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.

Enterprise analytics has been chasing the same goal for two decades: shrink the time between a business question and a trustworthy answer. Scheduled reports gave way to dashboards, then dashboards gave way to self-service business intelligence (BI). Even self-service depended on a data engineer having already built the right model for the right question, and the human analyst remained the bottleneck for everything outside the prepared dataset. Generative AI agents are the next step. Instead of visualizing data, they reason over it. They plan, write queries, evaluate results, refine, and iterate against the company’s live data on demand. Agentic analytics is the term for this shift: an autonomous agent at every business user’s elbow, doing the analyst’s work without waiting in the request queue.