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.

翻译：新兴的6G网络依赖于复杂的跨层优化，然而将高层意图手动转化为数学表述仍然是一个瓶颈。尽管大语言模型（LLM）展现出潜力，但单体式方法通常缺乏足够的领域基础、约束感知和验证能力。为解决这一问题，我们提出了ComAgent，一个基于多LLM的智能体人工智能框架。ComAgent采用一个闭环的感知-规划-行动-反思循环，协调文献检索、编码和评分等专门智能体，以自主生成可供求解器直接使用的数学表述和可复现的仿真。通过迭代分解问题和自我纠正错误，该框架有效弥合了用户意图与执行之间的差距。评估表明，在复杂的波束成形优化任务中，ComAgent实现了与专家相当的性能，并在多种无线任务中超越了单体式LLM，凸显了其在未来无线网络自动化设计中的潜力。