The Data Infrastructure Agentic AI Needs

Many enterprise agentic AI initiatives fail to reach production not because of model limitations but because agents lack secure, governed access to fragmented live business data and the context needed to interpret it correctly. The proposed solution is a data architecture combining federated access, which lets agents query warehouses, lakes, and SaaS systems in place rather than requiring broad data centralization; a context layer that supplies definitions, ownership, freshness, business logic, and cross-source meaning; and governance embedded directly in the access layer through read-only controls, RBAC/ABAC, authentication, and audit trails. The piece argues that standards such as the Model Context Protocol can standardize connections between agents and data platforms, although effective security depends on each implementation. It presents Starburst’s federated query platform, managed MCP server, and AIDA analytics agent as examples of this approach, positioning selective centralization as an option only when performance or compliance needs justify copying data.

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