LangSmith on AWS Supports Deep Agent Evaluation
TL;DR. A new guide details evaluation methods for deep AI agents using LangSmith on AWS, focusing on practical implementation for developers. - The resource combines evaluation insights from LangChain and Anthropic to improve agent reliability. - It presents five evaluation patterns and methods for building offline and online monitoring. - The guide uses Amazon Bedrock and a text-to-SQL agent for a full development-to-production lifecycle.
- AI agent evaluation presents significant challenges due to non-deterministic, multi-step operations.
- LangSmith on AWS offers a framework to evaluate and monitor deep agents from development to production.
- The guide outlines five specific evaluation patterns for deep agents.
- It teaches developers to build offline evaluations using pytest and LangSmith.
- The process includes configuring online monitoring for production systems.
- Amazon Bedrock and Amazon Nova 2 Lite are used in a text-to-SQL agent example.
- Deep agents are defined by their ability to extend thinking and handle complex inputs.
- Key evaluation terminology includes Task, Trial, Grader, Transcript, Outcome, and Evaluation Harness.
Sources
- Evaluating Deep Agents using LangSmith on AWS — aws.amazon.com