Task-Agentic Resource Scheduling in ISCC Networks via Multi-Agent LLMs

With the rapid advancement of 6G and intelligent networks, Integrated Sensing, Communication, and Computing (ISCC) has emerged as a key technology to support ubiquitous connectivity and intelligent services. However, many ISCC architectures remain performance-indicator-driven and lack task intent understanding and semantic awareness. As a result, they cannot re-prioritize objectives and constraints or coordinate cross-domain resources in real time, limiting adaptability in dynamic multi-scenario deployments. Recently, Large Language Models (LLMs) have enabled agentic reasoning and planning to interpret task intent and orchestrate cross-domain decisions in intelligent networks. To address this gap, we propose a multi-agent LLM-driven ISCC resource scheduling framework. Multiple LLM agents collaborate across functional layers, forming a perception- cognition-decision-action closed loop that enables cross-domain task decomposition, semantic reasoning, and real-time resource orchestration. This shifts ISCC scheduling from performance-indicator-driven optimization to taskand semantics-driven scheduling. We validate the framework through case studies in representative ISCC scenarios and discuss key challenges and future directions.