Build a semantic layer for agentic AI on AWS with Stardog and Amazon Bedrock AgentCore
AWS and Stardog demonstrate a new semantic layer for agentic AI that unifies data from Aurora and Redshift without ETL, running on Bedrock AgentCore.
- Stardog’s semantic layer enables agentic AI to query Aurora and Redshift databases without ETL, improving real-time data access.
- Amazon Bedrock AgentCore simplifies agent deployment by managing authentication, hosting, and tool credentials in one service.
- The integration supports multiple AWS compute options, including EKS, ECS, and Lambda, for flexible deployment.
- Customer 360 use cases benefit from unified semantic queries across transactional and analytical data sources.
AWS and Stardog have published a technical guide showing how to build a semantic layer for agentic AI on AWS. The solution uses Stardog’s Semantic AI Application to connect Amazon Aurora and Amazon Redshift databases, enabling agents to query customer 360 data without extract, transform, and load (ETL) processes. The semantic layer acts as a unified interface, translating natural language queries into structured database operations.
The deployment leverages Amazon Bedrock AgentCore, a managed service that bundles authentication, hosting, and tool credentials. This setup allows agents to run on AWS compute services like Amazon Elastic Kubernetes Service (EKS), Amazon Elastic Container Service (ECS), and AWS Lambda. The integration aims to simplify agentic AI development by reducing data integration complexity and improving query accuracy across disparate data sources.
The guide highlights practical use cases such as customer analytics, where agents can answer complex questions by combining data from transactional and analytical databases in real time. This approach eliminates the need for traditional ETL pipelines, reducing latency and operational overhead while maintaining data consistency.
Provides a streamlined way to build agentic AI systems that query multiple databases without complex ETL pipelines.
Reduces data integration costs and latency while enabling real-time customer analytics and decision-making.
Demonstrates how semantic AI layers can simplify agentic workflows on major cloud platforms.
- Semantic layer
- A data abstraction that translates natural language queries into structured database operations, enabling unified access to disparate data sources.
- Agentic AI
- AI systems capable of autonomous decision-making and task execution, often using agents that interact with data and tools.
- ETL
- Extract, Transform, Load: a process for moving and transforming data between systems, often used in data integration.
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