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Treat every AI-generated patch as a proposed change, not an approved change. Before you use it, confirm the repository and branch, restate the intended behavior, inspect the complete diff in context, verify comments and automated findings against the source, examine tests and CI results, and check the agent’s permissions and isolation. Only then should you commit, merge, deploy, or otherwise rely on the code.
This workflow applies whether an agent edited your local checkout, a separate worktree, or an isolated cloud workspace. The controls reduce risk, but they do not prove that the implementation is correct or secure.
1. Confirm exactly what the agent was asked to change
Start with identity and intent, not with the agent’s summary. Verify the repository, project, branch, pull request, author and task that produced the changes. A correct-looking patch in the wrong checkout can be just as damaging as a defective patch.
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- Open the repository and branch you actually intend to review.
- Read the original issue, task description or pull-request description.
- Write down the requested behavior, affected users, compatibility requirements and any explicit non-goals.
- Check the base revision and the set of files the agent was supposed to touch.
- Confirm whether the task was allowed to modify configuration, dependencies, generated files, migrations, tests or documentation.
OpenAI’s pull-request review guidance puts this identification step before examining implementation details; its sequence is documented in Review pull requests with Codex. If the request is ambiguous, resolve the ambiguity before approving code. Do not infer a new requirement from what the agent happened to implement.
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2. Inspect the complete diff, then follow important changes into context
Read the patch yourself. An agent-generated summary can omit a file, minimize a risky change or describe an intention that the code does not implement. OpenAI specifically advises inspecting changed files and relevant diff lines, and checking generated findings against the actual code.
- List the changed files. Look for unexpected additions, deletions, renames, generated artifacts, lock-file changes and edits outside the requested area.
- Read each changed hunk. Examine both the old and new code, not only the added lines.
- Open surrounding code. Follow callers, error paths, data validation, authorization checks, configuration loading and state transitions affected by the edit.
- Trace interfaces. Check that function signatures, API contracts, database schemas, serialization formats and feature flags still agree across the repository.
- Look for behavior outside the happy path. Consider empty input, malformed input, retries, timeouts, partial failure, concurrent requests, permission changes and rollback.
- Separate intended and incidental edits. Ask why every changed line exists. Remove unrelated formatting, refactoring or dependency updates unless the task explicitly requires them.
In a Git checkout, commands such as git status and git diff can help you establish the file set and inspect the patch, but use the review interface or version-control tools appropriate to your project. The important control is independent inspection of the source, not a particular command.
Questions to ask while reading
- Does the implementation actually produce the requested observable behavior?
- Does it preserve existing behavior for users and integrations that were not part of the request?
- Are new inputs validated at the trust boundary, and are errors handled without leaking secrets or sensitive data?
- Could a user, service or job reach a new operation without the intended authorization?
- Does the change introduce a dependency, permission, network call or data migration that the task never mentioned?
- Are tests asserting the behavior that matters, or only the easiest path through the new code?
3. Treat comments and AI findings as hypotheses
Review comments, automated scanners and AI-generated findings are useful leads, not verdicts. OpenAI Help Center guidance states: Review generated findings against the relevant code before relying on them.
Follow the cited path and reproduce the claimed behavior before accepting a finding or applying its suggested fix. The complete review sequence is described at OpenAI Help Center.
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A disciplined way to validate a finding
- Read the finding’s exact claim and file or line reference.
- Inspect the referenced code and its callers, inputs and state.
- Determine whether the alleged path is reachable under real permissions and configuration.
- Construct a focused test, reproduction or static check for the claimed failure.
- Decide whether the result requires a code change, a test, a documentation change or no action.
- Record the reasoning so a later reviewer can distinguish a verified defect from a rejected hypothesis.
4. Check tests, CI and failure cases in context
Review the project’s test results and continuous-integration checks, then run the appropriate tests when you can. A green check is evidence only for the cases and environment it covers. It does not replace source inspection, and it does not establish that new or changed tests match the requested behavior.
- Identify which checks ran, on which revision and with which environment or configuration.
- Read failures, warnings, skipped tests and flaky-test annotations instead of treating a green badge as the whole result.
- Compare test changes with the implementation. A test that was weakened, deleted or over-mocked may hide a regression.
- Add or run focused tests for boundary conditions, authorization, malformed data, retries and rollback where those cases matter.
- Check integration, migration, build and packaging signals in addition to unit tests when the patch crosses those boundaries.
- Review logs for unexpected network access, file writes, generated credentials or other side effects during testing.
OpenAI’s cloud-task documentation emphasizes reviewing both changes and test results before bringing work back into source control. See Using Codex Cloud for that workflow.
