arXiv:2606.28733v1 Announce Type: new Abstract: 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 decisio
The rapid advancement and deployment of LLM agents in increasingly complex, real-world environments necessitate robust methods for managing uncertainty and preventing undesirable or inefficient actions.
A strategic reader should care because improving agentic abstention is crucial for the reliable, safe, and efficient deployment of AI agents in critical applications and collapsing white-collar workflows.
This research introduces a formal framework for agents to recognize when to cease action under uncertainty, moving beyond basic single-turn abstention and enabling more robust, multi-turn operational capabilities.
- · AI Agent developers
- · Enterprises adopting AI agents
- · Automation software providers
- · Inefficient AI agent deployment strategies
- · Users facing unreliable agent interactions
More reliable and trustworthy AI agents become deployable across a wider range of enterprise applications.
Reduced operational costs and increased efficiency in processes managed or supported by AI agents due to fewer failed or unnecessary actions.
The development of 'responsible agent ecosystems' where autonomous systems inherently understand limitations and risks, accelerating broader societal adoption while mitigating black swan events.
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Read at arXiv cs.AI