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

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

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.

An agent that answers business questions from live data needs three layers: a model that plans and writes queries, a meaning layer that tells the agent what the data represents, and a runtime that hosts the agent and controls identity and access. The Stardog post in this series introduced this framing. The model layer in this post is Anthropic Claude on Amazon Bedrock , the same as prior posts (model availability varies by AWS Region ). The runtime layer is AgentCore runtime , a capability of Amazon Bedrock AgentCore. AgentCore Gateway handles inbound authorization, and AgentCore Identity , a capability of Amazon Bedrock AgentCore, holds outbound credentials for the tool targets, same as prior posts. EDB Postgres AI hosts agents and tools inside the database itself and acts as the connection point for external agents through its MCP endpoint.


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