The Agent Question Nobody Asked: When Should AI Interrupt You?

Most people assume an AI agent should ask for clarification as early as possible. This paper shows that the truth is more subtle.For long-horizon agents — AI systems that execute many steps over time — the value of a clarification depends on what is missing : goal, input, constraint, or context. Some answers lose value almost immediately. Others remain useful much later.For enterprises, this is not a UX detail. It is a governance problem : when should an agent stop, ask, and avoid compounding a bad assumption?Inspired by the work of Anmol Gulati, Hariom Gupta, Elias Lumer, Sahil Sen, and Vamse Kumar Subbiah, this episode was created using Google's NotebookLM.Read the original paper here :https://arxiv.org/abs/2605.07937v1