Researchers frame agent clarification as an active inference problem

A new arXiv paper proposes treating context acquisition for AI agents as an active inference problem. The work focuses on a common agent design question: when a user leaves out a constraint, should the system ask a question, retrieve information, try a tool, proceed with an assumption, or stop?

The authors model the tradeoff using beliefs over a hidden task state and actions selected to reduce expected uncertainty under cost. In simpler deterministic settings, that can resemble choosing the question or action with the best information gain per token.

The paper evaluates the idea with optimal question asking tasks and other agent scenarios. It is research rather than a deployed product, but it gives developers a clearer vocabulary for balancing speed, token cost, and the risk of acting on missing context.