AtScale MCP Server Connects Bedrock AgentCore Agents to Governed Data
Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore
Governance for agentic analytics is often bolted onto prompts, which is fragile and unauditable; this shows enforcement moving down into the data layer itself. Data engineers and platform teams building agents on AWS can follow the walkthrough to expose Redshift models safely through MCP. The pattern applies anywhere enterprises need row-level security for AI agents, with AtScale as the named alternative to hand-rolled semantic layers.
This is the third post in AWS's semantic-layer series on building agentic AI. It walks through constructing a governed customer 360 model on Amazon Redshift using AtScale. The model is then exposed to a Strands agent running on Amazon Bedrock AgentCore via the AtScale MCP server. Governance controls including identity propagation, RBAC, row-level security, and full query auditing are enforced at the semantic layer.
Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore
The third post in our semantic-layer series builds a governed customer 360 model on Amazon Redshift with AtScale, then exposes it to a Strands agent on Amazon Bedrock AgentCore through the AtScale MCP server. Identity propagation, RBAC, row security, and full query audit are enforced at the semantic layer, not the prompt.
