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
The rapid advancement in AI agent capabilities is demanding more sophisticated and real-time data access solutions, moving beyond traditional ETL for complex query needs.
This development allows for more autonomous and intelligent AI agents to operate directly on diverse enterprise data layers, enhancing real-time decision-making and operational efficiency.
Enterprises can now deploy AI agents that seamlessly query and integrate data from various sources without manual ETL processes, enabling new levels of automation for customer 360 insights.
- · AWS
- · Stardog
- · Enterprises adopting AI agents
- · Semantic layer technologies
- · Traditional ETL vendors
- · Data silos
- · Manual data integration roles
AI agents become significantly more powerful and pervasive within enterprise data environments due to enhanced data access.
Reduced operational overhead for data integration accelerates the development and deployment cycles of AI-powered applications.
The integration of diverse data sources via semantic layers could lead to the emergence of truly generalized enterprise AI agents capable of understanding and acting across an entire business.
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Read at AWS Machine Learning Blog