AI Coding Agents: Four Checks for Reviewing Changes

Reviewing changes from an AI coding agent calls for four practical checks: confirm the change matches the request, inspect the diff for unintended edits, run relevant tests and checks, and examine behavior and security risks that automation may miss. These checks apply whether an agent proposes a small fix or a larger feature; they help reviewers verify the result rather than assume that plausible-looking code is correct.

Why agent changes need a deliberate review

AI coding agents can read files, edit code, run commands, and sometimes make several related changes in one task. Their output may look consistent while still misunderstanding a requirement, overlooking an unusual case, or changing a file that was not meant to be touched. The same review principles used for human-written code therefore remain important.

A passing test suite is useful evidence, but it is not proof that a change is correct. Tests only check the cases they cover, and an agent may not know which behavior matters most to the people using the software. A reviewer should connect the request, the actual diff, and the available verification instead of treating any one of them as sufficient.

Check 1: Compare the change with the request

Start by restating the task in observable terms. Identify what should change, what should remain unchanged, and any constraints such as supported inputs, compatibility, or an expected error response. Then compare the implementation with those points.

• Check completeness: Does the change address every part of the request, not just the easiest part?
• Check scope: Did the agent introduce extra features, cleanup, or behavior changes that were not requested?
• Check assumptions: Does the code rely on an unstated interpretation of an ambiguous requirement?

For example, if a request says that a form should reject an empty email address, verify that the relevant submission path rejects it. A new visual warning alone may not prevent an invalid request from being sent. Conversely, changing validation across every form may be an unjustified expansion of a task limited to one form.

When the request is ambiguous, do not silently approve the agent’s guess. Resolve the intended behavior with the task owner or document the assumption so it can be checked.

Check 2: Inspect the full diff and its scope

Read the diff, not just the agent’s summary. A summary describes what the agent believes it changed; the diff shows the actual edits. Review the files changed, the surrounding code, and any additions or deletions that seem unrelated.

• Look for accidental edits to configuration, generated files, dependency manifests, or documentation.
• Check that a replacement did not remove existing behavior or error handling.
• Notice broad formatting or renaming changes that make the substantive edit harder to inspect.
• Review dependencies and scripts for changes in what is installed or executed.

Use the repository’s normal diff tools and, where useful, compare the final code with nearby patterns. A one-line change can still alter a public interface or a critical condition; a large diff may be mostly generated output. Size alone does not determine risk, but unexpected scope is a reason to pause and ask why it exists.

Check 3: Verify tests and other evidence

Find out what verification was actually performed. Check whether relevant tests ran, whether they passed, and whether the agent reports any skipped checks. If the project has established commands for tests, formatting, type checking, or static analysis, use the appropriate ones or confirm their results in the development workflow.

Match the verification to the change. A bug fix should generally have a test that would have exposed the original problem, where the project’s testing approach makes that practical. A behavior change may need tests for both the expected case and important boundaries. For example, a parser change might be checked with valid input, empty input, and malformed input rather than only one ordinary example.

Do not accept a claim such as “all tests pass” without knowing which tests ran and against which code. A test may be unavailable, skipped, or unrelated. Equally, a failing test is not automatically proof that the new change is wrong: it could expose an existing failure or an environment issue. Establish the cause before treating the result as resolved.

Check 4: Examine behavior, edge cases, and security

Reason through how the changed code behaves in realistic conditions, especially where inputs, state, permissions, or failures are involved. Ask what happens at boundaries and when an operation cannot complete. Consider whether errors are handled clearly and whether existing behavior remains compatible.

Security review should be proportionate to the code. For changes that handle user input, authentication, file access, network requests, or secrets, look for unsafe assumptions: untrusted data used without appropriate validation, access checks applied in the wrong place, sensitive values exposed in logs, or new commands that execute with unexpected input. These are prompts for review, not an exhaustive security audit.

Also look for maintainability. Is the logic understandable to the next person? Does it follow established project conventions? Is a complicated workaround hiding a simpler requirement or creating a new failure mode? If you cannot explain why an important change is safe, ask for clarification or request a narrower revision.

Make the review efficient without making it superficial

Risk-based review helps prioritize attention. A wording change in documentation does not usually need the same scrutiny as a change to permissions or data handling. Still, every change benefits from checking its stated purpose and scope.

1. Read the task and identify its expected outcome.
2. Scan the changed-file list for unexpected scope.
3. Review the diff, focusing on logic and interfaces.
4. Check relevant tests and other verification evidence.
5. Consider edge cases, security implications, and unresolved assumptions.

Keep the agent’s explanation separate from your evidence. It can help locate files or describe intent, but it does not replace reading the code or checking results. If a change is too large to understand as one review, split it into smaller, focused changes where possible.

Common review mistakes

Several shortcuts can make a review look complete without establishing that the change is sound:

• Approving because tests pass: Tests can miss the requested behavior or important edge cases.
• Trusting the summary: Summaries can omit unintended edits or limitations.
• Reviewing only the main function: Callers, configuration, and related tests may also change.
• Confusing clean formatting with correctness: Readable code can still implement the wrong requirement.
• Ignoring skipped checks: A missing test run should be recorded as a limitation, not treated as a pass.

Another misconception is that an AI-generated change needs an entirely different standard of review. The source of the code does not change the core question: does the patch meet the requirement, behave as intended, and fit the project safely? What may change is the need to verify claims carefully because the agent’s confidence is not independent evidence.

Frequently Asked Questions

What are the four checks for reviewing AI coding agent changes?
Check the request and intended behavior, inspect the full diff and scope, verify tests and other evidence, and assess behavior, edge cases, and security risks.

Are passing tests enough to approve an AI-generated change?
No. Passing tests show that the tested cases passed; they do not establish that the request was interpreted correctly or that untested cases are safe.

Should reviewers read every line of an agent’s diff?
Review the complete diff, giving attention according to risk. Do not rely only on a summary or inspect only the most obvious file, since related changes may affect behavior elsewhere.

What should I do if the agent skipped tests?
Determine which checks were skipped and why. Run relevant project checks when practical, or make the missing verification an explicit limitation before deciding whether the change can be accepted.

How should I review a very large agent change?
Break it into focused parts or ask for a smaller patch. Smaller changes are easier to connect to requirements, inspect, and verify than a broad bundle of unrelated edits.

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