Toward Agentic-Integrated Low-Altitude Wireless Networks: From Large AI Models to Autonomous Agents

Abstract

Low-altitude wireless networks (LAWNs) are expected to support mission-critical services in future sixth-generation systems, with tightly integrated communication, sensing, computation, and control. Beyond task-specific intelligence, emerging applications increasingly require autonomous behavior, explicit handling of mission intent, and coordinated decision-making across distributed agents. In this article, we introduce the agentic-integrated LAWNs, where autonomous agents and artificial intelligence (AI) models cooperate to translate mission intent into closed-loop network operation. We first present the architectural foundations of agentic-integrated LAWNs. This architecture consists of coupled aerial and edge segments organized into a three-layer framework, which integrates the basic functional, decision and cognition, and collaboration layers. We then discuss key enabling technologies for functional learning and adaptation, multi-agent coordination, and knowledge-driven enhancement. We further provide a case study on coordinated drone navigation within agentic-integrated LAWNs, highlighting improvements in safety and coordination efficiency. Finally, the article outlines future research directions to advance LAWNs.