IntentWeave: Weaving human intents into next-generation cellular networks

The increasing complexity of 6G networks, driven by heterogeneous services and strict performance requirements, has made traditional manual network management difficult to maintain and scale. To address this, Intent-Based Networking (IBN) allows operators to define desired outcomes instead of managing low-level configurations. However, existing IBN systems typically rely on structured inputs, limiting usability for users with limited domain knowledge. The industry is therefore moving toward natural language IBN powered by AI agents. However, significant challenges remain: (i) user intents can span multiple domains, i.e., Radio Access Network (RAN), Core Network (CN), and Mobile Edge Computing (MEC), requiring complex decomposition, and (ii) valid intents may be rejected if the infrastructure cannot meet resource demands. To tackle these issues, we propose IntentWeave, an Agentic AI framework powered by Large Language Models (LLMs) to clarify user intents, decompose them across domains, perform feasibility negotiation, activate and ensure intents. Experiments in real-world scenarios validate its effectiveness in enhancing user experience.