A million PRs later: agentic review is faster, not clearly better
Study of 1.02M reviewed PRs across human, LLM-assisted, and agentic eras—efficiency gains without matching quality gains.
This empirical study tracks 1.02 million reviewed PRs across 207 GitHub projects through three eras: human-centric, LLM-assisted, and agentic review. They model review discussions as interaction sequences among humans, LLM reviewers, and AI agents.
Agent-involved patterns correlate with faster decisions under gradual and rapid agent adoption. Those speed gains do not translate into better review quality. Once AI reviewers show up, human-AI collaboration patterns become the strongest explanatory factor for efficiency—activity and PR type still matter, but the interaction graph dominates.
Why it matters for code review
Shipping an agent reviewer can optimize cycle time while quietly degrading the quality signal. Instrument both. If your dashboard only shows “time-to-approve,” you will celebrate the wrong success.