An AI model reviewing its own work carries the same assumptions and blind spots into the review, making its own mistakes invisible. Using a model from a different company ensures a truly independent perspective, which is the entire point of a code review.
Instead of supervising every step, the human's most leveraged role is to act as a gatekeeper at critical junctures. The AI system handles all intermediate work, presenting a complete package for a single, high-stakes decision. This maximizes human judgment and minimizes micromanagement.
To prevent AI reviewer feedback from being ignored or blindly implemented, enforce a strict rule: every finding must be dispositioned in writing as 'fixed,' 'rejected,' or 'backlogged.' This creates a committed, searchable audit trail, making the review process transparent and valuable long-term.
An AI session anchors on its own internal reasoning. If a review fails, it's proof the session's logic was flawed. Instead of trying to correct the existing session, starting a fresh one for fixes acts as a powerful debugging tool, providing clean context and avoiding a cascade of errors.