Intent-Based Infrastructure and Service Orchestration Using Agentic-AI

This paper introduces a novel framework that integrates agentic Artificial Intelligence (AI) with Intent-Based Networks (IBN) to enable autonomous management, configuration, and optimization of mobile network services and resources. Leveraging the advanced reasoning and natural language processing capabilities of an Large Language Model (LLM), the proposed architecture translates high-level user intents into precise network actions, facilitating user-friendly and scalable network orchestration. The framework employs a distributed multi-agent system, where specialized agents collaborate to decompose user intents, provide computational infrastructure, and deploy services using industry-standard Infrastructure-as-Code (IaC) tools. By supporting natural language interactions, the system reduces operational complexity and enhances accessibility for users with varying technical expertise. Experimental evaluations demonstrate significant improvements in task completion rates, response accuracy, and operational efficiency compared to traditional manual methods, particularly for complex network management tasks. In essence, this work creates an intelligent network orchestration framework that adapts to user needs by automatically configuring network and computing resources while operating with minimal human intervention.