an explicit checkpoint
Changes to CI or automation deserve particular scrutiny before their workflows are allowed to run. GitHub warns that “GitHub Actions workflows can be privileged and have access to sensitive secrets,” and advises inspecting proposed workflow changes before allowing workflows to run on a Copilot branch. See GitHub’s guidance on reviewing Copilot output. Treat permission to run a workflow as a separate decision from whether its code diff looks acceptable.
Choose a review surface that exposes the evidence you need
Codex pull-request review, VS Code agent-edit review, and GitHub Copilot review represent different workflow surfaces, not a tested ranking. Choose based on where you need to inspect changes and validation, how permissions are managed, and how the repository handles integration.
| Review surface | Useful considerations |
|---|---|
| Local workspace or pull request with Codex | Codex’s guide covers findings, comments, asking for explanations, and requesting bounded fixes. Availability may vary because the page noted gradual rollout when accessed. |
| VS Code agent session | Documentation describes reviewing diffs and validation results, giving feedback, integrating changes, or discarding them. |
| GitHub Copilot code review | Documentation covers review effort, repository instructions, and configured approval behavior; Copilot approvals were labeled public preview in the documentation accessed for this article. |
Across these surfaces, check whether you can see tests and checks alongside the change, whether project instructions capture conventions and high-risk paths, and whether sensitive workflow files require an explicit human decision. No one interface guarantees safe or correct outcomes.
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