Build a semantic layer for agentic AI on AWS with Stardog and Amazon Bedrock AgentCore
AWS Machine Learning Navin Sharma
AWS and Stardog demonstrated building a semantic layer for agentic AI by connecting Amazon Aurora and Amazon Redshift through a federated knowledge graph that enables agents running on Amazon Bedrock AgentCore to answer cross-database questions without ETL pipelines. The solution uses an ontology-driven knowledge graph with virtual graphs mapping to live data sources, allowing the foundation model to compose answers across fragmented enterprise data while maintaining business logic rules and access controls. By separating the model layer, meaning layer, and agent runtime layer, organizations can enable AI agents to reason over enterprise data with the same fluency as senior analysts without duplicating business definitions across multiple systems.
Why it matters
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 without extract, transform, and load (ETL). The same Stardog deployment works behind AWS computes (Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS), and AWS Lambda). We use AgentCore here because it bundles inbound auth, hosting, and tool credentials into one managed service.
