Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge

AI agents are quickly becoming an integral part of customer workflows across industries by automating complex tasks, enhancing decision-making, and streamlining operations. However, the adoption of AI agents in production systems requires scalable evaluation pipelines. Robust agent evaluation enables you to gauge how well an agent is performing certain actions and gain key insights into them, enhancing AI agent safety, control, trust, transparency, and performance optimization.

Amazon Bedrock Agents uses the reasoning of foundation models (FMs) available on Amazon Bedrock, APIs, and data to break down user requests, gather relevant information, and efficiently complete tasks—freeing teams to focus on high-value work. You can enable generative AI applications to automate multistep tasks by seamlessly connecting with company systems, APIs, and data sources.

Ragas is an open source library for testing and evaluating large language model (LLM) applications across various LLM use cases, including Retrieval Augmented Generation (RAG). The framework enables ...