# Automated Code Review

Automated code review should not stop at comments. Replicas helps teams turn review checks, CI failures, and PR feedback into visible follow-up work.

- Canonical: https://tryreplicas.com/use-cases/automated-code-review
- Get started: https://tryreplicas.com/auth?mode=signup
- GitHub docs: https://docs.tryreplicas.com/features/github

## What is automated code review?

Automated code review is the use of software to inspect pull requests, detect issues, and enforce engineering expectations before code merges.

Replicas fits the part of the workflow where feedback becomes action. A finding can become a cloud coding task that inspects context, runs commands, makes a change, or returns an investigation note.

## How Replicas adds action to automated review

- **Checks produce concrete feedback:** A reviewer, CI job, static check, or repository-specific rule identifies something worth investigating.
- **Replicas opens a working context:** The agent inspects the pull request, logs, files, branch state, and local project commands from a cloud workspace.
- **The automation returns a handoff:** Replicas can push a fix, leave a tracker update, summarize the investigation, or ask for human direction.

## Automated review is more useful when it closes the loop

Finding an issue is only half of the workflow. Teams still need someone to triage the feedback, understand whether it is real, and decide what should happen next.

- **Automated review becomes actionable:** Review automation is more useful when a finding can become a task, not just another comment for a human to triage.
- **Teams can encode their own rules:** Replicas can support broad checks and narrow team rules, from unused code to framework-specific review standards.
- **CI failures stay visible:** A failing check can be tracked in one comment with an investigation status and final result.
- **Human review remains the gate:** The output is still a reviewable diff, note, or test result. Reviewers decide what should merge.

## What can trigger automated code review follow-up?

A useful signal can come from a human reviewer, a CI system, a review bot, or a rule your team defines for a repository.

- Failed test, lint, build, or typecheck jobs
- Human pull request review comments
- Automated review checks for duplication or unused code
- Repository rules such as no unnecessary effects
- Security, migration, or dependency review notes
- Style and maintainability feedback
- Repeated failures across the same PR
- A reviewer mention asking an agent to follow up

## Automated review can produce more than comments

Some review tasks need a code change, but others need verification, reproduction, or a concise handoff for the reviewer.

- **Fix commits:** Targeted changes pushed to the same branch or prepared for review.
- **Review replies:** Short responses that explain what changed, what was checked, and what still needs a human call.
- **Status trackers:** A durable PR comment that records which checks were investigated and how each one ended.
- **Investigation notes:** A concrete handoff when the right answer is reproduction detail, test output, or a recommendation.

## How to evaluate automated code review workflows

Evaluate both detection and follow-up. The strongest workflow makes feedback visible, actionable, and reviewable without removing human judgment.

- Can automation findings become actual engineering tasks?
- Can the agent inspect the repository, commands, logs, and PR diff before acting?
- Can it update one visible tracker instead of scattering comments?
- Can it follow team-specific review rules?
- Can it return non-code outputs such as test runs and investigation notes?
- Can humans review every result before merge?

## FAQ

### What is automated code review?
Automated code review uses software to inspect pull requests for issues such as test failures, maintainability problems, style violations, security concerns, or team-specific rules.

### How does Replicas fit automated code review?
Replicas is useful after a review signal exists. It can inspect the context, attempt a fix, run commands, and report back with a reviewable handoff.

### Does Replicas replace dedicated review tools?
No. Dedicated review tools can be excellent at finding issues. Replicas is strongest when the next step is follow-up engineering work.

### Can automated code review produce outputs other than pull requests?
Yes. Some review tasks should end as test results, reproduction notes, investigation summaries, or a recommendation instead of a code change.

## Related docs

- [AI code review agent](https://tryreplicas.com/use-cases/ai-code-review-agent): Learn how Replicas acts on review comments, CI failures, and automation findings.
- [Code review follow-up](https://tryreplicas.com/use-cases/code-review-follow-up): See the Replicas-native workflow for closing review loops after feedback arrives.
- [Cloud coding agents](https://tryreplicas.com/cloud-coding-agents): Understand why cloud workspaces make automated review follow-up possible.
- [GitHub integration](https://docs.tryreplicas.com/features/github): Connect GitHub events, PR comments, reviews, and CI status to Replicas workflows.