AWS Demonstrates Agentic Retrieval with LangChain and Bedrock Knowledge Bases

AWS published a hands-on tutorial on the Machine Learning Blog explaining how to combine LangChain with Amazon Bedrock Managed Knowledge Bases to compare standard retrieval versus agentic retrieval. The tutorial uses a multi-part question containing six intents as an example: with single-vector retrieval, the 5 returned results covered only 4 of the sub-intents, while agentic retrieval uses a planning loop to break the complex question into multiple sub-queries, evaluate the retrieved evidence, and iteratively supplement retrieval as needed, thus covering all sub-intents more completely. This is a first-hand official AWS tutorial demonstrating agentic retrieval's advantages over traditional single-shot retrieval for complex multi-intent queries, as well as the integration method between LangChain and Bedrock Knowledge Bases.

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