Handle an AI coding agent’s output as a proposed change, not as an authority. Before you run or merge anything, confirm the repository and branch, restate the intended behavior, inspect every changed line in context, check comments and conflicts, run the project’s relevant tests, and verify the final diff. Use sandboxing, worktrees, approvals, and network restrictions to limit what the agent can reach, but do not mistake isolation for proof that the code is correct.
The safest workflow separates review from application: first establish what changed and whether it solves the requested problem; only then give the change a path into your working tree, release branch, or production system.
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Start with the change’s identity and intended behavior
Open the repository or pull request that actually contains the agent’s work. A surprising number of review mistakes begin with the wrong checkout, stale branch, similarly named repository, or an uncommitted change left over from an earlier task. Record the repository name, remote, branch, commit or pull-request number, author, and current working-tree status before evaluating the code.
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Identity checklist
- Verify the remote URL and repository owner.
- Confirm the base and head branches, commit range, and whether the checkout contains uncommitted work.
- Match the title and description to the approved issue or task.
- Note required runtime, dependency, database, and deployment versions.
- Write down explicit non-goals so unrelated edits are easy to spot.
For a local checkout, commands such as git status --short, git remote -v, git branch --show-current, and git log --oneline --decorate -n 10 provide a quick identity check. Do not discard existing uncommitted work merely to make the agent’s patch easier to inspect; save it safely or use a separate worktree.
Read the complete diff, then follow behavior into nearby code
Review the patch directly rather than relying on an agent’s summary. Display the file list and line-level changes first, then open the surrounding functions, callers, configuration, migrations, tests, and documentation that determine how the changed code behaves. OpenAI’s pull-request guidance recommends inspecting relevant diff lines and checking generated findings against the actual source; its review instructions are available in Review pull requests with Codex.
Useful local commands include git diff --stat for scope, git diff --name-status for additions and deletions, and git diff <base>...<head> -- path/to/file for a focused comparison. For an uncommitted agent session, inspect both git diff and git diff --cached; an agent may have staged only part of its work.
Questions for every changed file
- Does this file belong to the requested behavior, or is it an unrelated cleanup?
- Does the implementation preserve public interfaces, data formats, permissions, and error handling?
- Are validation and authorization performed on every relevant path, including retries, background jobs, and administrative endpoints?
- Could a changed default, feature flag, migration, or configuration value affect existing installations?
- Do new dependencies, generated files, scripts, or lockfile changes have a clear reason?
- Are tests changed because behavior changed, or merely adjusted to accept a regression?
Trace important values across boundaries. A parameter validated in a controller may be transformed by a service, serialized by an API layer, and consumed by a worker. Review the entire path rather than assuming the first validation point is sufficient. For security-sensitive code, inspect authentication, authorization, secret handling, logging, file paths, command construction, and deserialization explicitly.
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Look for omissions as carefully as edits
AI agents often produce plausible implementation files while missing a caller, migration, test fixture, documentation update, rollback path, or platform-specific branch. Search for the changed symbol and configuration key across the repository. Compare the new behavior with neighboring implementations and established error conventions. An unchanged file can be the source of a compatibility break when a new assumption is introduced.
Use review comments and AI findings as hypotheses
Read existing human comments, automated checks, requested changes, and unresolved merge conflicts before deciding whether the patch is ready. Separate factual defects from style preferences and from questions that still need an answer. Resolve every conflict deliberately; accepting a conflict-resolution button without reading both sides can silently remove a fix or retain obsolete code.
Automated review is an aid, not an oracle. OpenAI’s guidance states: “Review generated findings against the relevant code before relying on them.” For each finding, reproduce the cited condition in the source, check whether the reported input or execution path is reachable, and determine whether the suggested fix preserves the requested behavior. A warning may be a false positive, while an unreported defect may still be present.
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- Copy the finding’s file, line, and claimed impact into your review notes.
- Open the current source at that location and inspect callers and data flow.
- Construct a minimal input or test that would trigger the alleged issue.
- Classify the result as confirmed, disproved, or uncertain.
- For a confirmed issue, write a regression test or a precise remediation request before applying the patch.
Do not let a high number of automated comments substitute for coverage. Ten comments about formatting do not outweigh one untested privilege boundary.
