Emerging 6G networks rely on complex cross-layer optimization, yet manually translating high-level intents into mathematical formulations remains a bottleneck. While Large Language Models (LLMs) offer promise, monolithic approaches often lack sufficient domain grounding, constraint awareness, and verification capabilities. To address this, we present ComAgent, a multi-LLM agentic AI framework. ComAgent employs a closed-loop Perception-Planning-Action-Reflection cycle, coordinating specialized agents for literature search, coding, and scoring to autonomously generate solver-ready formulations and reproducible simulations. By iteratively decomposing problems and self-correcting errors, the framework effectively bridges the gap between user intent and execution. Evaluations demonstrate that ComAgent achieves expert-comparable performance in complex beamforming optimization and outperforms monolithic LLMs across diverse wireless tasks, highlighting its potential for automating design in emerging wireless networks.
@article{arxiv.2601.19607,
title = {ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks},
author = {Haoyun Li and Ming Xiao and Kezhi Wang and Robert Schober and Dong In Kim and Yong Liang Guan},
journal= {arXiv preprint arXiv:2601.19607},
year = {2026}
}