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.

翻译：意图驱动网络作为一种创新技术，正日益受到重视，其通过高层级的请求声明自动执行网络运维，定义网络应达成的目标。本文提出IntAgent——一种集成NWDAF分析与工具的智能意图LLM智能体，用于实现网络运营商的意图。与既有方法不同，我们直接在NWDAF分析引擎内部构建意图工具引擎，使智能体能利用实时网络分析数据来指导其推理与工具选择。我们提供一套符合3GPP标准的增强型数据源，以提升网络运营商目标的动态情境感知实现能力，并配备MCP工具服务器用于调度、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与计划性策略实施）验证了本框架的有效性，证明IntAgent能够自主实现复杂的网络意图。