Agentic AI-Enhanced Semantic Communications: Foundations, Architecture, and Applications

TL;DR

This paper explores how integrating agentic AI into semantic communications enhances network intelligence, proposing a unified framework, analyzing various agent types, and demonstrating improved information reconstruction through a case study.

Contribution

It introduces a comprehensive framework for agentic AI-enhanced SemCom, covering architecture, applications, and a novel knowledge base-based coding scheme with experimental validation.

Findings

AKB-JSCC achieves higher reconstruction quality

Unified framework covers multiple network layers

Demonstrates benefits of agentic AI in SemCom applications

Abstract

Semantic communications (SemCom), as one of the key technologies for 6G, is shifting networks from bit transmission to semantic information exchange. On this basis, introducing agentic artificial intelligence (AI) with perception, memory, reasoning, and action capabilities provides a practicable path to intelligent communications. This paper provides a systematic exposition of how agentic AI empowers SemCom from the perspectives of research foundations, system architecture, and application scenarios. We first provide a comprehensive review of existing studies by agent types, covering embedded agents, large language model (LLM)/large vision model (LVM) agents, and reinforcement learning (RL) agents. Additionally, we propose a unified agentic AI-enhanced SemCom framework covering the application layer, the semantic layer, and the cloud-edge collaboration layer, forming a closed loop from intent to encoding to transmission to decoding to action to evaluation. We also present several typical scenarios, including multi-vehicle collaborative perception, multi-robot cooperative rescue, and agentic operations for intellicise (intelligent and concise) networks. Furthermore, we introduce an agentic knowledge base (KB)-based joint source-channel coding case study, AKB-JSCC, where the source KB and channel KB are built by LLM/LVM agents and RL agents, respectively. Experimental results show that AKB-JSCC achieves higher information reconstruction quality under different channel conditions. Finally, we discuss future evolution and research directions, providing a reference for portable, verifiable, and controllable research and deployment of agentic SemCom.