Distinguish evidence from confidence
Record what each check actually establishes. A unit test can demonstrate one input and expected output; it cannot by itself prove authorization, deployment configuration or behavior under concurrency. A build can show that the code compiles, while saying nothing about a migration’s safety. A static finding can identify a suspicious path, but only source inspection and a relevant reproduction establish whether that path matters. This distinction prevents a long list of green checks, or an agent’s confidence statement, from substituting for an engineering judgment.
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When a check is unavailable, document the gap rather than silently treating it as a pass. Note the untested environment, skipped integration step, unresolved flaky test or missing production-like configuration. That record tells the next reviewer exactly what remains uncertain.
5. Deliberately limit the agent’s operating scope
Permissions and isolation determine how much damage an agent could do while producing a patch. Grant only the file, command, tool and network access required for the task. Before accepting a change, inspect the active permission mode rather than assuming every action required approval.
OpenAI describes sandbox boundaries, approval rules and activity logs as complementary controls in Running Codex safely at OpenAI. Microsoft’s Visual Studio Code documentation similarly describes workspace limits, optional separate Git worktrees, session-level permissions and diff review in Secure AI-assisted development in Visual Studio Code.
What isolation can and cannot do
- A workspace or worktree can restrict where an agent edits files, making unrelated changes easier to detect.
- Command, tool and network permissions can reduce access to production systems, credentials or external services.
- Approval prompts can give you a chance to stop a risky operation, but configurable auto-approval or permissive modes can remove that pause.
- Logs help reconstruct what happened and which tools were used.
- None of these controls proves that the resulting code is correct, safe or free of malicious behavior.
- Injected credentials, allowed-path mistakes, network access and bypass modes can weaken isolation. Microsoft explicitly describes sandboxing as an additional layer, not a standalone security boundary or replacement for endpoint security.
Comparing common review setups
| Setup | Filesystem scope | Command, tool and network controls | Change isolation | How you review the result | Return to source control |
|---|---|---|---|---|---|
| Local agent session | The active workspace and any paths allowed by its configuration | Determined by the product’s current permission and approval mode | Changes may be mixed with your active checkout unless you create a separate workspace | Inspect the local diff, logs, approvals and test output | Review, then commit or discard in the checkout |
| Worktree-isolated session | A separate Git worktree or workspace selected for the task | Still governed by the agent’s configured command, tool and network permissions | Edits are separated from the active checkout until you deliberately transfer or merge them | Compare the worktree diff, logs, approvals and tests with the intended base | Merge, copy or commit only after review |
| Cloud task | An isolated task workspace defined by the service | Controlled by the cloud product’s environment and permission model | Each new task has a separate workspace; it does not automatically alter your local checkout | Review returned changes and test results before accepting them | Bring approved work into source control and commit important results |
The products in this table expose different environment and permission models. Do not assume a control described for one product exists, or has the same strength, in another. Verify the current documentation and settings for the workspace you are using.
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For a small, low-impact edit, a local session may be adequate if its permissions and diff are visible. A separate worktree makes it easier to keep an active checkout clean and compare the proposed base and head revisions. A cloud task can keep work away from the local checkout, but the returned patch still needs the same source review, test review and source-control commit. In every case, compare filesystem scope, command and network permissions, visibility of logs and approvals, and how the result returns to source control.
Do not treat a new cloud task as a continuation of an old one. OpenAI’s cloud guidance describes separate workspaces for new tasks, so preserve important work by committing it after approval. An isolated workspace is a place to inspect a change, not a substitute for durable project history.
6. Review repository instructions before relying on automated review
Repository-specific instructions can make automated feedback more relevant to local conventions, security rules and testing requirements. 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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Those files are part of the review surface. During pull-request review, GitHub says the reviewer reads instructions and skills from the head branch. Therefore, inspect instruction changes in the proposed branch just as you inspect source changes. An instruction that disables a check, redirects a tool or changes the definition of “done” can materially alter automated feedback.
Make instruction changes auditable
- Include instruction-file edits in the changed-file inventory.
- Compare the proposed instructions with the repository’s established conventions.
- Check whether they change required tests, security checks, file scope or tool permissions.
- Ask whether a path-specific rule conflicts with a root rule or silently exempts sensitive code.
- Re-run or re-read automated findings after understanding which instructions were active.
Instructions improve relevance; they do not make generated findings authoritative. The proposed branch can influence the reviewer’s context, which is why the instructions themselves require review.
7. Apply the change only after the final review
When implementation, evidence and permissions are understood, perform one last pass over the exact revision you will use. Confirm that the final diff is the one you reviewed, that required checks correspond to that revision and that unresolved comments or merge conflicts are handled.
- Refresh the branch or workspace state and verify the base and head revisions.
- Re-read the final diff, including generated files and dependency or lock-file changes.
- Confirm that tests and CI results belong to this final revision.
- Resolve or explicitly document every blocking comment, finding and conflict.
