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

• URL: http://arxiv.org/abs/2607.06786v1
• Date: Tue, 07 Jul 2026 20:34:05 GMT
• Title: From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
• Abstract summary: We introduce Autogenic network management, a reference architecture that extends agentic capabilities with self-programming, self reflection, self-orienting, and self-architecting capabilities.<n>We demonstrate the approach through high-priority operator scenarios drawn from TM Forum's autonomous network use cases.<n>We conclude with a research roadmap outlining the technical advances needed to make autogenic network management realistic in future 6G networks.
• Score: 5.637124580372625
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• AgentxGCore: Agentic AI for Next-Generation Mobile Core Network [0.0]
  Next Generation Mobile Networks (NextG) will adopt an AI-native architecture on the Core Network (CN)<n>New era in network orchestration and management begins, leveraging and empowering the Intent-based Networking (IBN) paradigm.<n>Proposal establishes an AI-driven closed-loop for continuous optimization based on real-time information.
  arXiv  Detail & Related papers  (2026-05-29T23:13:46Z)
• Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey [64.20429885919701]
  We propose an energy accounting framework identifying computational and communication costs across the Perception-Reasoning-Action cycle.<n>We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware-aware inference.<n>We identify open challenges of federated green learning, carbon-aware agency, 6th generation mobile communication (6G)-native Agentic AI, and self-sustaining systems.
  arXiv  Detail & Related papers  (2026-04-09T06:13:59Z)
• Communications-Incentivized Collaborative Reasoning in NetGPT through Agentic Reinforcement Learning [12.904732640630014]
  We propose a unified agentic NetGPT framework for AI-native xG networks.<n>A NetGPT core can either perform autonomous reasoning or delegate sub-tasks to domain-specialized agents via agentic communication.<n>The framework establishes clear responsibilities and interoperable, enabling scalable, distributed intelligence across the network.
  arXiv  Detail & Related papers  (2026-01-31T15:07:11Z)
• Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
  6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-12-04T03:09:33Z)
• Agentic AI for Mobile Network RAN Management and Optimization [0.0]
  Agentic AI represents a new paradigm for automating complex systems by using Large AI Models (LAMs)<n>This paper contributes to ongoing research on Agentic AI in 5G and 6G networks by tracing its evolution from classical agents to Agentic AI.<n>Core design patterns-reflection, planning, tool use, and multi-agent collaboration-are then described to illustrate how intelligent behaviors are orchestrated.
  arXiv  Detail & Related papers  (2025-11-04T12:34:57Z)
• From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
  Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
  arXiv  Detail & Related papers  (2025-10-07T05:45:25Z)
• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [68.9543153075464]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach [35.05793485239977]
  We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents.<n>We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet.
  arXiv  Detail & Related papers  (2025-03-20T00:48:44Z)
• Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning [55.641299901038316]
  AI-generated content can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content for resource-constrained users.<n>Such a paradigm faces two significant challenges: 1) raw prompts often lead to poor generation quality due to users' lack of experience with specific AIGC models, and 2) static service provisioning fails to efficiently utilize computational and communication resources.<n>We develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation.
  arXiv  Detail & Related papers  (2025-02-17T03:05:20Z)
• Closing the Responsibility Gap in AI-based Network Management: An Intelligent Audit System Approach [0.0]
  Existing network paradigms have achieved lower downtime as well as a higher Quality of Experience (QoE) through the use of Artificial Intelligence (AI)-based network management tools.<n>These AI management tools, allow for automatic responses to changes in network conditions, lowering operation costs for operators, and improving overall performance.<n>While adopting AI-based management tools enhance the overall network performance, it also introduce challenges such as removing human supervision, privacy violations, algorithmic bias, and model inaccuracies.
  arXiv  Detail & Related papers  (2025-02-08T15:30:25Z)
• Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities [148.601430677814]
  This paper presents a comprehensive overview of AI and communication for 6G networks.<n>We first review the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G.<n>The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks.
  arXiv  Detail & Related papers  (2024-12-19T05:36:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.