From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
TL;DR
This work introduces Autogenic network management, a reference architecture that extends agentic capabilities with self-programming, self reflection, self reflection, self-orienting, and self-architecting capabilities, and concludes with a research roadmap outlining the technical advances needed to make autogenic network management realistic in future 6G networks.
Abstract
Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime. However, achieving this vision at the scale and complexity of 6G networks requires management systems that can generate and evolve their own automation software during operation. We introduce Autogenic network management, a reference architecture that extends agentic capabilities with self-programming, self reflection, self-orienting, and self-architecting capabilities. The architecture supports practical staged deployment beginning with human-supervised LAM-based agents and progressing toward autonomous operation as confidence builds. We demonstrate the approach through high-priority operator scenarios drawn from TM Forum's autonomous network use cases, showing how autogenic management addresses real operational challenges. We conclude with a research roadmap outlining the technical advances needed to make autogenic network management realistic in future 6G networks.