Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases
Agentic retrieval breaks multi-part questions that single-shot RAG can't answer. Here's how to build it on Bedrock, and what it costs.
Why it matters
AWS/LangChain ship a hands-on tutorial for agentic RAG on Bedrock Managed Knowledge Bases. The practitioner value: a concrete pattern for multi-hop retrieval that traces cost per query path — single-shot vs. agentic — enabling informed tradeoff decisions.
The key facts
10 to knowAWS Bedrock Managed Knowledge Base now supports agentic retrieval patterns via LangChain
Tutorial demonstrates multi-part question handling: agentic retrieval vs. single-shot retrieval comparison
Cost tracking provided: trace events enable per-query cost comparison between retrieval strategies
No pricing or performance metrics disclosed in source
Publication date: October 5, 2026
Amazon Bedrock Managed Knowledge Base supports agentic retrieval
LangChain integration enables multi-part question handling
Comparison includes trace events and cost analysis between agentic and single-shot retrieval paths
Use case: queries that single-shot retrieval answers poorly
Published Oct 5, 2026
The story so far
Earlier coverage of this storyline
- Query claims in natural language with Amazon Bedrock Knowledge BasesAWS Machine Learning Blog
- This story
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrieval answers poorly. Run the same query through both paths, read the trace events, and compare what…