Active Inference Reframes AI Agent Context

The paper identifies a promising design principle for agents: uncertainty should trigger measured information gathering, not confident improvisation.

• –Treats clarification, retrieval, tool calls, and prompt trials as actions with explicit costs
• –Uses expected information gain to decide whether more context justifies its expense
• –Instantiates the idea through Optimal Question Asking across tasks with up to 300 candidates
• –Could reduce hallucinations and wasted tool calls by delaying irreversible actions
• –The main gap is deployment: real-world agents need reliable uncertainty estimates and semantic validation