Intelligent PR Review and Risk-Based Approval

Set the standard. Route the decision.

Book a demo Review Intelligence

Approval is a decision. Merge is a separate gate.

Combine eligibility rules, required checks and human ownership without treating one green signal as permission to ship.

GitHubReview policy / example decisions
PR created
CloudAEyeAI review
Low riskAuto-approve
Approved automatically
High riskHuman review
Routed to the right reviewer

Review every PR.
Approve by risk.

Not every pull request needs the same attention. CloudAEye reviews each change, weighs its risk, approves what is safe, and sends the rest to the people who should decide.

Detect bugs and prevent production incidents.

Identify logic errors, security vulnerabilities, regressions, and architectural issues before code reaches production. Catch defects that traditional code reviews and automated tests may miss.

  • Logic errors & regressionsCaught before merge
  • Security & architectureIssues flagged by severity
  • Critical findingsBlock approval
See how findings block approval

Assess PR risk and determine approval requirements.

Evaluate PRs based on code complexity, business impact, change scope, historical defects, and AI-generated code signals. Determine the appropriate level of scrutiny and approval required for each change.

  • Complexity & scopeSize of the change
  • Business impactWhat the change touches
  • History & AI signalsPast defects & slop score
See the review factors

Automatically approve low-risk PRs.

Identify safe, well-tested changes with minimal business and operational impact. Automatically approve eligible PRs to reduce review bottlenecks and accelerate delivery.

  • EligibilityLow-risk, well-tested changes
  • Required checksMust pass first
  • MergeRemains a separate gate
See the approval flow

Route high-risk PRs to the right human reviewers.

Identify changes requiring specialized expertise and route them to the appropriate code owners or domain experts. Require additional reviews and approvals for changes that could introduce significant business, security, or reliability risks.

  • High-risk changesLead maintainers
  • Specialized areasCode owners & domain experts
  • Consequential changesAdditional approvals
See how reviewers are chosen

Factors for assigning human reviews

CloudAEye considers review results, requirements, team context and production history together to prioritize pull requests.

  • Code Review Results

    Check for missing must-have changes and critical issues in the review findings.

  • Jira Issue Validation

    Verify that required functionality and acceptance criteria are implemented.

  • AI Slop Score

    Evaluate the change's Slop Score against the configured threshold.

  • Team History

    Consider the team's track record and manager-configured allowlists and denylists.

  • Bug Trends & Stability

    Review the stability of the affected code area and its historical bug trends.

  • Production Incidents

    Check incident history in the affected area and components.

  • Control Flow Analysis

    Detect unexpected or unnecessary changes to control flow.

  • Architecture Analysis

    Check alignment with architectural patterns and design principles, and select the best model for review.

  • Automated Tests

    The QA agent generates and reviews tests to check that product requirements are met.

  • Learn From Incidents

    Generate regression tests and review rules from past incidents to prevent recurrence and improve future reviews.

Assign the right reviewer. Balance the workload.

Lead maintainers for high risk changes.

Route sensitive, critical, or high risk changes to the affected component's lead maintainer.

Relevant expertise for other reviews.

Choose a team member with ownership and relevant knowledge of the affected code.

Workload aware assignment.

Among qualified team members, select the reviewer with the lowest current review workload. Distribute assignments without sacrificing the expertise the change needs.

Define ownership alongside your code.

Define ownership for each directory in an OWNERS.md file. Record the lead maintainer, team members, and contact details so review assignments start with the people responsible for the code.

CloudAEye can help create the file. Humans then maintain it as team members, ownership, Slack channels, and email aliases change.

OWNERS.md Example
# Component Ownership
Lead Maintainer: @username
Team Name: Core Backend
Team Members: @alice, @bob
Contact Slack: #core-backend
Contact Email: backend@example.com

Do not confuse silence with approval.

A Critical finding prevents approval. If the approval summary cannot be interpreted, CloudAEye posts a neutral comment rather than inferring approval. Checklist enforcement and the review verdict remain separate decisions.

Documented review verdicts

Commands

Claude Code
cloudaeye:assign-reviewer <pr#>
GitHub · Bitbucket · GitLab
@cloudaeye /assign_reviewer

Create an ownership file for a directory with /create_owners <dir>.