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Check tests, CI, and failure evidence in context
Inspect the project’s test results, continuous-integration checks, lint and type checks, build artifacts, and deployment previews. Run the narrowest relevant tests first to obtain fast feedback, then the broader suite required by the project. Use the same runtime, operating-system assumptions, feature flags, and database version that the change will encounter.
Interpret a passing check correctly
- A passing unit test is evidence only for the paths and inputs it covers.
- A green build does not prove that authorization, data migration, performance, or rollback behavior is correct.
- A skipped, flaky, quarantined, or cancelled check is not equivalent to a pass.
- Tests that were deleted, weakened, or reconfigured in the patch need separate scrutiny.
- When a test fails, preserve the failure output and identify whether the defect is in code, test assumptions, fixtures, environment, or an unrelated baseline problem.
Test the failure cases implied by the request, not just the happy path: malformed input, empty results, duplicate requests, timeouts, partial writes, permission denial, retries, concurrent updates, and service unavailability. For a schema or migration change, test both a fresh installation and an upgrade from a representative previous version. For a user-visible change, verify accessibility, localization, browser or client compatibility, and telemetry where those are part of the project’s acceptance criteria.
OpenAI’s pull-request review documentation recommends checking test results and reviewing and testing generated code; GitHub gives similar cautions in its documentation for Copilot Agents. Treat the vendor’s wording as process guidance, not as a guarantee about your particular repository.
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Choose an execution boundary before letting the agent act again
Limit the agent’s filesystem, command, credential, and network reach to what the task requires. Microsoft’s Visual Studio Code security guidance describes workspace-limited access, optional separate Git worktrees, session-level permissions, approval controls, and a diff-review flow in Secure AI-assisted development in Visual Studio Code. OpenAI describes sandbox boundaries, approval rules, and activity logs as complementary controls in Running Codex safely at OpenAI.
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Controls to set deliberately
- Workspace: expose only the repository and temporary directories needed for the task.
- Commands: require approval for destructive operations, package installation, database writes, deployment commands, and scripts that execute downloaded content.
- Network: block by default when possible; allow only required package registries, services, or documentation endpoints.
- Credentials: use short-lived, least-privilege credentials and keep production secrets outside the agent’s environment.
- Isolation: use a disposable branch, container, or separate worktree when the agent may make broad edits.
- Logging: retain prompts, commands, approvals, tool output, and resulting commits when the project’s policy permits it.
Read the active permission mode instead of assuming every tool will ask before acting. VS Code documents configurable terminal approval and auto-approval, including modes that can approve tools automatically. GitHub likewise notes that Copilot CLI prompts vary with the active permission mode. The setting in one product does not establish the behavior of another.
Isolation is a damage limiter, not a correctness test
A sandbox or worktree can restrict where an agent writes, but it cannot prove that the resulting code is correct. The VS Code documentation describes sandboxing as an additional layer rather than a standalone security boundary or replacement for endpoint security. Injected credentials, permitted paths, network access, and bypass modes can weaken isolation. Keep ordinary endpoint protection, repository access controls, secret rotation, and human review in place.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare local sessions, isolated worktrees, and cloud tasks
Choose the setup based on the change’s risk and the evidence you need to review. Compare filesystem scope, command and network permissions, separation from the active checkout, visibility of diffs and logs, and how the result returns to source control.
| Setup | Filesystem and checkout scope | Permissions and visibility | Best use | Specific cautions |
|---|---|---|---|---|
| Local agent session | Usually the current workspace unless you configure a narrower directory or container. | Immediate access to local tools; approval and auto-approval behavior depends on the product and settings. Review the live diff and command log. | Small, well-understood edits where you need rapid iteration. | Uncommitted work, credentials, and unrelated files may be exposed; confirm the active permission mode. |
| Separate Git worktree | Changes are isolated from the active checkout while sharing repository history. | Diffs can be reviewed independently before cherry-picking or merging. | Features, refactors, and experiments that should not disturb the branch you are using. | It limits checkout interference but does not by itself restrict network access, credentials, or dangerous commands. |
| Cloud task workspace | A new task receives an isolated workspace rather than your current local files. | Review the returned changes, logs, and test results before bringing work into source control. | Long-running or parallel tasks where reproducible isolation is valuable. | Important work must be committed or otherwise transferred; each new task has a separate workspace. |
OpenAI describes isolated task workspaces and the need to review changes and test results in Using Codex Cloud. The isolation model, available tools, and retention rules can change, so verify the current documentation and the settings shown by the environment you are using.
