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.

1. Review signal arrives

   A CI failure, human PR review, review-bot comment, or automation finding creates a concrete follow-up task.

2. Replicas inspects the context

   The agent reads the diff, logs, comments, repository rules, and surrounding code inside a cloud workspace.

3. 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 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.

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

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.

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?

• Automated code review

  Compare comment-only review automation with agent workflows that can investigate and follow up.

• Code review follow-up

  See how review comments, CI failures, and repo checks become tracked engineering work.

• Cloud coding agents

  Learn how cloud workspaces let agents run software engineering tasks away from a local machine.

• GitHub integration

  Configure GitHub PR comments, reviews, and CI failure response workflows.

• Automations docs

  Create automations that run review checks and follow-up workflows.

• Workspaces docs

  Understand how Replicas workspaces run coding agents and keep outputs inspectable.