The question that keeps coming up is how a team reviews the growing volume of AI-generated code without burning out its human reviewers. The honest answer on Azure DevOps is that Microsoft has documented exactly one native lever, and it does not pretend to replace the human. It changes what the human looks at.
That lever is Copilot code review for Azure Repos, acting as an automated reviewer, plus the branch policy machinery Azure has had for years. The two combine into a workflow where the machine absorbs the routine first pass and the human stays the gate. Everything below comes from Microsoft's own documentation, and the edition split matters more than most writeups admit.
Copilot code review is an automated reviewer, not a merge gate
The Copilot code review documentation describes Copilot as "an automated reviewer that posts comments and suggestions on changed code, so you get feedback before a human reviewer signs off." That sentence is the whole strategy. The model is not approving anything. It is a first pass that lands before the human does the work, and approval stays with people.
Three scope details are worth knowing before treating this as your volume answer.
First, edition. Copilot code review for Azure Repos is Azure DevOps Services only. The page carries the standard limited-preview banner: no Service Level Agreement, limited support, functionality can change or be removed without notice. Nothing on the page describes it for Azure DevOps Server. For self-managed teams on Server, the native path is not stated in the docs, which means the answer is a third-party tool wired in by webhook or a build-your-own reviewer, not native Copilot.
Second, enablement is three scopes. A Project Collection Administrator turns the feature on for the organization, a Project Administrator enables it at project level or lets repository owners override, and an individual repository owner enables it on the pull requests in their repo. There is no surprise here, but the three-layer structure matters if you want central control over where the AI reviewer is allowed to run rather than letting each repo decide on its own.
Third, TFVC repositories are not supported. If your team still uses Team Foundation Version Control, this feature does not apply, and that is stated plainly in the prerequisites.
The review effort control, and automatic review policies
Once enabled, the volume story gets concrete. A "review effort" setting controls how thoroughly Copilot analyzes a pull request. A project administrator sets the default and decides whether repository administrators may override it, and a user can pick a different effort level for a single review. The docs are explicit about the trade: higher effort consumes more tokens and increases cost. Billing runs through Azure Cost Management, so this is metered and visible.
The other part is automatic review policies, which let Copilot review new pull requests without anyone requesting a review. That is the real volume lever. Instead of every human starting by chasing down which PRs need their eyes, the model is already in the review list when the PR opens. The person arrives at a PR that has already been read once, with the findings waiting.
This matters specifically for the growing-AI-code scenario. The failure mode of volume review is that reviewers skim everything and miss the one change that matters. An automated first pass does not fix attention, but it does compress the number of things each human has to look at, because the model's comments mark where to look.
Branch policies keep the human in the gate
Copilot code review never replaces the human gate, and this is where the older branch policy system earns its keep. The branch policies documentation is edition-broader than the AI feature. It applies to Azure DevOps Services, Azure DevOps Server, and Azure DevOps Server 2022, so self-managed teams get this part even when they cannot get native Copilot review.
The relevant policies for an AI-volume world are the ones that enforce human sign-off without asking humans to review everything:
- Require a minimum number of reviewers keeps the approval count real. The model can comment, but humans still have to approve on protected branches.
- Automatically included reviewers assigns specific people when changes touch certain paths. This has been the standard way to make sure the right human sees a risky change, with or without AI.
- Check for comment resolution forces the thread to be talked through before a PR completes. With a model now posting comments, this policy is how you guarantee the model's findings are actually addressed rather than silently dismissed.
- Build validation runs a pre-merge build on the changes, which catches the class of AI-generated mistakes a reviewer would rather not be the first to see.
- Status check policies let other services post success before a PR completes, which is the documented hook that a third-party reviewer on Server edition can use.
The framing that works: the AI absorbs the breadth, the branch policies keep the depth human.
What a volume workflow looks like on Azure Repos
A setup that answers the question directly, using only documented pieces:
- Enable Copilot code review at the three scopes for the repositories you want covered. Set a moderate default review effort at project level so cost scales with the default rather than the loudest repo.
- Turn on automatic review policies so new PRs get the model's first pass without manual requests.
- On your protected branches, require a minimum number of human reviewers, enable comment resolution checking, and add build validation.
- Audit the model's comments on a sample of PRs before you widen the default effort level. The limited-preview status means you should not relax human gates on the strength of a feature the vendor has not put on an SLA.
The Server edition, where the native path is not stated
For Azure DevOps Server there is no documented native Copilot review, so the automated first pass has to come from outside the vendor. A third-party reviewer like CodeRabbit integrates with Azure DevOps and runs as an external review service, which means it can sit in the status check policy slot described above. The branch policy half of the workflow works identically on Server and becomes the human gate for that tool too.
One caveat applies to the third-party route the same way it does to the native one: an external reviewer posting comments is not a gate, it is a first pass. The minimum-reviewer and comment-resolution policies are what turn its output into something your team actually acts on.
What is not stated
For self-managed Azure DevOps Server, Microsoft's docs do not say when or whether native Copilot code review will arrive. The limited-preview status of the Services feature reinforces that this is not a production guarantee. Teams on Server who want the automated first pass have to source it outside the vendor, and the branch policy machinery is what keeps the result safe while they do.
The complete answer to the volume question is therefore two documented halves. Native Copilot review on Services makes the first pass automatic and metered. The branch policy system, present across every Azure DevOps edition, keeps the human gate intact so the model's volume does not become the team's risk. Neither half does the whole job, and Microsoft's documentation is clear about what each one does. That is the honest state of the feature, and it is worth reading the pages yourself before promising your team otherwise.
