Agentic Code Review

AI agents have made writing code nearly free, but human reading speed hasn't changed — making code review the new bottleneck. Four independent 2026 datasets (Faros AI, CodeRabbit, GitClear, GitHub) consistently show AI adoption quadruples code volume while delivering only ~12% real productivity gain, with defect rates rising from 9% to 54%, review times up 441%, and zero-review merges up 31%. The core problem: agent-written code has no human intent behind it, so reviewers must reconstruct reasoning that never existed — a fundamentally slower task. The right response depends on blast radius: solo developers with no users can lean heavily on AI reviewers and tests, while teams maintaining large systems need tiered, evidence-required, heterogeneous review with a human owning every merge. Practical recommendations include risk-tiering PRs, requiring evidence before review, keeping PRs small, scrutinizing test changes carefully, treating CI gates as inviolable, and running two AI reviewers with different strengths rather than one. The human role shifts from reading every diff to owning accountability, judgment on whether changes are right to build, and auditing the system at a higher level.