Apply only a reviewed patch
Once the implementation, evidence, and permissions satisfy the task’s acceptance criteria, apply the smallest reviewed unit. Keep unrelated formatting or cleanup out of the same commit. If the agent produced several possible fixes, select one explicitly and discard the rest rather than merging a bundle of alternatives.
- Save or commit unrelated local work, or move the agent output to a separate worktree.
- Rebase or update the review branch according to the project’s normal policy, then re-check the diff because conflict resolution can change behavior.
- Run the required tests and record the exact commit, environment, and result.
- Inspect the final diff after formatting, code generation, dependency installation, or migration steps.
- Review file permissions, generated artifacts, lockfiles, and deployment configuration for unexpected changes.
- Commit with a message that identifies the approved behavior and links to the task or pull request.
- Submit or merge only through the project’s normal review and branch-protection rules.
For a cloud result, do not assume that a successful task is already part of your repository. Fetch or apply the reviewed commit through the project’s normal source-control process. For a local result, do not run deployment or production-data commands merely because the agent suggested them; obtain the approvals required for those operations.
Repository instructions deserve review too
Repository-specific instructions can make automated feedback more useful by documenting coding conventions, test commands, security requirements, and paths that need special handling. GitHub documents repository-wide .github/copilot-instructions.md, root AGENTS.md, and path-specific instruction files in Using GitHub Copilot code review.
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Instructions are part of the execution context, not unquestionable policy. During pull-request review, GitHub says Copilot reads instructions and skills from the head branch. Therefore inspect changes to instruction files just as you inspect source changes: an attacker or careless contributor could alter the rules that shape automated feedback. Confirm that an instruction does not authorize secret access, weaken tests, suppress security findings, or direct the agent to ignore project controls.
Common failure modes and recovery actions
| Symptom | Likely cause | Safe response |
|---|---|---|
| The diff contains unrelated files or mass formatting. | The agent started from a dirty tree, followed a broad instruction, or ran a formatter across the repository. | Stop, preserve the original state, split the intended edits from noise, and regenerate the patch in a clean branch or worktree. |
| Tests pass locally but fail in CI. | Different runtime, operating system, dependency lock, service, feature flag, or test data. | Compare environments and reproduce the CI command; do not weaken the test until the environmental difference is understood. |
| An automated finding cites a vulnerability that cannot be reproduced. | False positive, stale line reference, or an unreachable path. | Trace the current data flow, document the counterexample, and retain the finding only if the relevant risk remains. |
| A migration works on a fresh database but not an upgrade. | Existing data, ordering, permissions, or rollback assumptions were not tested. | Test a copy of a representative previous schema, verify backups and rollback, and require an explicit migration plan. |
| The agent requests a secret, broad network access, or a destructive command. | The task scope is too broad or the workflow is attempting to bypass a control. | Deny the request, narrow permissions, use a temporary least-privilege credential if genuinely necessary, and have a human perform irreversible operations. |
| A cloud task completed but its changes cannot be found locally. | The task used an isolated workspace and did not transfer or commit its result. | Open the task’s returned diff and logs, then export the reviewed commit or patch through source control. |
A final gate before merge or release
Use this checklist immediately before accepting the change:
- The repository, base branch, head branch, task owner, and intended behavior are confirmed.
- Every changed file has a purpose tied to the request; unrelated edits are removed.
- Relevant callers, configuration, migrations, dependencies, and documentation were inspected.
- Human comments, automated findings, and merge conflicts are resolved or explicitly tracked.
- Each important AI finding was verified against the current source.
- Required unit, integration, end-to-end, lint, type, build, and migration checks ran in an appropriate environment.
- Failure paths, authorization, secrets, retries, concurrency, and rollback behavior were considered where relevant.
- The agent’s workspace, commands, network, credentials, and approval mode matched the risk.
- The final post-test diff contains no generated or permission changes that were not reviewed.
- The reviewed commit is recorded and enters source control through normal branch protection and human approval.
Following this gate does not make AI-generated code automatically safe. It makes the decision auditable: you know what was requested, what changed, what evidence supports it, what the agent could access, and why the remaining risk is acceptable.
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