From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective

2026-07-07

AI Summary

This work addresses the scalability and complexity challenges in 6G network operations, where existing Agentic AI falls short of achieving full autonomy due to its inability to self-generate and evolve software at runtime. To bridge this gap, the paper introduces the Autogenic Network Management architecture—the first to bring the autogenic paradigm into networking—leveraging four core capabilities: self-programming, self-reflection, self-direction, and self-architecting. These enable a progressive evolution from human-supervised LAM (Language Agent Model) agents toward fully autonomous operations. The proposed architecture integrates autonomous use cases from established frameworks such as TM Forum and demonstrates effectiveness in high-priority operator scenarios, thereby delineating key technical pathways and phased deployment strategies essential for realizing autonomous 6G network management.

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.

Problem

• Autogenic network management
• 6G networks
• self-programming
• autonomous network
• Large AI Model

Innovation

• Autogenic network management
• Self-programming
• Large AI Model (LAM)
• Autonomous network
• 6G