自进化智能体让无线网络自主优化，无需人工干预。
From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions
- 构建多智能体协同框架，通过角色分工与监督机制实现自主演进。
- 在低空无线网络中将固定天线升级为可移动天线，性能提升最高达52.02%。
- 适合研究下一代自主无线系统、智能体协作与自适应学习的学者。
自进化智能体人工智能为未来无线系统提供了新范式，使智能体能持续自主适应和改进而无需人工干预。与静态AI模型不同，自进化智能体嵌入自主演化循环，可响应环境动态更新模型、工具和工作流。本文全面概述了自进化智能体AI的分层架构、生命周期及关键技术，包括工具智能、工作流优化、自我反思与演化学习。我们进一步提出一种多智能体协作的自进化智能体框架，多个大语言模型（LLMs）在协调代理的指令下分配角色专用提示，通过结构化对话、迭代反馈和系统性验证，实现全生命周期自主执行。以低空无线网络（LAWNs）中的天线演化为例，实验证明该框架可自主将固定天线优化升级为可移动天线优化，性能提升最高达52.02%，显著优于固定基线，且几乎无需人工干预，验证了其在下一代无线智能中的适应性与鲁棒性。
原文摘要 · Abstract (English)
Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems by enabling autonomous agents to continually adapt and improve without human intervention. Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and workflows in response to environmental dynamics. This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques, including tool intelligence, workflow optimization, self-reflection, and evolutionary learning. We further propose a multi-agent cooperative self-evolving agentic AI framework, where multiple large language models (LLMs) are assigned role-specialized prompts under the coordination of a supervisor agent. Through structured dialogue, iterative feedback, and systematic validation, the system autonomously executes the entire life cycle without human intervention. A case study on antenna evolution in low-altitude wireless networks (LAWNs) demonstrates how the framework autonomously upgrades fixed antenna optimization into movable antenna optimization. Experimental results show that the proposed self-evolving agentic AI autonomously improves beam gain and restores degraded performance by up to 52.02%, consistently surpassing the fixed baseline with little to no human intervention and validating its adaptability and robustness for next-generation wireless intelligence.
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