From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
arXiv:2607.06786v1 Announce Type: cross 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 capa
The accelerating complexity and scale of emerging 6G networks necessitate more autonomous and self-evolving management systems that traditional agentic AI approaches may soon not sufficiently address.
This concept introduces a paradigm shift in network management, moving beyond pre-programmed automation to systems capable of generating and evolving their own operational software, critical for future infrastructure.
Network management shifts from a human-supervised agent-based system to one where the network can autonomously adapt and write its own automation code, increasing resilience and reducing human intervention.
- · Telecommunications companies
- · AI software developers
- · Network equipment manufacturers
- · Traditional network operators
- · Manual IT professionals
Next-generation 6G networks will be managed by highly autonomous, self-evolving AI systems, reducing operational overhead.
This deep integration of AI in critical infrastructure could lead to new cybersecurity vulnerabilities or dependencies on AI ethics frameworks to ensure operational integrity.
The development of 'autogenic systems' able to write and evolve their own code could provide a blueprint for similar self-managing AI in other complex domains, accelerating the 'AI Agents' narrative.
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Read at arXiv cs.AI