# Build a semantic layer for agentic AI with EDB Postgres AI and Amazon Bedrock AgentCore

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/build-a-semantic-layer-for-agentic-ai-with-edb-postgres-ai-and-amazon-bedrock
> **Original Source**: [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/build-a-semantic-layer-for-agentic-ai-with-edb-postgres-ai-and-amazon-bedrock-agentcore/)
> **Author**: Rajesh Nagpal (undefined)
> **Published Date**: Tuesday, November 3, 2026
> **Category**: INDUSTRY
> **Tags**: AI, AWS ML Blog, industry

## Executive Summary
Learn how to build a governed semantic layer for agentic AI inside EDB Postgres AI and query it from a Strands Agents agent on Amazon Bedrock AgentCore.



## Full Coverage
In this post, we show how to build a semantic layer for agentic AI using EDB Postgres AI . We run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 (C360) questions. Earlier posts in this series built the same C360 pattern with Stardog's knowledge graph , Denodo's data virtualization platform , AtScale's dimensional model , and Strategy Mosaic's linked models

EDB Postgres AI puts the semantic layer inside the operational Postgres database as an AIDB extension . Business meaning lives next to the data in standard Postgres COMMENT statements, the join graph comes from Postgres foreign keys, and governance uses Postgres GRANT and row-level security through the Model Context Protocol (MCP) endpoint that ships with AIDB

By the end of this post you will know:

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