When a customer service agent autonomously queries order databases, retrieves return policies, and synthesizes answers, it needs governed access to multiple data sources across your organization. Building agentic AI applications on a modern data mesh requires fine-grained access control enforced at every layer of the data interaction chain. AI agents that autonomously discover database schemas, construct SQL queries, and synthesize data from multiple sources expose governance gaps that the single-checkpoint model built for Retrieval Augmented Generation (RAG) can’t address. Organizations need controls from tool discovery through query execution to response synthesis.

In an earlier post, Build secure RAG applications with AWS serverless data lakes, we showed how to enforce fine-grained access control (FGAC) over RAG by filtering vector search results using metadata such as business domain and security classification. That approach worked because RAG’s data interaction was simple: retrieve chunks from a pre-built vector index, filter by metadata, and present results.

This post shows how to build a governed, serverless data mesh on AWS that provides the secure, scalable data foundation production agentic AI requires. The architecture extends the original with three key changes: