Solutions for enabling agentic sensing with network optimization

Integrated Sensing and Communications (ISAC) represents a paradigm shift in wireless networks, where sensing and communication functions are jointly designed and optimized. By leveraging the same spectral and hardware resources, ISAC enables advanced capabilities such as environment perception, object tracking, and situational awareness, while maintaining efficient and reliable data transmission. There are sensing scenarios and use cases that involve a distributed sensing task, in which multiple sensors participate and contribute with (raw or pre-processed) sensing data, which is processed by a sensing service (e.g., fusing sensing measurements from the different sensors). This sensing service needs to be executed on some kind of sensing processing/computing function which receives raw (or preprocessed) data from multiple sources, potentially of different (heterogeneous) kinds (e.g., RF and non-RF sensing, or RF from different radio technologies). This processing might impose time synchronization constraints on the reception of the different parts of data, as well as potentially specific computing and/or AI/ML capabilities on the processing node. The joint selection of sensing entities, processing locations, and network configuration under time-varying conditions results in a large, coupled, and non-stationary decision space. These characteristics motivate the use of agentic AI to enable distributed, closed-loop configuration and reconfiguration of sensing and networking resources. This document presents initial considerations and potential solution directions for an architecture that enables the use of agentic AI for sensing (as an exemplary use case) supporting network optimization.