Agentic AI Empowered Intent-Based Networking for 6G
This work addresses the challenge in existing intent-based networking (IBN) approaches of simultaneously achieving flexibility and interpretability in natural language understanding while strictly enforcing technical constraints—a key bottleneck for 6G autonomous orchestration. To bridge this gap, the authors propose a hierarchical multi-agent framework that integrates large language models (LLMs) with domain-expert agents. Leveraging the ReAct reasoning-action loop, the system collaboratively decomposes high-level natural language intents into network slice configurations compliant with RAN and core network constraints. This architecture represents the first approach to enable interpretable, constraint-aware, and iteratively reasoned automatic translation from intent to configuration. Experimental results demonstrate significant performance gains over rule-based systems and direct LLM prompting across diverse benchmark scenarios, validating its effectiveness in O-RAN deployments and highlighting the critical role of context-aware prompt engineering in network automation.