Addressing the code review bottleneck

The bottleneck of reviewing code generated by AI agents can be managed using three primary methods. First, an adversarial review agent should perform internal code reviews before a human sees the pull request, catching issues early. Second, agents should verify their work end-to-end using computer use tools to simulate user interaction, providing screenshots and recordings as proof. Finally, each pull request should be scored for merge risk based on criteria like impact, alignment, and rollback cost, helping human reviewers prioritize their focus on the most critical changes.