Review an AI pull request: a step-by-step guide

To review an AI pull request, read the agent's plan first, scan the diff for correctness and scope, confirm the tests actually exercise the change, run it locally if it touches anything risky, and merge only when it meets the same bar you hold for a human contributor. The review is the safeguard that makes autonomous agents safe to use, so it is worth doing deliberately rather than rubber-stamping.

The good news is that reviewing an agent's PR is mostly the review skill you already have, applied with a couple of extra checks. Here is the procedure step by step.

A step-by-step checklist to review an AI pull request

1. Read the plan. A well-built agent attaches the plan it followed. Read it before the diff. The plan tells you what the agent thought it was doing, which makes the diff far faster to evaluate and surfaces wrong assumptions early.
2. Scan the diff for correctness. Read the change the way you would read any PR. Does it do what the task asked? Are edge cases handled? Does it follow the conventions of the surrounding code? Treat unfamiliar patterns as questions, not as defects, but get them answered.
3. Check the tests. Confirm the PR includes or updates tests and that they actually assert the new behavior, not just that the code runs. A passing suite that does not exercise the change is a false signal. See automated testing for what good coverage looks like.
4. Look for scope creep. Agents sometimes change more than the task required. Flag edits outside the stated scope, unrelated formatting churn, or refactors you did not ask for. Smaller, focused PRs are easier to trust and easier to revert.
5. Run it locally. For anything touching data, security, migrations, or a critical path, check out the branch and run it. CI catches a lot, but local exploration catches the things tests were not written for.
6. Merge. When the change is correct, in scope, well tested, and behaves locally, merge it through your normal process with its required checks and approvals. Nothing about the agent changes the merge bar.

What to watch for specifically

A few patterns deserve extra attention in agent PRs:

• Confident-but-wrong changes that pass a weak test. This is why step three checks that tests assert behavior, not just execution.
• Silent assumptions about inputs or environment that look fine in the diff but break at the edges.
• Over-broad edits where a focused fix would do. Ask the agent to narrow the change rather than merging a sprawling one.
• Missing error handling on paths the happy-path tests do not cover.

None of these are unique to AI; they are the same things you watch for in any PR. The agent just produces more of them faster, which is exactly why the review step is non-negotiable.

Why the review step is the whole point

Agentcode is review-first by design: it never merges on its own. That means the review is not a formality you can skip when you are busy; it is the mechanism that keeps autonomy safe. The agent earns more trust the same way a human contributor does, by a track record of clean reviewed work, not by being granted the merge button up front.

The merge button stays with you. Everything else, the planning, editing, and testing, is what you delegate.

Making reviews faster over time

Two investments make agent reviews quick rather than burdensome. The first is a strong test suite, because good coverage turns most of step three into reading a green check. The second is clear task descriptions, because a precise task produces a focused PR that is fast to scan. Both compound: the better your tests and tasks, the lighter each review becomes.

For the bigger picture of how this fits the workflow, read the AI software development workflow and the pillar on what an AI coding agent is. To see the loop that produces these PRs, visit how it works, and to understand the review-first stance in depth, see AI code review. When you are ready to put a real task in front of the agent, pricing is on pricing.