Agentic-Defined Networking (ADN): A Vision, Architectural Elements, and Future Directions

Abstract

Network intelligence has largely evolved around logically centralized control and orchestration. Although this model simplifies coordination, it creates a critical dependency on centralized services and limits localized adaptation. This paper presents Agentic-Defined Networking (ADN), an architecture that treats autonomous Artificial Intelligence agents as first-class entities embedded across the network infrastructure. ADN has two defining properties. First, agents perceive local state, maintain beliefs, reason over operator-defined objectives, coordinate with peers, and actuate programmable resources without requiring a persistent central controller in the critical decision path. Second, the mapping between agents and infrastructure is a deployment choice, supporting device-level, cluster-level, and hierarchical configurations. We formalize the ADN agent model and workflow and define an operational framework covering communication, lifecycle management, governance, and security. We evaluate feasibility and scaling through Mininet-AI experiments with topologies of up to 200 switches. The results characterize routing throughput, reasoning latency, fault-mitigation time, and coordination cost under distributed and hierarchical configurations. The study provides initial evidence of feasibility while identifying the challenges that must be addressed before production deployment.