Optimizing Agents with Active Inference: Fetch Only Context That Changes Decisions

rohanpaul_ai · x · 2026-08-24

AI agents face a costly problem: when information is missing, they either guess too early or keep retrieving/calling tools without knowing if more context is worth the cost. This paper uses active inference to make this choice explicit: every clarification, retrieval, or tool call must earn its tokens and latency by reducing uncertainty relevant to the final answer. In tests, frontier models narrowed down answers but used more questions than an optimal planner. On a generation task, targeted clarification raised verifier compliance from 0.0417 to 0.375. The recommendation is to give agents an explicit context-acquisition layer, deciding before each step whether to ask, retrieve, or act—stopping once the missing context that changes the decision is obtained.

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Optimizing Agents with Active Inference: Fetch Only Context That Changes Decisions

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