AWS has introduced a modern data mesh strategy to support the development of secure agentic AI applications. This approach addresses the governance challenges of AI agents that autonomously interact with multiple data sources. The architecture enables fine-grained access control across all data interaction layers, ensuring no single point of failure can expose unauthorized data. The solution is designed to meet the needs of production-level agentic AI systems by providing a scalable and secure data foundation. Source: awsml
The architecture extends previous approaches by replacing Amazon OpenSearch Serverless with Amazon S3 Vectors for cost-optimized knowledge bases, which can reduce vector storage and query costs by up to 90% in moderate query-frequency workloads. It also replaces general-purpose Amazon S3 with Amazon S3 Tables, governed by AWS Lake Formation, to deliver up to 10 times higher transactions per second compared to self-managed Iceberg tables. These changes enhance scalability and security while maintaining control over data access. Source: awsml
The RAG architecture enforced governance at a single checkpoint: metadata-filtered vector retrieval. That approach served RAG workloads well. Agentic patterns introduce additional steps, creating a multi-step chain where each step requires its own authorization decision. In RAG, the system queries one pre-built vector index with metadata filters at retrieval time. In agentic AI, the system discovers which tables exist, understands schemas, constructs SQL, retrieves from vector stores, and synthesizes results. A metadata filter at a single retrieval boundary cannot govern this five-step chain. Source: awsml
