How to review agent-generated PRs for test gaps

How do I review agent-generated pull requests so I catch missing tests and risky diffs

Review agent-generated pull requests in three passes: first, read the diff for scope and side effects; second, check whether the behavior change has tests that fail before the fix and pass after it; third, look for risky files and require a smaller PR if the change touches contracts, auth, payments, migrations, or deployment code. GitHub supports required reviews, required status checks, and code owner review for exactly this kind of gatekeeping.

The part people get wrong is reviewing the patch as if it were human-authored and coherent by default. Agent output often includes a correct core change plus extra edits that look harmless but widen the blast radius, such as formatting churn, helper refactors, or changes that quietly alter error handling. Treat those extras as separate work. If the PR mixes a feature with cleanup, ask for a split before you approve anything. GitHub’s own guidance on standardized pull requests and stackable AI-generated changes points toward smaller, focused diffs because focused diffs are easier to review and approve.

Start with the contract. Read the title, issue, or prompt, then check whether the diff really does only that job. If a request says “fix login timeout,” look for edits outside the auth path, especially unrelated database writes, UI copy, config files, or shared utilities. The risky pattern is an agent using a shared helper to make the immediate test pass while silently changing behavior for other callers. Shared files deserve extra scrutiny because they expand the impact of a small-looking diff.

Then inspect the tests before you inspect the code again. A good PR has at least one test that would have failed on the old code, not just a test that confirms the happy path after the fact. If the PR only adds snapshots, console output checks, or a generic “returns 200” assertion, ask what behavior changed and where that behavior is pinned down. The review standard is simple: the test should prove the bug or requirement, not merely echo the implementation. GitHub’s pull request review and dependency review docs both emphasize checks that block merge when the important validation fails.

A practical test review sequence works well. Run the new tests alone, then run the affected suite, then run the full relevant package or service tests if the change touches shared code. If the PR adds code but no new tests, ask whether existing tests already cover the changed path. If they do not, the PR is incomplete. If the agent claims the tests are unnecessary because the change is “small,” treat that as a warning sign, not an exception. Small diffs still break when they sit on a hot path or alter default behavior.

Use a risk checklist on every file. Files that change schemas, migrations, permissions, secrets handling, job queues, payment logic, auth flows, serialization, API responses, build scripts, or deployment manifests should be reviewed line by line. These are the places where one missed line causes production damage. GitHub recommends code owners for sensitive areas, and branch protection can require those reviews before merge. If your repo does not already enforce that, the human reviewer has to do the work manually every time.

Look for “risky diffs” by shape, not by size alone. A tiny change that flips a default, removes a retry, loosens validation, or changes error propagation can be more dangerous than a large refactor. The inconvenient part is that agents often produce code that is syntactically clean and behaviorally broad. Read for behavior changes, especially hidden ones: early returns, broader exception catches, changed null handling, new background jobs, and new calls to shared services. Those are the edits that escape casual review.

A good habit is to compare the PR against the tests it should force you to add. For example, if the diff changes input validation, there should be a test for invalid input, a test for the boundary case, and a test for the success path. If the diff changes a retry loop, there should be a test that fails once and then succeeds, plus a test that stops after the retry budget. If the diff touches a parser or serializer, there should be a regression test for the exact payload shape that previously broke. That style of review turns the agent’s patch into a checklist instead of a guess.

Watch for missing assertions around failure mode. Agent-generated tests often prove only that code works when everything is perfect. That is not enough for production code, because the bug usually appears in the failure path, the partial path, or the edge case. Ask what happens when the external API times out, the database returns empty data, the input is malformed, or the user has an older record shape. If the PR does not include those cases, it does not close the loop.

If the PR is large, review it in layers. First approve the shape of the change, then the implementation details, then the tests. GitHub’s stacked pull request guidance exists because layered review reduces cognitive load and keeps each diff focused. For agent-generated work, this is even more important, because one overbroad patch can hide a bug inside a correct-looking refactor. When a single PR mixes data model changes, UI work, and background processing, ask for separate branches or at least separate commits before you evaluate risk.

Set branch protection so your review rules do not depend on memory. Require status checks, require code owner review for owned files, and keep stale approvals from surviving new pushes on important branches. GitHub documents these controls in branch protection and rulesets, and they are the difference between a careful review process and a polite suggestion. If an agent updates a PR after review, the old approval should not silently carry over if the change touched the sensitive part of the code.

When you reject a PR, be specific. Say which behavior lacks coverage, which file is too risky to change in the same patch, and what test would convince you. For example: “Add a regression test for null user profile data, split the schema migration from the endpoint change, and move the retry change into its own PR.” That kind of feedback helps the agent or the human author produce the next patch in the right shape instead of guessing. GitHub’s review tools support line comments, file comments, and review requests for exactly this workflow.

If you want a simple rule, use this one: no merge until the diff is small enough to reason about, the tests prove the change instead of restating it, and every sensitive file has the right owner looking at it. That is the fastest way to catch missing tests and risky diffs before they reach production. For teams using DevConnect, the same rule applies when you trade review help with another builder, because the value is in the review quality, not the volume. You can keep the workflow free and simple at https://devconnectplatform.com, but the gate should stay strict.

What is the fastest way to spot a missing test

Look for a behavior change that is not named in a test. If the PR fixes a bug, ask which old input failed and where that input is now covered. A test that only checks the happy path is not enough.

What files deserve the most caution in an agent-generated PR

Schema migrations, auth, payment, secrets, deployment, queueing, and shared utility code deserve extra caution. Those files can change behavior far outside the visible diff, so require a narrower patch and a stronger explanation.

Should I approve if the code looks right but the tests are weak

No. Approve only after the tests prove the behavior change or a pre-existing test already covers the exact path. Weak tests let a clean-looking patch merge with the bug still in place.

How do I keep the review process from drifting over time

Make the rules structural, not informal. Use required checks, code owner review, and stale review dismissal on protected branches so the process still works when the team is busy or the PR was written by an agent.