AWS ML BlogSaturday · May 30, 2026FREEEvaluating Deep Agents using LangSmith on AWS
The AWS ML Blog post, published May 28, 2026, combines insights from LangChain's work on deep agent evaluation and Anthropic's guide to AI agent evals. It provides a practical framework for evaluating deep agents, including five evaluation patterns: correctness, robustness, efficiency, safety, and alignment. The guide demonstrates building offline evaluations using pytest and LangSmith, and configuring online monitoring for production deployments. The walkthrough uses a text-to-SQL deep agent built with Amazon Bedrock, covering the full development-to-production lifecycle. This approach allows developers to catch regressions, validate agent behavior, and monitor performance in real-time. The post is aimed at teams building complex AI agents that require rigorous testing and observability.
Provides a structured evaluation framework for deep agents, reducing deployment risks.