A4FN: an Agentic AI Architecture for Autonomous Flying Networks
It addresses the challenge of autonomous network control in infrastructure-limited, mission-critical environments for applications such as disaster response, but it is a position paper with no empirical results, making it incremental in nature.
This paper tackles the problem of automating Flying Networks (FNs) using UAVs for real-time, intent-driven control in scenarios like disaster response, by proposing A4FN, an Agentic AI architecture that leverages Generative AI and LLMs to enable adaptive network reconfiguration and dynamic resource management.
This position paper presents A4FN, an Agentic Artificial Intelligence (AI) architecture for intent-driven automation in Flying Networks (FNs) using Unmanned Aerial Vehicles (UAVs) as access nodes. A4FN leverages Generative AI and Large Language Models (LLMs) to enable real-time, context-aware network control via a distributed agentic system. It comprises two components: the Perception Agent (PA), which semantically interprets multimodal input -- including imagery, audio, and telemetry data -- from UAV-mounted sensors to derive Service Level Specifications (SLSs); and the Decision-and-Action Agent (DAA), which reconfigures the network based on inferred intents. A4FN embodies key properties of Agentic AI, including autonomy, goal-driven reasoning, and continuous perception-action cycles. Designed for mission-critical, infrastructure-limited scenarios such as disaster response, it supports adaptive reconfiguration, dynamic resource management, and interoperability with emerging wireless technologies. The paper details the A4FN architecture, its core innovations, and open research challenges in multi-agent coordination and Agentic AI integration in next-generation FNs.