🤖 AI Summary
This work addresses the limitations of existing 6G network assurance mechanisms, which lack precise understanding and proactive validation of user intent, thereby struggling to guarantee end-to-end SLA compliance. To bridge this gap, the paper proposes the first agent orchestration framework that integrates test-driven development (TDD) principles into 6G assurance. The framework leverages multi-agent collaboration to decompose user intent into auditable specifications and automatically generates verification tests prior to resource allocation, establishing a deterministic mapping from intent to test. Built upon the TM Forum information model, a message-driven architecture, the MCP protocol, and open-source large language models (LLMs), the prototype implementation reveals significant disparities among LLMs in tool-calling reliability and hallucination control, underscoring the critical role of domain knowledge integration in enhancing agent system effectiveness.
📝 Abstract
This work proposes an agentic, intent-driven end-to-end (E2E) orchestration framework that integrates intent co-creation with a Test-Driven Quality Assurance paradigm. In this framework, autonomous agents iteratively refine a user's initial intent into a confirmed, auditable specification. Furthermore, the system automatically derives validation tests from these intents before provisioning, directly mirroring the Test-Driven Development workflow in software engineering to ensure proactive Service Level Agreement (SLA) compliance. The architecture is grounded in a standards-aligned knowledge representation using TM Forum (TMF) information models and catalogs. This enables deterministic graph traversal from high-level Product Offerings down to granular Service/Resource and Test specifications. We prototyped this architecture by extending OpenSlice with a message-driven, multi-agent pattern and integrating MCP-enabled (Model Context Protocol) tool access for real-time knowledge retrieval. Currently, our evaluation of the agents targets the intent co-creation phase as a baseline toward full-scale orchestration. Building on experiments with multiple open-source Large Language Model (LLM) backends integrated with the TMF-based knowledge base, we observe substantial variability in tool-use reliability and hallucination patterns, underscoring the critical importance of robust knowledge integration in agentic 6G systems.