Agentic xApp and Programmable Network API Framework for Autonomous Enterprise 6G Platform

Abstract

Enterprise 6G platforms require control systems which possess the ability to learn through programmed instructions for handling various radio edge networking and transport system alterations that occur during changing service demands. The combination of SDN/NFV systems with conventional rule-based orchestration methods does not support real-time system modifications which need distributed processing power to maintain ultra-reliable operation and low-latency performance in enterprise environments. The research paper presents an agentic xApp framework which operates through a programmable network API system to achieve autonomous operation in enterprise 6G environments. The system uses learning-based agents through RAN Intelligent Controller (RIC) xApps to create a closed-loop system which controls spectrum resources and workload distribution and policy implementation across different vendor networks. A declarative network-as-code interface translates high-level enterprise intents into optimized control actions through a constrained multi-objective optimization model which evaluates latency and energy efficiency and SLA compliance as unified performance criteria. Federated orchestration mechanisms enable distributed domain coordination with preserved system scalability and maintained operational independence for each local domain. The system performance evaluation shows improved SLA results and decreased setup requirements and shorter control-loop response times during dynamic enterprise workload testing compared to traditional centralized methods. The proposed framework advances the transition from programmable automation toward fully autonomous, AI-native enterprise 6G network platforms.