---
id: 20261021-T0-07
title: "工具型AI智能体何时该自己动手、何时该问人？新研究给出决策框架"
title_en: "When Should Tool-Using AI Agents Act, Ask for More Info, or Escalate to Humans?"
url: https://ai.daily.yangsir.net/daily/20261021-T0-07
issue_date: 2026-10-21
publish_date: 2026-10-20T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2610.24194
---

# 工具型AI智能体何时该自己动手、何时该问人？新研究给出决策框架

工具型LLM智能体在不确定条件下反复面临选择：自主执行、继续收集信息，还是升级给人类处理。这些决策需要在任务成功率、交互成本和出错风险之间做权衡。论文针对这一「最后一道疑虑」问题建立分析框架，试图让智能体判断何时该行动、何时该退后。实际影响是让自动化流程在关键节点主动交还控制权，减少智能体在不确定时硬着头皮执行带来的错误。

## English Version

**When Should Tool-Using AI Agents Act, Ask for More Info, or Escalate to Humans?**

Tool-using LLM agents repeatedly hit decision points under uncertainty: act autonomously, gather more information, or escalate to a human. Each choice trades off task success against interaction cost and the risk of error. The paper frames this "last doubt" problem and works toward agents that know when to act and when to step back. In practice, that means automated workflows can hand control back at critical junctures instead of pushing ahead while uncertain and producing mistakes.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2610.24194)

**详情页**：https://ai.daily.yangsir.net/daily/20261021-T0-07

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