When Agentic AI Meets Integrated Sensing and Communication

• URL: http://arxiv.org/abs/2608.05792v1
• Date: Thu, 06 Aug 2026 09:26:49 GMT
• Title: When Agentic AI Meets Integrated Sensing and Communication
• Abstract summary: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) into a goal-driven, closed-loop intelligent system.<n>This survey unifies the literature within a six-stage closed-loop framework.<n>It introduces five levels of agentic maturity, ranging from physical-layer primitives to fully closed-loop ISAC.
• Score: 23.084474668808912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC. Existing work on learning-based sensing, resource allocation, reconfigurable intelligent surfaces (RIS), edge intelligence, multi-agent coordination, and resilient networking has developed largely in isolation. This survey unifies the literature within a six-stage closed-loop framework comprising observation, contextualization, reasoning and prediction, planning and orchestration, execution and collaboration, and feedback and resilience. It also introduces five levels of agentic maturity, ranging from physical-layer primitives to fully closed-loop agentic ISAC. We use this framework to review advances in multimodal intelligence, large language models, reinforcement learning, federated learning, RIS-assisted control, Unmanned Aerial Vehicle (UAV) and vehicular networks, and AI-native network management, and analyze privacy, security, resilience, and sustainability as cross-cutting requirements of the full perception-reasoning-action loop. An audit of representative studies against nine agentic-specific evaluation criteria shows that no system reports more than one or two of them, exposing a gap between claimed and demonstrated agentic maturity. We identify open challenges in physical-to-semantic grounding, predictive world models, real-time agent-PHY interaction, safe tool use, heterogeneous multi-agent collaboration, benchmarking, and resource-efficient autonomy.

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