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From Evidence to Action: How Tool-Using Agents Fail
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Academic research evaluates failure modes in tool-using AI agents, finding static capabilities fail to predict execution safety.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 7 October 2026
- Collected by OneBench
- 8 Oct 2026, 03:01 UK
- Original headline
- From Evidence to Action: How Tool-Using Agents Fail ↗
Stored source excerpt
arXiv:2610.07753v1 Announce Type: new Abstract: Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by…
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The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.
OneBench interpretation
Institutional assessment
So what
Static evaluation benchmarks fail to capture execution errors and unevidenced actions in autonomous agent workflows.
Do what
Review agentic validation frameworks with the team responsible for model risk management before deploying execution-capable agents.