桥接LLM智能体与数据空间：基于模型上下文协议的架构中介方法

中文摘要 AI 辅助

数据空间支持跨组织边界的主权化、受治理的数据共享，但其与AI智能体的集成仍具挑战性，原因在于概率性语言模型交互与策略驱动型数据基础设施之间的不匹配。本文提出一种基于模型上下文协议（MCP）的架构中介方法，通过Eunomia智能体实现，以支持大语言模型（LLM）智能体与数据空间服务之间的受控交互。所提出的中介层将数据空间能力转化为结构化、模式驱动的工具，AI智能体可发现并调用这些工具，同时保持治理约束。原型实现验证了跨目录发现、元数据检索和数据服务调用的端到端交互，且无需修改现有数据空间组件。结果表明，基于协议的中介能够实现AI智能体与数据空间生态系统的互操作、标准对齐的集成。该方法为寻求在受治理的数据共享环境中引入AI驱动自动化，同时保持合规性、互操作性和架构关注点分离的组织提供了实践指导。

英文摘要

Data Spaces enable sovereign and governed data sharing across organizational boundaries, but their integration with AI agents remains challenging due to mismatches between probabilistic language model interactions and policy-driven data infrastructures. This article presents an architectural mediation approach based on the Model Context Protocol (MCP), implemented through the Eunomia Agent, to enable controlled interaction between large language model (LLM) agents and data space services. The proposed mediation layer translates data space capabilities into structured, schema-driven tools that AI agents can discover and invoke while preserving governance constraints. A prototype implementation validates end-to-end interaction across catalog discovery, metadata retrieval, and data service invocation without modifying existing data space components. Results demonstrate that protocol-based mediation enables interoperable and standards-aligned integration of AI agents into data space ecosystems. The approach provides practical guidance for organizations seeking to introduce AI-driven automation into governed data-sharing environments while maintaining compliance, interoperability, and architectural separation of concerns.

发表机构

• Universidad Politécnica de Madrid（马德里理工大学）

机构由 AI 辅助整理，请以论文原文为准。