When Agents Fail: Model or Harness--Who's to Blame?
Factual evidence
What the source reports
Researchers introduced a framework to evaluate AI agent failures by separating model capabilities from execution harness performance.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 26 October 2026
- Collected by OneBench
- 27 Oct 2026, 02:01 UK
- Original headline
- When Agents Fail: Model or Harness--Who's to Blame? ↗
Stored source excerpt
arXiv:2610.32483v1 Announce Type: cross Abstract: LLM agents combine models with execution harnesses, so agentic benchmark performance reflects their joint behavior. We argue that harnesses should…
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OneBench interpretation
Institutional assessment
So what
Isolating framework bugs from underlying model failures changes how institutions diagnose and mitigate operational errors in agentic deployment.
Do what
Review agent validation frameworks to ensure error-attribution metrics distinguish between execution harness failures and model reasoning errors.