arXiv AI
Aug 14
Auditable Agents
arXiv:2604. 05485v2 Announce Type: replace Abstract: LLM agents call tools, query databases, delegate tasks, and trigger external side effects.
When Agents Act Unwatched: The Reduced-Supervision Paradox in Agentic AI
The paper "When Agents Act Unwatched: The Reduced‑Supervision Paradox in Agentic AI" discusses how the promise that AI systems will continue acting after users stop watching creates an accountability inversion. It argues that as stepwise supervision recedes, verification shifts into the runtime infrastructure—authority, records, interrupts, outcome checks, and repair—forming what the authors call the reduced‑supervision paradox. A 63‑artifact audit across research papers and engineering sources shows that agents’ action surfaces are more visible than the mechanisms needed to hold them accountable, with tool mediation and monitoring traces appearing in 40 and 37 artifacts, while checkpoint placement, validator independence, recovery, and contestability are rarely visible. "whyItMatters":"The study highlights that observable action paths can replace accountability when verification is moved onto users after meaningful intervention is no longer possible."
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arXiv AI
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arXiv AI
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