Agentic artificial intelligence (AI) presents a promising pathway toward realizing autonomous and self-improving wireless network services. However, resource-constrained, widely distributed, and data-heterogeneous nature of wireless networks poses significant challenges to existing agentic AI that relies on centralized architectures, leading to high communication overhead, privacy risks, and non-independent and identically distributed (non-IID) data. Federated learning (FL) has the potential to improve the overall loop of agentic AI through collaborative local learning and parameter sharing without exchanging raw data. This paper proposes new federated agentic AI approaches for wireless networks. We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop. Moreover, we conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks (LAWNs). Finally, we provide a conclusion and discuss future research directions.

翻译：智能体人工智能为实现自主且自我优化的无线网络服务提供了一条前景广阔的路径。然而，无线网络资源受限、分布广泛且数据异构的特性，对依赖集中式架构的现有智能体人工智能构成了重大挑战，导致高通信开销、隐私风险以及非独立同分布数据问题。联邦学习通过无需交换原始数据的协作式本地学习与参数共享，有望改进智能体人工智能的整体循环。本文提出了面向无线网络的新型联邦智能体人工智能方法。我们首先总结了智能体人工智能的基础与主流联邦学习类型。接着，我们阐述了每种联邦学习类型如何能够强化智能体人工智能循环中的特定环节。此外，我们开展了一项案例研究，探讨如何利用联邦强化学习提升智能体人工智能在低空无线网络中行动决策的性能。最后，我们给出结论并讨论了未来的研究方向。