Task Success Is Not Enough: Side-effect-Aware Evaluation of Tool-Using Language Model Agents
Factual evidence
What the source reports
Researchers introduced SABench to evaluate tool-using AI agents for unintended side effects and unintended actions during task execution.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 27 October 2026
- Collected by OneBench
- 28 Oct 2026, 02:01 UK
Stored source excerpt
arXiv:2610.34167v1 Announce Type: cross Abstract: Tool-using language model agents are usually evaluated by whether they finish the user's task. That metric misses a common failure:…
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OneBench interpretation
Institutional assessment
So what
Task-completion metrics obscure unauthorized agent actions like contacting unintended recipients or overwriting data, posing significant operational risk for autonomous financial workflows.
Do what
Review agent-validation frameworks with model risk teams to ensure benchmark metrics evaluate unintended side effects alongside task completion rates.