# When Agentic AI Meets Integrated Sensing and Communication

arXiv: https://arxiv.org/abs/2608.05792 · HTML: https://mlsanity.com/paper/2608.05792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 45 of its 125 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 30 of 45)

- [Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach](https://mlsanity.com/paper/2011.10282/) (2020)
- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2505.02795/) (2025)
- [Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management](https://mlsanity.com/paper/2011.14197/) (2020)
- [Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions](https://mlsanity.com/paper/2508.18725/) (2025)
- [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025)
- [Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective](https://mlsanity.com/paper/2411.04136/) (2024)
- [Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design](https://mlsanity.com/paper/2201.12581/) (2022)
- [Intelligent Surfaces Empowered Wireless Network: Recent Advances and The Road to 6G](https://mlsanity.com/paper/2312.16918/) (2023)
- [Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2412.04074/) (2024)
- [Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G](https://mlsanity.com/paper/2501.06726/) (2025)
- [Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey](https://mlsanity.com/paper/2508.08620/) (2025)
- [Accelerating Edge Intelligence via Integrated Sensing and Communication](https://mlsanity.com/paper/2107.09574/) (2021)
- [Networked ISAC for Low-Altitude Economy: Coordinated Transmit Beamforming and UAV Trajectory Design](https://mlsanity.com/paper/2406.16946/) (2024)
- [Integrated Sensing and Communications Over the Years: An Evolution Perspective](https://mlsanity.com/paper/2504.06830/) (2025)
- [HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning](https://mlsanity.com/paper/2310.06336/) (2023)
- [Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data](https://mlsanity.com/paper/2405.06299/) (2024)
- [Towards Secure Semantic Communications in the Presence of Intelligent Eavesdroppers](https://mlsanity.com/paper/2503.23103/) (2025)
- [Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons](https://mlsanity.com/paper/2506.16070/) (2025)
- [Joint Active and Passive Beamforming Design for Reconfigurable Intelligent Surface Enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2206.00525/) (2022)
- [Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization](https://mlsanity.com/paper/2212.06396/) (2022)
- [Empowering Over-the-Air Personalized Federated Learning via RIS](https://mlsanity.com/paper/2408.12162/) (2024)
- [Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration](https://mlsanity.com/paper/2506.07647/) (2025)
- [Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial](https://mlsanity.com/paper/2509.05946/) (2025)
- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives](https://mlsanity.com/paper/2509.09193/) (2025)
- [Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning](https://mlsanity.com/paper/2308.07273/) (2023)
- [Integrated Communication, Sensing, and Computation Framework for 6G Networks](https://mlsanity.com/paper/2310.03265/) (2023)
- [Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data](https://mlsanity.com/paper/2402.10593/) (2024)
- [Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2411.08918/) (2024)

## Abstract (arXiv)

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.