Agentic-Defined Networking (ADN): A Vision, Architectural Elements, and Future Directions
TL;DR
The study provides initial evidence of feasibility while identifying the challenges that must be addressed before production deployment and formalize the ADN agent model and workflow and define an operational framework covering communication, lifecycle management, governance, and security.
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.