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Infrastructure AWS Machine Learning

Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases

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 each retrieval path costs.

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More in Infrastructure

Infrastructure AWS Machine Learning

Rethinking access control for RAG with Amazon Quick and Amazon Bedrock

Enterprise RAG unlocks insights from knowledge sources like SharePoint, Google Drive, and Confluence, but those sources carry complex permissions. Learn how Amazon Quick and Amazon Bedrock Knowledge Bases enforce document-level access controls in real time, verifying permissions directly with authoritative sources at query time.

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Infrastructure AWS Machine Learning

Beyond hours saved: Building the business case for agentic automation

The RPA-era ROI model misses most of the value agentic automation creates. This post gives AI center of excellence leaders a framework to size the full value of agents across time savings, exception handling, decision quality, and maintenance economics, and to prioritize which workflows to automate first.

Read brief Original at AWS Machine Learning (opens in a new tab) ↗