Building a Semantic Layer for Agentic AI on AWS with Denodo and Amazon Bedrock AgentCore
Current overview (not historical)
The article explains how to construct a semantic layer on AWS using the Denodo platform, enabling federated queries across Amazon Aurora, Amazon Redshift, and on‑premises PostgreSQL data. It details the creation of base, derived, and metric views in Denodo Virtual DataPort, and how to expose the semantic layer to AgentCore‑hosted agents via the Model Context Protocol Server. The approach is also illustrated with a similar implementation using Strategy Mosaic, highlighting the flexibility of Denodo in integrating diverse data sources for agentic AI applications.
Written with AI from the reporting · updated 10/20/2026
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New at this step ·Strategy Mosaic Enables Agentic AI on AWS by Providing a Unified Semantic Layer for Bedrock AgentCore
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- AWS Machine Learning Blog · First-partyStrategy Mosaic Enables Agentic AI on AWS by Providing a Unified Semantic Layer for Bedrock AgentCoreOriginal source ↗
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2 developments10/21/2026, 12:34:11 AM
AWS Integrates Denodo Semantic Layer with Bedrock AgentCore for Federated Customer 360 QueriesAWS Machine Learning Blog
10/21/2026, 12:34:11 AM
Strategy Mosaic Enables Agentic AI on AWS by Providing a Unified Semantic Layer for Bedrock AgentCoreAWS Machine Learning Blog
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- 1Oct 21, 00:34single sourcereport-time only
基于Denodo和Amazon Bedrock AgentCore在AWS上构建智能体语义层
Read original source ↗ AWS Machine Learning BlogReplay this moment in the explorer - 2Oct 21, 00:34single sourcereport-time only
在 AWS 上使用 Strategy Mosaic 和 Amazon Bedrock AgentCore 构建 Agen
Read original source ↗ AWS Machine Learning BlogReplay this moment in the explorer
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First-party sources · 2 reports
- AWS Machine Learning Blog · Oct 20, 23:39AWS Integrates Denodo Semantic Layer with Bedrock AgentCore for Federated Customer 360 Queries
AWS demonstrates how to build a semantic layer using Denodo that federates Amazon Aurora, Amazon Redshift, and an on‑premises PostgreSQL database into a single Customer 360 view. The Denodo MCP Server exposes governed views to a Bedrock AgentCore‑hosted agent via the Model Context Protocol, while AgentCore handles authentication and credential management. The setup supports row‑ and column‑level security and can run on Denodo Agora or self‑managed EC2/EKS instances.
Read original source ↗ - AWS Machine Learning Blog · Oct 20, 23:36Strategy Mosaic Enables Agentic AI on AWS by Providing a Unified Semantic Layer for Bedrock AgentCore
Strategy Mosaic is used as a semantic layer on AWS to support agentic AI via Amazon Bedrock AgentCore. The solution models a Customer‑360 domain across Amazon Redshift and Amazon Aurora with two linked models, avoiding ETL. Mosaic’s dimensional model exposes a Model Context Protocol that lets a Strands agent reach the semantic layer, while row‑level and object‑level security propagate end‑to‑end. The same governed definitions can be shared with Power BI, Tableau, notebooks, and custom apps through JDBC/ODBC, XMLA, and REST.
Read original source ↗
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- AWS Machine Learning BlogSelectedAWS Integrates Denodo Semantic Layer with Bedrock AgentCore for Federated Customer 360 Queries
AWS demonstrates how to build a semantic layer using Denodo that federates Amazon Aurora, Amazon Redshift, and an on‑premises PostgreSQL database into a single Customer 360 view. The Denodo MCP Server exposes governed views to a Bedrock AgentCore‑hosted agent via the Model Context Protocol, while AgentCore handles authentication and credential management. The setup supports row‑ and column‑level security and can run on Denodo Agora or self‑managed EC2/EKS instances.
- AWS Machine Learning BlogSelectedStrategy Mosaic Enables Agentic AI on AWS by Providing a Unified Semantic Layer for Bedrock AgentCore
Strategy Mosaic is used as a semantic layer on AWS to support agentic AI via Amazon Bedrock AgentCore. The solution models a Customer‑360 domain across Amazon Redshift and Amazon Aurora with two linked models, avoiding ETL. Mosaic’s dimensional model exposes a Model Context Protocol that lets a Strands agent reach the semantic layer, while row‑level and object‑level security propagate end‑to‑end. The same governed definitions can be shared with Power BI, Tableau, notebooks, and custom apps through JDBC/ODBC, XMLA, and REST.