reviewing-code-multi-ai

To run a multi-AI code review, submit your code changes or pull requests to the orchestrator, which coordinates Claude Code, Codex, Gemini, and Droid to evaluate architecture, correctness, performance, and security, generating a consolidated report with issues and priorities.

Consensus scoring in multi-AI code reviews works by aggregating evaluations from Claude Code, Codex, Gemini, and Droid, cross-referencing their findings to surface agreements, detect conflicts, and generate a unified score with actionable fixes for production-grade safety.

Yes, you can automatically apply code fixes using the iterative review cycles with auto-apply options, which include rollback capabilities and test validation to ensure safe, progressive improvement of your codebase without risking breaking changes.

The best way to resolve conflicts between AI code reviewers is using the orchestrator's conflict detection mechanism, which structures guidance for applying changes, provides audit trails, and drives iterative improvements to safely manage differing architectural or security suggestions.

Multi-AI code review works with both local code changes and pull requests, allowing you to submit either format to the orchestrator for comprehensive evaluation across architecture, correctness, performance, and security perspectives with test validation and rollback support.

Using multiple AI models for code review provides cross-perspective evaluation, where orchestrating Claude Code, Codex, Gemini, and Droid surfaces consensus scores and conflicts that a single model would miss, ensuring robust audit trails and production-grade safety.