Legacy system data models can interoperate only if their syntactic and semantic differences are resolved. To address this problem, we developed the Intelligent Mapping Toolkit (IMT), which enables mixed-initiative mapping of metadata and instances between relational data models. IMT employs a distributed multi-agent architecture so that, unlike many other efforts, it can perform mapping tasks that involve thousands of schema elements. This architecture includes a novel federation of matching agents that leverage case-based reasoning methods. As part of our predeployment evaluation for USTRANSCOM and other DoD agencies, we evaluated IMT’s mapping performance and scalability. We show that combinations of its matching agents are more effective than any that operate independently, and that they scale to realistic problems.