Build a semantic layer for agentic AI on AWS with Strategy Mosaic and Amazon Bedrock AgentCore
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.
Proof1 independent outlet
Key points
- This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy.
- 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.
- How to model a C360 domain in Strategy Mosaic across Amazon Redshift and Amazon Aurora using two linked models, with no ETL.
Sources (1)
- [1]Build a semantic layer for agentic AI on AWS with Strategy Mosaic and Amazon Bedrock AgentCoreAWS Machine Learning Blog · Oct 20, 03:36 PM
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.
This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore
- Oct 20, 2026Exa 3.0 Search API Tops Perplexity, Brave and Parallel on 8 Benchmarks
- Oct 19, 2026Developers can now access SageMaker Studio through the new AWS experience
- Oct 19, 2026Making the internet better for AI agents might make it worse for everyone else
- Oct 19, 2026Autonomous intelligence for mining productivity: a multi-agent AI approach to operational value creation
- Oct 18, 2026TinyFish Makes Search Free: What It Really Costs an AI Agent to Read the Web
