Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore
In the first post in this series, we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift.
Proof1 independent outlet
Key points
- We ran a Strands agent on Amazon Bedrock AgentCore against that layer.
- AtScale authors the model in the Semantic Modeling Language, an open, YAML-based specification versioned in Git, and exposes it simultaneously, from a single deployed model, over three protocols: SQL, XML for Analysis (XMLA), and the Model Context Protocol (MCP).
- How AtScale plans a dimensional query against Amazon Redshift, and how AtScale’s autonomous aggregate management accelerates the query patterns an agent generates.
- How to expose the model to an AgentCore-hosted Strands agent through the AtScale MCP server, and how to register that MCP server with AgentCore Gateway, a capability of Amazon Bedrock AgentCore.
Sources (1)
- [1]Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCoreAWS Machine Learning Blog · Oct 20, 03:33 PM
In the first post in this series, we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift.
We ran a Strands agent on Amazon Bedrock AgentCore against that layer.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026[AINews] DeepSeek DeepInfer: Open Source SOTA Inference running 155T toks/day/cluster making $22m/day at ~80% margin for a 81 day payback
- Oct 19, 2026Structured outputs for Anthropic Claude models on Amazon Bedrock
- Oct 19, 2026Pegasus 1.5 from TwelveLabs is now available in Amazon Bedrock
- Oct 19, 2026Launch HN: Laminar (YC S24) – RL-trained model that finds errors in agent traces
- Oct 19, 2026NVIDIA Open Sources Run:ai Under Apache 2.0 for Kubernetes GPU Orchestration
- Oct 19, 2026TorchNeuron: running HF models on AWS Trainium is easier than ever
