Wireless agentic systems enable agents to autonomously perceive, reason, and act. However, existing works neglect the tight coupling between sensing and control in closed-loop integrated sensing and communication (ISAC) systems. In this paper, we propose an active inference (AIF)-driven wireless agentic system for closed-loop ISAC, which jointly optimizes control and sensing resource allocation via backward--forward message passing on a factor graph. The AIF agent maintains a generative model as a digital twin by integrating a localization model for uncertainty-aware state inference and a localization channel knowledge map (CKM) for approximating observation quality during planning. Simulation results demonstrate that the AIF-enabled agent adaptively allocates sensing resources based on spatially varying channel conditions, achieving superior balance among tracking accuracy, control effort, and sensing resource consumption over baseline strategies.
@article{arxiv.2604.19599,
title = {Active Inference-Enabled Agentic Closed-Loop ISAC with Long-Horizon Planning},
author = {Guangjin Pan and Zhuojun Tian and Mehdi Bennis and Henk Wymeersch},
journal= {arXiv preprint arXiv:2604.19599},
year = {2026}
}
Comments
5 pages, 4 figures and 1 table. This work has been submitted to the IEEE for possible publication