Build Semantic Layer for Agentic AI on AWS with Stardog, Bedrock
▶ The 2-minute explainer
Summary
This post demonstrates building a semantic layer on AWS using Stardog with Amazon Aurora and Redshift, and querying it with a Strands Agents agent on Amazon Bedrock AgentCore. This setup enables answering complex customer 360 questions across disparate data sources without ETL.
Why it matters
Professionals can learn to create a unified data view for agentic AI, enabling more intelligent and context-aware applications without complex data integration pipelines.
How to implement this in your domain
- 1Evaluate the need for a semantic layer to unify disparate data sources.
- 2Integrate Stardog's Semantic AI Application with Amazon Aurora and Redshift.
- 3Configure Amazon Bedrock AgentCore to host agentic AI workflows.
- 4Develop Strands Agents to query the semantic layer for specific business questions.
- 5Test and refine the agent's ability to answer complex queries across integrated data.
Who benefits
Key takeaways
- A semantic layer unifies data from various sources for AI applications.
- Stardog and Amazon Bedrock AgentCore can power agentic AI workflows.
- This approach enables complex queries like "customer 360" without ETL.
- AgentCore simplifies hosting and tool access for AI agents on AWS.
Original post by Navin Sharma
"In this post we show how to build a semantic layer on AWS using Stardog’s Semantic AI Application over Amazon Aurora and Amazon Redshift, and how to run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 questions across both sources…"
View on XOriginally posted by Navin Sharma on X · view source
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