🤖 AI Summary
This study addresses the fundamental mismatch between the probabilistic interactions of large language model agents and the policy-driven architecture of data spaces, which impedes their seamless integration. To overcome this challenge, we propose Eunomia, a mediation layer architecture built upon the Model Context Protocol (MCP). Without requiring modifications to existing components, this approach translates data space capabilities into schema-driven, structured tools, thereby enabling controlled interaction and standardized interoperability between AI agents and data spaces. A prototype implementation validates the end-to-end workflow from catalog discovery to service invocation. The results demonstrate that this architectural mediation effectively reconciles compliance, interoperability, and decoupling while strictly preserving governance constraints.
📝 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.