🤖 AI Summary
This work addresses the challenge of accurately interpreting ambiguous or incomplete user intents in highly complex 6G networks, where existing intent-driven systems often fall short. To bridge this gap, the paper proposes an agent-based end-to-end orchestration framework that leverages domain-expert agents collaborating with the TM Forum knowledge ecosystem to iteratively refine high-level intents into precise, machine-readable service directives. The approach innovatively decouples cognition from execution and introduces a dual-layer memory mechanism to ensure consistency across multi-turn interactions. Furthermore, the authors develop the first open-source large language model evaluation prototype tailored for 6G Network-as-a-Service (NaaS). Experimental results demonstrate that while current open-source models exhibit strong compliance in instruction following, they still show a significant deficiency in accurately and hallucination-free mapping of fine-grained intents into valid, catalog-supported service orders.
📝 Abstract
6G network complexity necessitates high levels of autonomy, yet current intent-based systems struggle with ambiguous or incomplete human requests. This paper introduces an agent-based, intent-driven end-to-end (E2E) orchestration framework designed for Network-as-a-Service (NaaS) delivery through collaborative intent co-creation. The proposed system leverages a pool of Domain Expert Agents and a TM Forum-aligned Body-of-Knowledge (BoK) to iteratively refine user requests into deterministic, machine-readable actions. A fundamental design principle is the decoupling of cognition and actuation, where AI-driven reasoning is isolated from standardized execution controllers to ensure safety and operational trust. The framework includes a dual-layer memory system to maintain coherence during multi-step collaborations. The presented prototype, built on ETSI OpenSlice and the Model Context Protocol (MCP), evaluates across several open-source Large Language Models (LLMs). While these models demonstrate high instruction compliance, results reveal a significant gap in translating high-resolution intents into valid, catalog-backed orders without hallucinations.