Build a data foundation for agentic AI on AWS: From lakehouse to MCP

This article outlines a four-pillar framework for building a data foundation on AWS that supports agentic AI, addressing fragmentation, unstructured data accessibility, system interoperability, and governance challenges.

• Pillar 1: Build a unified lakehouse using Amazon SageMaker Lakehouse with S3 Tables and Apache Iceberg for zero-ETL ingestion across operational, analytical, and AI data
• Pillar 2: Implement context engineering with knowledge bases, session memory, and data tools to provide agents curated, relevant context within token limits
• Pillar 3: Enable multi-agent collaboration using Agent2Agent (A2A) protocols and Model Context Protocol (MCP) via Amazon Bedrock AgentCore Gateway for secure tool access
• Pillar 4: Establish governance through Amazon SageMaker Data Catalog with discovery, publish-subscribe access controls, and lineage tracking for trust and compliance
• Sequence implementation: start with lakehouse and zero-ETL, then context engineering, then multi-agent orchestration, with governance as a parallel track from day one

This integrated framework addresses why 40% of agentic AI projects fail by providing a governed, consistent data foundation that prevents hallucinations and enables agents to communicate and act reliably at enterprise scale.