Large Model based Agents for Network Operation and Maintenance
draft-chuyi-nmrg-ai-agent-network-02
| Document | Type |
Expired Internet-Draft
(individual)
Expired & archived
|
|
|---|---|---|---|
| Author | Chuyi Guo | ||
| Last updated | 2026-04-23 (Latest revision 2025-10-20) | ||
| RFC stream | (None) | ||
| Intended RFC status | (None) | ||
| Formats | |||
| Stream | Stream state | (No stream defined) | |
| Consensus boilerplate | Unknown | ||
| RFC Editor Note | (None) | ||
| IESG | IESG state | Expired | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:
Abstract
Current advancements in AI technologies, particularly large models, have demonstrated immense potential in content generation, reasoning, analysis and so on, providing robust technical support for network automation and self-intelligence. However, in practical network operations, challenges such as system isolation and fragmented data lead to extensive manual, repetitive, and inefficient tasks, the improvement of intelligence level is very necessary. This document identifies typical scenarios requiring enhanced intelligence, and explains how AI Agents and large model technologies can empower networks to address operational pain points, reduce manual efforts, and explore impacts on network data, system architectures, and interfaces correspondingly. It further explores and summarizes standardization efforts in implementation.
Authors
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)