Evaluating Deep Agents using LangSmith on AWS
📰 AWS Machine Learning
Learn to evaluate deep agents using LangSmith on AWS with a practical guide
Action Steps
- Apply five evaluation patterns for deep agents to assess their performance
- Build offline evaluations using pytest and LangSmith for thorough testing
- Configure online monitoring for production to track agent performance in real-time
- Use LangSmith on AWS to streamline the evaluation process for deep agents
- Deploy a text-to-SQL deep agent with Amazon Bedrock for a full development to production lifecycle
Who Needs to Know This
Machine learning engineers and developers on a team can benefit from this guide to evaluate and improve their deep agents, ensuring reliable and efficient production deployments
Key Insight
💡 Evaluating deep agents is crucial for reliable production deployments, and using LangSmith on AWS can simplify the process
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🤖 Evaluate deep agents like a pro with LangSmith on AWS! 💡
Key Takeaways
Learn to evaluate deep agents using LangSmith on AWS with a practical guide
Full Article
This post combines learnings from LangChain’s work on evaluating deep agents and Anthropic’s guide to demystifying evals for AI agents into a practical guide. In this post, you will learn how to: 1) apply five evaluation patterns for deep agents, 2) build offline evaluations using pytest and LangSmith, and 3) configure online monitoring for production. The walkthrough uses a text-to-SQL deep agent with Amazon Bedrock for the full development to production lifecycle.
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