Copilot's Approval Preview Gives AI Review a Defined Place in the Merge Decision

When an assessment can become an approval

GitHub introduced Copilot pull-request approvals in public preview on September 1 for Pro, Pro+, Max, Business and Enterprise. An approval assessment appears in the review overview, but that assessment alone does not meet merge requirements. Actual approving reviews require administrators to enable the feature. The [release](https://github.blog/changelog/2026-09-01-copilot-code-review-can-now-approve-pull-requests/) gives AI-generated review a possible formal role in a software team's decision process, with approval counting disabled by default.

That is a consequential product distinction. A comment saying that a change looks acceptable can inform a developer without changing the repository's state. An approval recorded against a requirement can help make the change eligible to merge. The words may sound similar to a reader, but the system treats them differently. Giving administrators control over that transition creates a way to use machine judgment deliberately, instead of allowing a persuasive paragraph to acquire authority through habit.

The opportunity is strongest where a team can define a narrow class of changes for which automated review supplies useful evidence. Repetitive maintenance can consume attention that would otherwise go to difficult design decisions. An AI reviewer may help redistribute that effort. Whether it does so depends on the work and the quality of its findings; a new approval control does not establish a reduction in defects or waiting time.

The scope includes every changed file

GitHub's [configuration guide](https://docs.github.com/en/copilot/how-tos/copilot-on-github/set-up-copilot/configure-code-review) separates permission to submit approvals from permission for them to count. Repository settings can limit counting by file patterns, with up to 15 patterns. Every changed file must match one of the allowed patterns; leaving the list blank allows all files. Enterprise and organization policies can constrain the repositories allowed to use the feature. These controls support a limited rollout without treating every project as equally suitable.

The all-files condition makes the shape of a pull request important. Imagine an allowed class of documentation edits. A change that also alters executable code should not inherit that class merely because most of its files are documentation. The criterion concerns the entire set of changed paths. For a team adopting the preview, this encourages coherent change sets whose contents correspond to the authority being granted. It also gives reviewers a concrete reason to split unrelated work that has been bundled together.

A path is still an imperfect description of risk. Documentation can contain operational commands. A small configuration file can change how a service behaves. The fact that a change falls within an allowed directory does not prove it is harmless. Teams choosing a scope need a technical understanding of what those files do, rather than treating a short patch or familiar extension as a substitute for judgment.

This is where the preview offers something more useful than an indiscriminate automation switch. An administrator can connect the permitted action to a recognizable part of the repository. That makes responsibility easier to explain to contributors: the system has authority over a defined category of work because the maintainers chose that category.

An approval belongs to the code that was reviewed

GitHub's [code-review overview](https://docs.github.com/en/copilot/concepts/agents/code-review) says a new commit dismisses a Copilot approval, after which another review can be requested. The same document explains that review uses a purpose-built combination of models and does not offer model switching. It also identifies files the service does not review, including dependency-management files, logs and SVGs. Those limits should remain visible when interpreting a favorable result.

Binding an approval to a reviewed state matters because a pull request can keep changing after a reviewer finishes. A later commit may be a harmless spelling correction, or it may alter the behavior that prompted the original review. Reusing the old approval without accounting for that difference would attach a conclusion to material the reviewer never assessed. The principle applies to machine and human reviews alike: the evidence is about a particular version of the work.

The configuration guide separately makes review of new pushes optional. Without that option, an automatic review happens only once. Dismissal therefore does not by itself mean that a fresh review has already run. A contributor needs an intelligible path from the changed code to the next review, rather than discovering a missing approval only when attempting to merge.

Review coverage deserves similar care. A reader should not translate a favorable overview into a claim that every changed artifact received equivalent scrutiny. When a patch contains material outside the reviewer's coverage, another source of evidence must carry that part of the decision. The benefit of automated review grows when its output helps a maintainer understand what remains uncertain, instead of merely making the screen look finished.

Merging still has other conditions

GitHub's [ruleset documentation](https://docs.github.com/en/repositories/configuring-branches-and-merges-in-your-repository/managing-rulesets/available-rules-for-rulesets) treats required approvals and required status checks as separate controls. Administrators can require passing checks before merging and can specify the expected application supplying a status. They can also require reviews from code owners. A Copilot approval that satisfies an approval count does not establish that a test suite passed or erase those independently configured conditions.

This gives AI review a useful place alongside other forms of evidence. A test exercises particular behavior under specified conditions. A review can reason about a change that the tests do not exercise, including an assumption that looks questionable. Neither exhausts the problem. Keeping both contributions visible allows an organization to benefit from AI reasoning while retaining mechanically enforced checks on behavior it already knows how to test.

There is a further ownership detail in GitHub's [CODEOWNERS guide](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners). Review requests use the ownership file from the pull request's base branch, and maintainers can require owner approval. The guide also recommends assigning ownership to the ownership file itself. This protects the mechanism by which a project identifies responsible reviewers. It helps distinguish a contribution to a decision from authority to redefine who makes that decision.

The preview is a constructive step toward making AI assistance part of an explicit engineering process. A model can contribute judgment, and a repository can state when that judgment counts. The strongest use of the feature will preserve the reasons behind a merge decision so a later maintainer can understand them. More approvals alone would be a poor measure of progress. Changes that reach users with less wasted effort and a clear account of why they were accepted would be a meaningful one.