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Build a semantic layer for agentic AI on AWS with Strategy Mosaic and Amazon Bedrock AgentCore

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Build a semantic layer for agentic AI on AWS with Strategy Mosaic and Amazon Bedrock AgentCore

This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy.

In the first post in this series, we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift and ran a Strands agent on Amazon Bedrock AgentCore against that layer. The second post kept the same customer 360 (C360) pattern and swapped in Denodo’s data virtualization platform, extending the source mix to include an on-premises PostgreSQL cluster. This post uses the same C360 pattern again, with Strategy Mosaic as the semantic layer.

Stardog’s semantic layer is ontology-based: concepts and relationships modeled as RDF, with IRIs as the unit of federation and SPARQL as the query language. Denodo’s is data virtualization: base and derived views composed in VQL. Mosaic’s is dimensional: attributes, hierarchies, facts, and metrics composed through Strategy’s metric expression syntax, with linked models as the unit of federation. That model serves four native protocols from one definition: Model Context Protocol (MCP) for AI agents, JDBC/ODBC over Trino for SQL clients, XMLA for Power BI and Excel, and REST for custom applications. Behind those protocols is a long list of certified data sources . Supported systems include cloud warehouses such as Snowflake and Databricks. Operational databases include Amazon Relational Database Service (Amazon RDS) , Oracle, and SQL Server. Analytical engines include Amazon Athena , Trino, and SAP HANA, and software as a service (SaaS) sources include Salesforce. The linked-model pattern extends beyond the Amazon Redshift and Amazon Aurora pair this walkthrough uses.


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