摘要：数据空间支持跨组织边界的自主且受治理的数据共享，但由于概率性语言模型交互与策略驱动的数据基础设施之间存在不匹配，将其与 AI Agent 集成仍面临挑战。本文提出一种基于 Model Context Protocol（MCP）的架构中介方法，并通过 Eunomia Agent 实现，以支持大语言模型（LLM）Agent 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换为由结构化模式驱动的工具，AI Agent 可以发现并调用这些工具，同时保留治理约束。原型实现验证了贯穿目录发现、元数据检索及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，基于协议的中介机制能够实现 AI Agent 向数据空间生态系统的可互操作、符合标准的集成。该方法为寻求在受治理数据共享环境中引入 AI 驱动自动化的组织提供了实践指引，同时维持合规性、互操作性以及架构职责分离。

Abstract: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.