Lingxiao Sun, Zhaoyang Zhang, Zihan Lin et al.
Future sixth-generation (6G) networks are expected to support low-altitude wireless networks (LAWNs), where unmanned aerial vehicles (UAVs) and aerial robots operate in highly dynamic three-dimensional environments under stringent latency, reliability, and autonomy requirements. In such scenarios, autonomous task execution at the network edge demands holistic coordination among sensing, communication, computing, and control (SC3) processes. Agentic Artificially Intelligent Radio Access Networks (Agentic AI-RAN) offer a promising paradigm by enabling the edge network to function as an autonomous decision-making entity for low-altitude agents with limited onboard resources. In this article, we propose a task-oriented Agentic AI-RAN architecture that enables SC3 task execution within a single edge node. The proposed architecture addresses the challenge of coordinating heterogeneous workloads in resource-constrained edge environments. To validate this framework, we prototype a representative low-altitude UAV system on a general-purpose Graphics Processing Unit (GPU) platform and evaluate it through an autonomous drone-navigation case study. The current prototype instantiates the platform-agnostic design through Multi-Instance GPU (MIG) partitioning and containerized deployment, providing physical resource isolation and coordinated execution between real-time communication and multimodal inference. Experimental results demonstrate low closed-loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, highlighting the feasibility of the proposed framework for mission-critical low-altitude wireless networks in 6G.