IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks

TL;DR

IntAgent is an innovative LLM-based agent that leverages NWDAF analytics and tools to autonomously fulfill complex network intents in next-generation networks, enhancing automation and context-awareness.

Contribution

We developed a novel intent agent integrating live NWDAF analytics within a 3GPP-compliant framework, enabling dynamic, autonomous network management.

Findings

Effective in ML-based traffic prediction

Enables scheduled policy enforcement

Demonstrates autonomous intent fulfillment

Abstract

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.