Two AIs, One PR: Adversarial Code Review with LLMs (p.agnihotry.com)

A developer has introduced an innovative workflow called Claudex to enhance the coding and review process involving AI systems. Observing a previously undetected issue where single-agent AI-generated code appeared plausible yet created regressions across multiple applications, the developer implemented a two-AI review system. In this setup, Claude generates the code while Codex reviews it, enforcing accountability and a clear audit trail through structured commits and comments. This approach not only helps catch mistakes that would otherwise go unnoticed but also documents the reasoning behind code changes, reducing knowledge loss and improving team understandability. The significance of this dual-AI process in the AI/ML community lies in its potential to enhance software stability and reliability. By addressing three major issues—blind spots in reviews, cross-surface drift between different application components, and the absence of a review paper trail—this method has drastically reduced cross-client regressions. The disciplined approach, which includes running contract checks across diverse platforms before merging, fosters a more trustworthy collaboration with AI, ensuring safety in shipping code changes. This case exemplifies how integrating multiple AI perspectives can create a more robust software development environment.