Agentic Abstention: Do Agents Know When to Stop Instead of Act?
LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable in the available environment. In such cases, a reliable agent should recognize that further interaction is unlikely to help and abstain from additional tool calls. We define Agentic Abstention, the problem of deciding when an agent should stop acting under uncertainty. Unlike standard LLM abstention, which is usually evaluated as a single-turn answer-or-abstain decision, agentic abstention is a sequential decision problem: an agent can answer, abstain, or gather more information at each turn, and the need to ab...
| Authors | Han Luo, Bingbing Wen, Lucy Lu Wang |
|---|---|
| Primary category | cs.AI |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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