How to Review AI-Generated Code Before It Merges

A benchmark can check whether code produces the expected output. A maintainer asks more questions. Did the patch stay inside scope? Do the tests cover the failure that matters? Does the design fit the repository? Can another engineer understand the change next month?

Cognition’s FrontierCode benchmark focuses on mergeability, including scope, tests, design, and whether maintainers would approve the pull request. That standard resembles the cost teams face after an agent declares success.

Define the requested behavior, allowed files, constraints, test expectations, and excluded cleanup. An agent that refactors nearby code may improve one metric and create a review burden nobody requested.

Require the agent to report changed files and explain any scope expansion. If new work becomes necessary, pause and ask rather than hiding it inside the patch.

An agent can write tests that confirm its own misunderstanding. Start with user flows, production incidents, and invariants the business cannot violate. Ask whether the test would fail before the fix and whether it covers the consequence behind the bug.

Use another tool to generate additional cases if helpful, then let an engineer decide which failures matter. Delegation gets cheaper faster than verification. The testing plan belongs in the task from the beginning.

Several agents working at once create a dispatch problem. The operator needs to see task state, branch or worktree, changed files, blockers, test results, and pending review. A chat list does not provide enough operational control.

Products such as Devin Desktop and Arena Agent Mode point toward command-center interfaces where people assign work and inspect execution. The interface should surface uncertainty and stalled runs instead of rewarding output volume.

Check naming, abstractions, error handling, dependencies, documentation, and alignment with local patterns. A patch that introduces a second architecture for a small feature carries a future cost even if the current test passes.

Ask the agent to explain the tradeoff in plain language. Reviewers should distrust elaborate justification for a change that the codebase does not need.

Track review cycles, rejected patches, escaped defects, revert rate, and maintainer time. Lines generated and tasks completed reward activity. Merge rate alone can also mislead if reviewers lower standards to keep the queue moving.

Compare agent-assisted work with similar human work over enough tasks to see a pattern. The goal is dependable engineering leverage with the same or better ownership quality.