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.
Multi-agent systems have emerged as a powerful solution for addressing complex, real-world problems that require reasoning across data sources, tools, and business constraints. However, ensuring that these systems are consistently helpful, accurate, explainable, and adhere to business constraints in production scenarios is a critical challenge.
Amazon Bedrock AgentCore is a platform designed to build, connect, and optimize agents at scale, providing a fully managed capability for assessing agent performance across development and production. Amazon Bedrock AgentCore Evaluations is a key component that addresses this challenge by offering both built-in and custom evaluators to measure agent performance across various quality dimensions, such as helpfulness, task success, and explainability.
This evaluation framework complements traditional evaluation methods that focus solely on model response quality, providing structured, measurable insights into the decision-making process of agentic systems.
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