Build observable enterprise agentic retrieval using Managed Amazon Bedrock Knowledge Base with AWS CloudFormation

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Teams that add Retrieval Augmented Generation (RAG) to a foundation model usually start with a single retrieval step against a single knowledge base. That works until the questions get harder, when the answer spans several sources, or the system has to decide which source to consult before it can respond. Enterprise agentic retrieval solves that: an agent reasons about the question, routes it to the right knowledge base, retrieves iteratively, and returns a cited answer. But it introduces a harder operational problem. Once an agent reasons and retrieves in a loop, you can no longer see what it did or whether the answer was any good. A previous post, Build an end-to-end RAG solution using Amazon Bedrock Knowledge Bases and AWS CloudFormation, automated a single-shot RAG workflow with a self-managed (vector-store) Knowledge Base. Amazon Bedrock Knowledge Bases has evolved from RAG to agentic retrieval with the launch of managed knowledge bases. Managed Knowledge Bases agentic retrieval performs multi-turn planning, executes retrieval tools, and generates grounded answers with citations. This post takes the next step: an enterprise agentic retrieval solution where an agent reasons, retrieves across multiple knowledge bases, and synthesizes a cited answer. It is built on the Amazon Bedrock Managed Knowledge Base and Amazon Bedrock AgentCore, with observability and evaluation built in from the start. You deploy all of it with a single AWS CloudFormation chain.

Figure 1: End-to-end architecture from synthetic corpora in Amazon S3 through two Managed Knowledge Bases, the …

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