Don't Start What You Can't Finish: A Counterfactual Audit of Support-State Triage in LLM Agents
New diagnostic framework reveals frontier models require explicit structural cues to avoid dangerous task overcommitment.
Current agent evaluations largely reward execution on fully specified tasks, while recent work studies clarification [11, 22, 2], capability awareness [9, 1], abstention [8, 14], and search termination [20, 5] mostly in isolation. This leaves open whether agents can diagnose why ...
ArXiv CS.AI
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This research reveals that AI agents often blindly overcommit to tasks even when necessary information is missing, posing a significant risk to reliability in real-world workflows. By implementing explicit categorical decision paths, developers can unlock latent triage capabilities that allow agents to intelligently pause or clarify before executing failed operations.
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