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To add AI code review to your workflow, configure the feature for your code host, choose whether reviews are manual or automatic, add instructions that describe your project’s standards, and keep your existing human review and merge protections in place. On GitHub, the relevant feature is GitHub Copilot code review for pull requests. On GitLab, you can request GitLab Duo review on merge requests or configure its agentic Code Review Flow, which has additional group and CI runner requirements.

Choose the review mode that fits your workflow

Start with where your team hosts code and how much automation you want. A manually requested review gives developers control over when AI weighs in. Automatic review can cover new requests without someone remembering to ask, but you should decide whether it should run on drafts and again after new commits.

Option How it starts Key setup considerations
GitHub Copilot code review Manual requests or automatic pull-request reviews, depending on configuration. Personal, repository, organization, and ruleset controls are available. Automatic personal review has plan or license requirements; draft and new-push behavior are separate choices.
GitLab Duo reviewer Assign @GitLabDuo or use automatic merge-request reviews. Automatic settings can be configured at project, group, or instance scope. Drafts, requests with no changes, and requests matching exclusions are exceptions to automatic review.
GitLab Duo Code Review Flow An agentic flow that runs as a CI/CD job. Requires group enablement, an eligible project role, and a configured runner or hosted runners. This is a distinct setup from requesting the non-agentic Duo reviewer.

These options are not identical implementations. Pick the one your host supports and that fits your permissions, automation needs, and data policies.

Set up GitHub Copilot code review

Enable personal automatic reviews

  1. Open your Copilot settings and select Code review.
  2. Enable Automatic Copilot code review.
  3. Choose separately whether to review draft pull requests and whether to review each new push.

GitHub lists personal automatic review for Copilot Pro, Pro+, and Max, or a Copilot Business or Enterprise license. It is unavailable for managed user accounts. Repository and organization rulesets can also request reviews; overlapping settings result in a single review.

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Configure repository or organization behavior

Repository administrators can use repository settings under Copilot → Code review to configure automatic behavior and review effort. Organization owners can set defaults across repositories, and enterprise-level rulesets can target organizations and repositories and require Copilot review.

Decide when repeat reviews should run. Without review-on-new-push behavior, GitHub says a pull request is reviewed only once. Draft reviews can give contributors feedback earlier, before human review is requested. Review timing and effort are separate controls: changing automatic review does not remove the selected effort level for manual requests. Re-reviews can repeat comments that were previously dismissed or downvoted.

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Choose effort and add repository instructions

GitHub describes Lite as standard, targeted review and Balanced as deeper analysis for complex logic, security-sensitive code, and cross-service changes. Balanced can use more AI credits and marginally more GitHub Actions minutes. The configuration page reviewed listed Max as “Coming soon,” so do not assume it is generally available.

Put project review guidance in .github/copilot-instructions.md for repository-wide instructions, and use path-specific instructions when different areas need different standards. For example, direct the reviewer to apply a security checklist to authentication code or to check changed API handlers for compatibility. GitHub reads instructions and skills from the pull request’s head branch, so proposed instruction changes can be tested in that pull request.

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Set up GitLab Duo review

Request the non-agentic reviewer

On a merge request, either assign @GitLabDuo as a reviewer or enter /assign_reviewer @GitLabDuo in a comment. This manual route is useful when you want a developer to decide when AI feedback is appropriate.

For automatic reviews, configure the feature at project, group, or instance scope. Settings cascade, with more specific settings taking precedence. Automatic review skips draft merge requests, requests with no changes, and requests that match exclusion rules. An excluded request can still be reviewed manually.

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Enable the agentic Code Review Flow

  1. Confirm that the group and project meet the relevant GitLab Duo Agent Platform prerequisites.
  2. At the top-level group, enable Allow foundational flows and Code Review.
  3. Confirm that the person setting up or using the flow has Developer, Maintainer, or Owner access on the project.
  4. Provide a runner configured with the gitlab--duo tag and a Docker-capable executor, or enable hosted runners.
  5. Add an agent configuration file with project toolchain and dependency context; GitLab recommends this so the flow can work with the project’s environment.

Because the flow runs as a CI/CD job, include runner availability and CI configuration in your rollout plan. It is not simply another name for assigning the non-agentic Duo reviewer.

Add merge-request instructions and assess context

GitLab supports custom merge-request review instructions. For its non-agentic reviewer, GitLab documents the merge-request title and description, original contents of changed files, diffs, filenames, and custom instructions as model inputs. Before enabling it for private code, check that this context is permitted by your organization’s data policies.

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GitLab documents prompt guardrails including structured prompts, context boundaries, and filtering tools intended to reduce sensitive-data exposure and prompt-injection risk. Those safeguards reduce risk; they do not establish that sending code is risk-free.

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Decide when AI should review and what it should see

Choose review timing deliberately

  • Manual review: use it when developers should choose which requests need AI feedback or when you are piloting the feature.
  • Review on opening: use automatic review when you want a consistent first pass on new requests.
  • Draft review: enable it when early feedback is useful before a request is ready for human review.
  • Review after new pushes: enable this if each update should receive another pass; do not assume a later push triggers a review by default.

Review-on-push can produce repeat comments, including comments that someone previously dismissed or downvoted. Decide whether the extra coverage is worth the potential noise for your team.

Set boundaries for code context

Review the feature’s documented inputs and your own organization’s policies before sending private code to a model. On GitLab, the documented context for the non-agentic reviewer includes request metadata and changed-file content, not only the diff. For GitHub, the reviewed setup documentation does not settle code-review-specific retention and processing terms for every plan or deployment; check the terms that apply to your exact organization and plan.

Roll it out without weakening review controls

  1. Start narrowly. Select a limited set of repositories and begin with manually requested or draft reviews where that fits your process.
  2. Write project-specific guidance. Tell the reviewer which conventions, security checks, compatibility concerns, or paths matter. Keep instructions specific enough to guide review without asking it to approve changes automatically.
  3. Evaluate comments against the change. Ask human reviewers to check each finding against the diff and project standards, resolve valid issues, and flag false positives so the team can improve its instructions or exclusions.
  4. Expand automation only when it fits. Once the instructions and exclusions work for the pilot, decide whether to enable broader automatic, draft, or new-push reviews.
  5. Keep merge safeguards active. Preserve human approval requirements and branch protections. GitHub approvals require explicit configuration and are described as a public preview in the cited documentation; do not treat an AI review as approval or as a security certificate.

Plan for review failures and limits

GitLab documents that a large merge request can exceed the selected model’s context window. In that case, the reviewer retries without original file contents, which reduces context and may make feedback less specific. A second failure returns a generic error. GitLab documents a 120-second AI Gateway request timeout for Duo Code Review. Smaller merge requests and excluding irrelevant file context can reduce the chance of context-related failure.

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GitLab also characterizes Security Review Flow results as AI-generated advisory input, not an authoritative or complete security assessment. Apply the same caution to AI review generally: it can help surface issues, but it does not replace project expertise or established security review.

Use a practical go-live checklist

  • Confirm the code host, eligible plan or add-on, and the permissions needed to enable the feature.
  • Choose manual or automatic triggering, then explicitly decide about drafts and new pushes.
  • Add repository or merge-request instructions that reflect your actual standards.
  • For GitLab Code Review Flow, verify group enablement, project role, and runner setup before expecting jobs to run.
  • Check code-context handling against the organization’s data policies and applicable plan terms.
  • Keep human approvals and merge protections in force, and establish how contributors report valid findings and false positives.

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