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Add AI code review at the pull request or merge request stage, where it can comment on a change while it is still being reviewed. Keep builds, tests, linting and security scans as separate, repeatable CI checks, and treat AI findings as review input—not proof that code is correct or safe. People should retain responsibility for merge decisions, especially for consequential changes.
Where AI review fits in a delivery pipeline
Trigger an AI review when a pull request (PR) or merge request (MR) opens, or when new commits arrive if the platform supports that configuration. The review should return findings in the change’s conversation or review interface, so authors and reviewers can evaluate each comment in context.
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This is different from replacing CI. Use conventional CI jobs for builds, unit and integration tests, linting, and security scanning: these checks can be rerun against the same change and configured as merge requirements. An AI reviewer can flag possible defects, unclear code, or missed conventions, but its comments may be incomplete or incorrect. Do not treat a clean AI review as a passing test or security assessment.
Choose an integration that fits your repository host
GitHub Copilot code review is designed for pull requests; GitLab Duo Code Review Flow runs as a CI/CD job in merge-request context. The right starting point depends on the host, deployment, eligibility, workflow, and the automation capacity your team already operates.
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| Consideration | GitHub Copilot code review | GitLab Duo Code Review Flow |
|---|---|---|
| Review surface | Pull requests. GitHub also documents use through the CLI, mobile, IDEs, and Azure DevOps public preview. See GitHub’s overview. | Merge-request context through GitLab Duo Agent Platform flow. See GitLab’s Code Review Flow documentation. |
| Execution | Agentic review capabilities use GitHub Actions; workflow customization is documented. See GitHub’s code review guide. | Runs as a CI/CD job and requires a configured runner or hosted runner. See GitLab’s flow prerequisites. |
| Configuration | Reviews can be requested manually, and automatic review settings are documented for eligible plans. Repository instructions can tailor review. See GitHub’s configuration guide. | Requires group-level enablement and documented project and runner prerequisites; an agent configuration file is recommended for project toolchain and dependency context. See GitLab’s flow documentation. |
| Availability | Paid Copilot plans; organization policies can control availability. Confirm current eligibility in GitHub’s overview and configuration documentation. | GitLab.com, Self-Managed, and Dedicated offerings, with version, tier, setting, and runner requirements that vary. Confirm the current requirements in GitLab’s flow documentation. |
For GitHub repositories
Start by checking whether your organization’s Copilot plan and policies permit code review. A reviewer can request Copilot on a PR; GitHub also documents automatic review configuration for eligible plans. For automation through the REST API, GitHub’s guide specifies requesting copilot-pull-request-reviewer[bot]. Agentic capabilities use GitHub Actions, so account for Actions availability and customize the workflow if needed. Follow the current steps in Using GitHub Copilot code review and Configuring code review by GitHub Copilot.
For GitLab repositories
GitLab’s Code Review Flow executes as a CI/CD job rather than a separate reviewer request. Before enabling it, check the group-level flow settings, project permissions and prerequisites, and the runner setup, including runner availability, tags, and executor where relevant. Also confirm any GitLab Duo namespace configuration required by your deployment. GitLab recommends an agent configuration file to give the flow access to the project’s toolchain and dependencies. Follow the current Code Review Flow documentation; exact availability and requirements depend on deployment, version, tier, and configuration.
Give the reviewer useful project context
Without local conventions, an AI reviewer may miss what matters to your codebase or raise comments that do not fit it. Supply concise, maintainable guidance about architecture, preferred patterns, test expectations, high-risk areas, and what reviewers should prioritize.
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- GitHub: use a repository-wide
.github/copilot-instructions.md, path-specific instruction files, orAGENTS.mdcontext for code review. See GitHub’s code review guide. - GitLab: configure custom review instructions and consider the recommended agent configuration file for toolchain and dependency context. See GitLab’s flow documentation.
Keep instructions specific enough to guide review, but do not use them as a substitute for executable tests, enforced security checks, or documented ownership rules.
Keep merge gates and human accountability clear
Make the roles explicit in repository policy: CI reports whether required checks pass; AI comments suggest issues to investigate; human reviewers decide whether the change is acceptable. Keep required tests and security scanners in their existing CI jobs rather than inferring their result from an AI review.
Set a human review policy for sensitive changes, such as changes to authentication, authorization, secrets handling, deployment configuration, or other high-impact areas. Limit automation’s permissions and credentials to what it needs, particularly for workflows that process contributions from outside the organization or invoke agents with tools. GitLab’s guidance on security threats in agentic systems discusses risks and access management. On GitHub, rollout guidance recommends integrating tests in Actions or another CI/CD system and cautions that guardrails cannot ensure vulnerable or error-prone code will never be merged; see Maintaining codebase standards in a GitHub Copilot rollout.
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- Confirm prerequisites. Check product eligibility, organization policy, hosting or deployment requirements, and the availability of GitHub Actions or a suitable GitLab runner.
- Enable advisory reviews. Start with comments on a limited set of repositories or teams. Do not make AI findings a required merge gate during the initial rollout.
- Add project guidance. Give the reviewer relevant conventions, risk areas, and test expectations using the platform’s supported instruction or agent configuration files.
- Review the results with developers. Track whether findings are useful, noisy, timely, and acted on. Compare feedback with existing review and CI outcomes; do not assume an accuracy or time-saving rate in advance.
- Adjust policy deliberately. Improve instructions, permissions, and scope based on observed results. If considering any blocking behavior, evaluate that narrow policy carefully and keep deterministic checks and appropriate human review in place.
Plans, preview status, models, tiers, runner requirements, and controls can change. Check the linked platform documentation and your organization’s settings before promising that a feature is available or enabling it broadly.
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