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Observing and evaluating production agents using OpenSearch Agent Health

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announcement

AWS details a solution for observing and evaluating production AI agents using OpenSearch Agent Health. This approach leverages OpenTelemetry traces to capture agent behavior and compares it against desired outcomes, addressing the limitations of traditional testing. It enables teams to monitor AI agent quality and establish quality gates in their development lifecycle. The post provides a detailed walkthrough for deploying a sample solution on AWS, integrating services like Amazon Bedrock, OpenSearch Ingestion, and Amazon OpenSearch Service.

  • →Solution Architecture for AI Agent Observability
  • →Walkthrough: Deploying and Evaluating a Sample AI Agent
Notes (2) ›
  • Solution Architecture for AI Agent Observability

    The solution integrates an AI agent on AWS with OpenSearch Agent Health for observability and evaluations. It utilizes OpenTelemetry traces to capture agent behavior, with Amazon OpenSearch Ingestion and Amazon OpenSearch Service for data collection and storage. The architecture also includes AWS Amplify for UI and Amazon Bedrock for LLMs powering the agent and evaluations.

  • Walkthrough: Deploying and Evaluating a Sample AI Agent

    A step-by-step guide is provided to deploy a sample e-commerce AI agent to AWS, start OpenSearch Agent Health locally, and connect it to cloud resources. The walkthrough demonstrates exploring agent traces and creating/running test cases to evaluate agent quality. Prerequisites for the deployment include an AWS account, Git, Node.js, and AWS Cloud Development Kit (CDK).

Read the original announcement →

https://aws.amazon.com/blogs/big-data/observing-and-evaluating-production-agents-using-opensearch-agent-health/

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