- Check deployment, migration and rollback implications before committing or merging.
- Commit approved work to source control. For cloud tasks, do not treat an isolated workspace as durable history until the result is committed.
- After integration, monitor the relevant service, logs and error signals and be prepared to revert if production behavior differs from the reviewed expectation.
The final approval is a human decision about a verified change. It is not an endorsement of the agent’s summary, confidence score or number of passing checks.
8. Common failure modes and the safer response
The patch is larger than the request
Stop and classify every extra edit. Unrelated refactoring and formatting increase review surface and can conceal defects. Ask the agent to revert unrelated work or split it into a separate, independently reviewable change.
The summary says a file was untouched, but the diff shows it changed
Trust the diff and investigate the discrepancy. Re-run the file listing and inspect the complete patch. Do not approve until you understand why the summary and source disagree.
Tests pass, but the behavior is still suspicious
Identify what the tests do not cover: permissions, malformed input, concurrency, external failures, migrations or production configuration. Add a focused reproduction or inspect the relevant path manually.
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An AI finding recommends a broad rewrite
Validate the smallest concrete claim first. If the cited defect is real, prefer a narrowly scoped fix and a regression test over an unrelated redesign unless the task explicitly calls for one.
A sandbox makes the task feel safe
Check actual allowed paths, credentials, network access, approval mode and logs. Isolation limits reach; it does not establish correctness or replace endpoint and operational security.
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A pull request contains new agent instructions
Review those instructions as proposed code. Determine whether they alter tools, permissions, checks or review criteria, and verify that they do not suppress evidence a reviewer needs.
A cloud task produced useful work but no commit
Preserve the approved result in source control before ending the task or starting another one. Review the returned patch and test results first; then commit the exact revision you accepted. Do not rely on the workspace alone as the project’s history.
A permission prompt never appeared
Do not assume that means the action was harmless. Inspect the active mode and its auto-approval settings. Product configurations differ, and some modes can approve tools without an interactive pause.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. A compact approval checklist
- Repository, branch, base revision and author are correct.
- The requested behavior and non-goals are written down.
- Every changed file and important diff hunk has been read in context.
- Unrelated edits, generated files, dependencies and migrations are explained.
- Comments and AI findings were verified against the actual source.
- Tests, CI checks and failure cases were reviewed for the final revision.
- Permission mode, allowed paths, network access, approvals and logs were checked.
- Repository instructions, including files added or changed by the proposed branch, were reviewed.
- Conflicts, blocking comments and rollback or deployment implications are resolved.
- The final result is committed or merged only after this review.
Following this sequence keeps the agent in its proper role: a fast implementation assistant whose output still requires deliberate engineering review.
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Frequently Asked Questions
Can I approve an AI-generated patch because all tests pass?
No. Passing checks provide evidence only for the cases and environment they cover. Read the complete diff, verify the requested behavior, inspect authorization and failure paths, and confirm that the tests themselves were not weakened or made incomplete.
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Should I trust an AI code-review finding?
Treat it as a hypothesis. OpenAI Help Center guidance says to review generated findings against the relevant code before relying on them. Follow the cited path, inspect callers and configuration, and reproduce the claimed behavior before accepting a fix.
Is a sandbox enough to make agent changes safe?
No. Sandboxing and worktrees limit reach, but they do not prove correctness or replace endpoint security. Check allowed paths, credentials, network access, approval or auto-approval mode, and activity logs.
What is the safest place to review an agent’s patch?
There is no universal winner. Compare local, worktree-isolated and cloud setups by filesystem scope, command and network permissions, visibility of diffs and logs, isolation from your active checkout, and the process for returning approved work to source control.
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Review its returned changes and test results, then commit important approved work to source control. A new cloud task has a separate workspace and should not be treated as durable project history.
Do repository instruction files need review too?
Yes. Files such as .github/copilot-instructions.md, root AGENTS.md and path-specific instructions influence automated review. GitHub notes that pull-request review uses instructions from the head branch, so proposed instruction changes belong in the diff review.
What if the agent changed files outside the request?
Stop and classify each extra edit. Revert or split unrelated formatting, refactoring, dependency, configuration or generated-file changes unless the task explicitly requires them. A smaller patch is easier to verify.
When is it safe to merge or deploy?
Only after the exact final revision has been inspected, relevant findings and comments have been resolved or documented, tests and CI results have been checked, permissions and isolation understood, and deployment, migration and rollback implications considered.
The Bottom Line
AI coding agents accelerate implementation; they do not eliminate review. Confirm the repository and goal, inspect every meaningful diff in context, validate findings, interpret tests honestly, constrain permissions, review repository instructions and preserve approved cloud work in source control. Apply the change only when you understand what it does, what evidence supports it and what risks remain.
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