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

Jul 16, 2026

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Standards bodies such as TM Forum, 3GPP, and ETSI are aligning on agentic AI as the basis for next-generation network management, where large-model agents autonomously interpret intent, coordinate resources, and adjust runtime operations. Scaling this vision to complex 6G requires management systems that can create and evolve their own automation software during operation. The work proposes autogenic network management, a reference architecture adding self-programming, self-reflection, self-orientation, and self-architecting. It supports staged rollout from human-supervised agents to autonomous operation and validates concepts via TM Forum operator scenarios, concluding with a research roadmap for future 6G.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

Introduction

How Do AI-Native Networks “Think”?

From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective Petar Djukic, Sudipta Acharya, Takai Eddine Kennouche, and Burak Kantarci arXiv :2607 .06786v 1 [ cs .NI ] 7 Jul 2026 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, selfreflection, 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. Index Terms—Agentic AI, Autogenic network management, AI-native 6G, Self-evolving networks, Code generation, Architectural evolution I. INTRODUCTION THE next generation of 6G promises to be AI-native, embedding machine learning (ML) across every layer, function, and interface [1] . While this vision offers unprecedented capabilities, from semantic communications to intentdriven operations, it also introduces operational complexity that exceeds current network management approaches. As ML software proliferates throughout network functions (NFs), maintaining performance at scale while managing interdependencies becomes a critical challenge. This paper argues that achieving autonomous AI-native operations at scale requires autonomous network management where the management plane can generate new automation software, validate its correctness, and modify its own operational structure during runtime. We term this form of autonomy“autogenic” and present autogenic network management asan architectural approach for realizing Agentic AI in 6G networks, where LAM-based agents autonomously make decisions, generate solutions, and evolve system behavior in response to operational requirements. In this article, we present the architectural foundations, design principles, and a research roadmap for autogenic network P. Djukic is with Bell Labs Research, 600 March Rd, Kanata, ON K2K 2E6. (e-mail: [petar.djukic@nokia-bell-labs.com](petar.djukic@nokia-bell-labs.com)). T. Kennouche is with Nokia Technology Standards, Nokia France, 12 rue Jean Bart Massy 91300. (e-mail: [takai.kennouche@nokia.com](takai.kennouche@nokia.com)) S. Acharya and B. Kantarci are with University of Ottawa, 350 Legget Drive, Kanata, ON, Canada, K2K 2W7 (email:{sacharya2,[burak.kantarci](burak.kantarci}@uottawa.ca)[}](burak.kantarci}@uottawa.ca)[@uottawa.ca](burak.kantarci}@uottawa.ca)) management systems. Our work directly addresses the architectural challenges identified by standards bodies for agentbased network management, providing a reference architecture compatible with TM Forum’s autonomous network framework and ETSI’s experiential networked intelligence vision. A. How Do AI-Native Networks “Think”? In a future 6G system, artificial intelligence is expected to be pervasive. ML models, adaptive controllers, and datadriven optimization routines will operate across all layers and interfaces. The air interface will incorporate AI-native mechanisms for detection, scheduling, and other core Radio Access Network (RAN) operations. LAMs will coordinate functions across the network [1], [

FAQs

What limitation in current network management makes AI-native 6G operations difficult at scale?

AI-native 6G introduces complex interdependencies because ML software will run across functions and layers. Managing performance and interactions using current approaches becomes impractical at scale.

What is autogenic network management and how does it extend agentic AI?

Autogenic network management is an architectural approach that extends agentic capabilities with self-programming, self-reflection, self-orienting, and self-architecting so the management plane can generate, validate, and modify automation software during runtime.

How does the document propose operational deployment progressing toward autonomy?

It supports staged deployment starting with human-supervised LAM-based agents and advancing toward autonomous operation as confidence builds.

Jul 16, 2026

Upload by Skyler