AI

Agents' Last Doubt: Knowing When to Act and When to Step Back in Tool-Using Agents

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 20/10/2026, 04:03:25 UTC.

Briefings

A new counterfactual dataset of 3,115 outcomes from 1,036 decision points in ToolSandbox enables training of SURE-Act, a controller that improves offline action-selection utility by 0.0309 and end-to-end gains of up to 0.0575 in ToolSandbox's Overall Score. SURE-Act outperforms adapted decision rules from SAGE-Agent and Calibrate-Then-Act by 0.1506 and 0.1438 respectively. The study addresses the challenge of balancing task success against interaction cost and irreversible errors in tool-using agents.

Key points

• SURE-Act controller selects actions for a frozen backbone agent at inference time using a counterfactual dataset of 3,115 outcomes from 1,036 decision points.
• Learning to rank alternative actions improves offline action-selection utility by 0.0309 compared to a regression-only baseline.

Context

Tool-using LLM agents face decision points under uncertainty, requiring trade-offs between task success, interaction cost, and irreversible error risks. Existing trajectories only record outcomes, missing consequences of alternatives. This work constructs a counterfactual dataset to address this gap.

Limitations

The dataset contains 3,115 outcomes from 1,036 decision points in ToolSandbox. The study establishes the first counterfactual dataset for ToolSandbox and provides actionable insights, but specific causal claims regarding the exact mechanism of improvement remain unverified.

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