# AI Code Review Agent An AI code review agent should do more than leave comments. Replicas helps teams turn PR reviews, CI failures, and automated checks into reviewable follow-up work. - Canonical: https://tryreplicas.com/use-cases/ai-code-review-agent - Get started: https://tryreplicas.com/auth?mode=signup - GitHub docs: https://docs.tryreplicas.com/features/github ## What is an AI code review agent? An AI code review agent is an AI system that reviews pull requests, checks changes against engineering expectations, and helps teams follow up on feedback. For Replicas, the important part is the follow-up loop. A review finding, failed check, or PR comment becomes a software engineering task the agent can inspect, attempt, and return as a visible result. ## How Replicas handles code review follow-up - **Review signal arrives:** A CI failure, human PR review, review-bot comment, or automation finding creates a concrete follow-up task. - **Replicas inspects the context:** The agent reads the diff, logs, comments, repository rules, and surrounding code inside a cloud workspace. - **The loop closes:** Replicas can push a fix, reply with findings, update a tracker comment, or leave the work ready for human review. ## Code review is more valuable when the loop closes Code review tools are useful when they find issues. They become more useful when teams can turn those findings into fixes, test runs, and tracked handoffs without creating a separate manual task. - **Review findings become work:** A useful review does not stop at a comment. Replicas can investigate, edit code, run checks, and report back. - **CI failures get a first response:** When checks fail, Replicas can read the failure, inspect the code path, and attempt a focused fix. - **Repository rules stay enforceable:** Teams can encode common review expectations such as DRY code, unused code, framework rules, or project-specific constraints. - **Humans keep control:** Replicas keeps the session, commands, files, comments, and final output visible so reviewers decide what ships. ## What can trigger an AI code review follow-up? Replicas works best when feedback is specific enough for an agent to inspect and act on. That can come from humans, CI systems, review bots, or repo-specific automation. - Human PR review comments - Failed CI checks - Review-bot findings - Repository-specific automation checks - Framework rules such as avoiding unnecessary effects - Style, duplication, and unused-code feedback - Follow-up comments on an active PR - Repeated failures that need one combined investigation ## The output is not just another review comment A review comment can identify a problem. A coding agent can also investigate the problem, change files, run checks, and return a concrete handoff. - **Follow-up commits:** Small, targeted fixes pushed back to the active branch for reviewer inspection. - **Tracker comments:** A single PR comment that records the failure, status, and final note instead of scattering updates. - **Investigation summaries:** A concise explanation of what failed, what was inspected, and what should happen next. - **Human review handoff:** A reviewable diff, session transcript, and test output that a teammate can accept, reject, or steer. ## How to evaluate an AI code review agent If a tool only writes comments, evaluate it like a reviewer. If it can act on feedback, evaluate whether the work remains inspectable, reviewable, and under team control. - Can it react to CI failures and PR review comments? - Can it inspect logs, files, diffs, and repository context before replying? - Can it push a follow-up fix when the feedback is actionable? - Can it keep one visible tracker for repeated CI or review failures? - Can teams configure repo-specific review rules and automation checks? - Can humans inspect and approve the final output before merging? ## FAQ ### Is Replicas an AI code review tool? Replicas is not only a review surface. It is a cloud coding agent platform that can respond to review signals, inspect code, run commands, make changes, and report back with reviewable work. ### Can Replicas respond to failed CI checks? Yes. Replicas can receive CI failure events, track them in one PR comment, inspect the logs and code, attempt a fix, push changes, and update the row with the result. ### Can Replicas follow up on human code reviews? Yes. Teams can configure Replicas to react to human PR reviews on linked PRs, or mention Replicas when they want a specific review comment handled. ### How is this different from a bot that only comments on a PR? Comment-only review tools can identify issues. Replicas is useful when the next step is engineering work: reproduce the problem, edit code, run checks, and return a fix or investigation note. ### Does Replicas replace human code review? No. Replicas is designed to handle review follow-up work while keeping humans in the loop for inspection, steering, approval, and merge decisions. ## Related docs - [Automated code review](https://tryreplicas.com/use-cases/automated-code-review): Compare comment-only review automation with agent workflows that can investigate and follow up. - [Code review follow-up](https://tryreplicas.com/use-cases/code-review-follow-up): See how review comments, CI failures, and repo checks become tracked engineering work. - [Cloud coding agents](https://tryreplicas.com/cloud-coding-agents): Learn how cloud workspaces let agents run software engineering tasks away from a local machine. - [GitHub integration](https://docs.tryreplicas.com/features/github): Configure GitHub PR comments, reviews, and CI failure response workflows. - [Automations docs](https://docs.tryreplicas.com/features/automations): Create automations that run review checks and follow-up workflows. - [Workspaces docs](https://docs.tryreplicas.com/features/workspaces/overview): Understand how Replicas workspaces run coding agents and keep outputs inspectable.