AWS releases tutorial on building semantic layer with EDB Postgres AI
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The AWS Machine Learning Blog released a tutorial on how to use the AIDB extension of EDB Postgres AI to build a semantic layer inside a Postgres database, and have Strands Agents agents running on Amazon Bedrock AgentCore query that layer through an MCP endpoint to answer customer 360 questions. The tutorial notes that the AIDB AMI comes with a SemKB named semkb by default; after creating c360_kb, the instance will have two SemKBs; at this point, every call to aidb.semantic_kb_search must explicitly pass kb_name. On network configuration, the ALB target group connects to EC2 port 8765 over HTTPS and does not validate the backend certificate chain, so a self-signed certificate is sufficient for development and testing; in production, an internal ALB should be used together with AgentCore Gateway's VPC egress so that the connection between them remains private.
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- AWS Machine Learning BlogBuild a semantic layer for agentic AI with EDB Postgres AI and Amazon Bedrock AgentCore
This post shows how to build a semantic layer for agentic AI using EDB Postgres AI's AIDB extension, where business meaning lives in Postgres COMMENT statements, join paths come from foreign keys, and governance uses GRANT and row-level security through the built-in MCP endpoint. A Strands Agents agent on Amazon Bedrock AgentCore queries the layer via AgentCore Gateway and an ALB to answer customer 360 questions, with two Postgres roles receiving different results for the same prompt based on column-level grants. AIDB also captures OpenTelemetry traces for each SQL execution as rows in the aidb_otel schema, queryable with ordinary SQL.
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