用大模型让网络自动理解并执行运营商意图，实时调用分析工具
IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks
- 在NWDAF内嵌意图工具引擎，直接调用实时网络分析数据
- 支持机器学习预测流量与定时策略执行，验证自主决策能力
- 适合5G/6G网络自动化运维人员，推动智能网络演进
意图驱动网络（IBNs）作为创新技术，通过高层请求语句实现网络操作自动化，定义网络应达成的目标。本文提出IntAgent，一个集成NWDAF分析与工具的智能意图大模型代理，能够响应网络运营商的意图。不同于以往方法，我们在NWDAF分析引擎中直接构建意图工具引擎，使代理可利用实时网络分析数据辅助推理与工具选择。我们提供一个增强的、符合3GPP标准的数据源，提升对网络运营目标的动态上下文感知能力，并构建MCP工具服务器以支持调度、监控与分析工具。通过两个实际用例——基于机器学习的流量预测和定时策略执行——验证了该框架的有效性，证明IntAgent具备自主完成复杂网络意图的能力。
原文摘要 · Abstract (English)
Intent-based networks (IBNs) are gaining prominence as an innovative technology that automates network operations through high-level request statements, defining what the network should achieve. In this work, we introduce IntAgent, an intelligent intent LLM agent that integrates NWDAF analytics and tools to fulfill the network operator's intents. Unlike previous approaches, we develop an intent tools engine directly within the NWDAF analytics engine, allowing our agent to utilize live network analytics to inform its reasoning and tool selection. We offer an enriched, 3GPP-compliant data source that enhances the dynamic, context-aware fulfillment of network operator goals, along with an MCP tools server for scheduling, monitoring, and analytics tools. We demonstrate the efficacy of our framework through two practical use cases: ML-based traffic prediction and scheduled policy enforcement, which validate IntAgent's ability to autonomously fulfill complex network intents.
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