Intent-based management for next-generation networks: An LLM-centric vision

Intent-Based Networking (IBN) represents a fundamental shift in how networks are designed, operated, and managed, enabling stakeholders to express what the network should achieve rather than how it should be configured. By allowing service owners to specify high-level goals and constraints, IBN lays the foundation for truly autonomous network management. In this direction, standardization efforts such as those led by TM Forum have introduced intent-driven architectures that pave the way toward self-managing networks in the 6G era. Despite this progress, today’s intent frameworks largely rely on declarative formats such as JSON or YAML, which still require detailed knowledge of intent models and northbound interfaces. This creates a cognitive and operational gap between human intent and network behavior, limiting the accessibility and agility promised by autonomous networking. A natural and transformative evolution of IBN is to elevate intent expression to natural language. In this keynote, we present an LLM-centric vision for intent-based management in next-generation networks, where Large Language Models (LLMs) serve as semantic bridges between human objectives and network operations. By translating natural language intents into operational policies and configurations, LLMs can dramatically simplify service deployment and management. We further discuss how few-shot learning and human-in-the-loop feedback enable continuous adaptation, accountability, and trust, positioning LLMs as key enablers of cognitive, goal-driven, and fully autonomous 6G networks.