This post builds enterprise agentic retrieval on a Managed Amazon Bedrock Knowledge Base with the AWS Cloud Development Kit (AWS CDK). To see why that’s worth building, it helps to start with where ordinary Retrieval Augmented Generation (RAG) breaks down. Most RAG systems start the same way: they embed the question, fetch the nearest chunks from one index, hand them to a model, and return the answer. One retrieval, one generation. For a large class of questions that’s the right design, and there’s no reason to build anything more complicated.
It fails in a recognizable way. Ask a question whose answer spans two corpora, and a single retrieval returns half of what the model needs, which is worse than returning nothing, because the answer comes back fluent and incomplete. Ask a question where the right source depends on the question, and a single index has no mechanism to choose. Ask a question that needs a second look at the corpus after seeing the first result, and there is nowhere for that second look to happen.
Enterprise agentic retrieval answers all three by putting a reasoning agent in the loop. The agent decides whether to retrieve, which knowledge base to consult, and whether to go back for more before it composes an answer. This is the direction Amazon Bedrock Knowledge Bases have evolved. With the launch of the Managed Knowledge Base, retrieval is no longer a single embed-and-fetch call. It has become an agentic capability: multi-turn planning, tool-driven retrieval, and grounded, cited answers.
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