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Microsoft · Background agent

PRAssistant

PRAssistant is Microsoft's internal AI code reviewer, built by its Developer Division Data and AI team. It joins each pull request as a reviewer, summarizes the change, and comments on specific lines with suggested edits the author applies. Microsoft says it shaped GitHub's Copilot code review.

1

Approach type
Background agent
Work
Code review
Human involvement
Drafts reviewed
Invocation
Event driven
Interfaces
Github
Deployment stage
Scaled
Evidence strength
Detailed primary
Entry reviewed

Where people stay involved

  • pull request → AI review commentsWork product review · Level 3

Level 3 for pull request → AI review comments; human attention boundary: work-product-review.

1

Observation date
2025-07

Implementation details

Interfaces

github

1

Reported results and limitations

The catalog records what the sources report, with the scope and the denominator of every figure. A qualification below limits the figure it sits under.

Reported metrics

Headline claim

Supports more than 90% of Microsoft PRs, impacting over 600,000 pull requests per month

1

Reported by
Microsoft
Scope
PRs supported by the internal AI review assistant across Microsoft
Denominator
Company pull requests for coverage share
Observation date
2025-07
Key observation

More than 90% of pull requests across the company

1

Reported by
Microsoft
Scope
PRs supported by the internal AI review assistant across Microsoft
Denominator
Company pull requests for coverage share
Key observation

More than 600,000 pull requests impacted per month

1

Reported by
Microsoft
Scope
PRs supported by the internal AI review assistant across Microsoft
Denominator
Company pull requests for coverage share
Key observation

About 5,000 repositories in early onboarding

1

The source does not report the denominator of this figure.

Reported by
Microsoft
Scope
Repositories onboarded in early AI code-review experiments
Method
Early experiments and data science studies across onboarded repositories

Sources and research details

Citations link to the original publisher. Each source also keeps a preserved copy in the repository, so a changed or removed page stays checkable.

  1. Enhancing code quality at scale with AI-powered code reviewshttps://devblogs.microsoft.com/engineering-at-microsoft/enhancing-code-quality-at-scale-with-ai-powered-code-reviews/Engineering blog · First party · Last source verification: 2026-08-31
    Preserved copy ↗
Research details for every claim on this page
  1. Summary
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  2. Headline claim
    Statement type
    Metric
    Provenance
    Reported
    Confidence
    Medium
    Confidence reason
    Microsoft reported the figures in its own engineering blog without independent verification.
    Reported by
    Microsoft
    Scope
    PRs supported by the internal AI review assistant across Microsoft
    Denominator
    Company pull requests for coverage share
    Method
    Not reported
    Observation date
    2025-07
  3. Interfaces
    Statement type
    Fact
    Provenance
    Reported
    Confidence
    High
    Confidence reason
    A linked first-party source states the claim.
  4. Key observation
    Statement type
    Metric
    Provenance
    Reported
    Confidence
    Medium
    Confidence reason
    Microsoft reported the figures in its own engineering blog.
    Reported by
    Microsoft
    Scope
    PRs supported by the internal AI review assistant across Microsoft
    Denominator
    Company pull requests for coverage share
    Method
    Not reported
    Observation date
    Not reported
  5. Key observation
    Statement type
    Metric
    Provenance
    Reported
    Confidence
    Medium
    Confidence reason
    Microsoft reported the figures in its own engineering blog.
    Reported by
    Microsoft
    Scope
    PRs supported by the internal AI review assistant across Microsoft
    Denominator
    Company pull requests for coverage share
    Method
    Not reported
    Observation date
    Not reported
  6. Key observation
    Statement type
    Metric
    Provenance
    Reported
    Confidence
    Medium
    Confidence reason
    Microsoft reported the figures in its own engineering blog.
    Reported by
    Microsoft
    Scope
    Repositories onboarded in early AI code-review experiments
    Denominator
    Not reported
    Method
    Early experiments and data science studies across onboarded repositories
    Observation date
    Not reported
  7. Operating model assessment
    Statement type
    Inference
    Provenance
    Catalog judgment
    Confidence
    Medium
    Confidence reason
    The engineering blog describes engineers acting on PRAssistant review comments, which locates human attention at work-product review.
    Observation date
    2025-07

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