Low-Altitude Wireless Networks (LAWNs), composed of Unmanned Aerial Vehicles (UAVs) and mobile terminals, are emerging as a critical extension of 6G. However, applying Large Language Models in LAWNs faces three major challenges: 1) Computational and energy constraints; 2) Communication and bandwidth limitations; 3) Real-time and reliability conflicts. To address these challenges, we propose Aerial Agentic AI, a hierarchical framework integrating UAV-side fast-thinking Small Language Model (SLMs) with BS-side slow-thinking Large Language Model (LLMs). First, we design SLM-based Agents capable of on-board perception, short-term memory enhancement, and real-time decision-making on the UAVs. Second, we implement a LLM-based Agent system that leverages long-term memory, global knowledge, and tool orchestration at the Base Station (BS) to perform deep reasoning, knowledge updates, and strategy optimization. Third, we establish an efficient hierarchical coordination mechanism, enabling UAVs to execute high-frequency tasks locally while synchronizing with the BS only when necessary. Experimental results validate the effectiveness of the proposed Aerial Agentic AI.

翻译：低空无线网络由无人机和移动终端组成，正成为6G的关键扩展。然而，在低空无线网络中应用大语言模型面临三大挑战：1) 计算与能效约束；2) 通信与带宽限制；3) 实时性与可靠性冲突。针对这些挑战，我们提出空中代理人工智能——一种分层框架，将无人机端快速思考的小语言模型与基站端慢速思考的大语言模型相整合。首先，我们设计基于小语言模型的代理，使其能够在无人机上实现机载感知、短期记忆增强和实时决策。其次，我们构建基于大语言模型的代理系统，利用长期记忆、全局知识和基站端的工具编排，进行深度推理、知识更新与策略优化。第三，我们建立高效的分层协调机制，使无人机能够本地执行高频任务，仅在必要时与基站同步。实验结果验证了所提出的空中代理人工智能的有效性。