Multi-cloud lakehouse architecture on AWS for Agentic AI, Part 1: Architecture and best practices

This article presents a multi-cloud lakehouse architecture on AWS that unifies metadata catalogs and data access across multiple providers to enable enterprise agentic AI systems with consistent governance and real-time data discovery.

• Agentic AI requires unified, governed, real-time access to enterprise data across multiple platforms and cloud providers
• Architecture combines catalog federation (Iceberg REST API), query federation, and data ingestion patterns to unify metadata
• AWS Interconnect – multicloud provides private, high-speed connectivity to GCP for secure cross-cloud data access
• Apache Iceberg open table format ensures data portability and eliminates vendor lock-in across platforms
• AWS Lake Formation enforces fine-grained access control and governance consistently across all federated data sources
• AI agents access unified catalog through MCP servers and AWS SDK to query data via Amazon Athena or Redshift Spectrum
• Production considerations include managing metadata drift, non-deterministic query latency, and hallucination risks in federated queries

The architecture enables organizations to query and analyze petabytes of data across on-premises, multiple clouds, and SaaS applications without full migration while maintaining centralized governance and audit controls.