提出自生成网络管理架构，让系统能自主编程演化。
From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
- 引入自编程、自反思等能力，实现系统自我演化
- 支持从人工监督到完全自治的渐进式部署
- 针对6G运维难题提供可落地的解决方案
3GPP、ETSI和TM Forum等标准组织正将智能体人工智能作为下一代网络管理的基础，即基于大模型的智能体可自主理解意图、协调资源并在运行时调整行为。然而，要实现6G网络规模下的这一愿景，需具备在运行中生成并演化自动化软件的管理系统。本文提出自生成网络管理（Autogenic network management）参考架构，扩展了智能体能力，加入自编程、自反思、自定向与自架构功能。该架构支持从人工监督的大模型智能体起步，逐步过渡至完全自治。通过采用TM Forum的高优先级运营商用例，验证了该方法对实际运维挑战的解决能力。最后，提出技术路线图，明确未来6G网络实现自生成管理所需的关键突破。
原文摘要 · Abstract (English)
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.
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