The checklist provides a structured approach to building and evaluating agent performance, emphasizing the importance of starting with simple, signal-generating evaluations, using LangSmith for trace-to-data conversion, and manually reviewing traces to identify failure patterns. It outlines steps for defining success criteria, separating capability and regression evaluations, and assigning ownership to a single domain expert to avoid blaming infrastructure or data pipeline issues. The process also involves designing specialized grader systems, running evaluations with multiple trials and distinct types, and ensuring the evaluation suite directly measures production behaviors.
Managed Deep Agents gives developers a managed way to build, run, and deploy Deep Agents with built-in runtime, streaming, sandboxes, evals, memory, and auth.

LangSmith Bring Your Own Cloud is now generally available on AWS, giving Enterprise teams managed observability, evaluation, and deployment inside their own VPC.

Learn what AI agents are, how they work in an LLM loop, and where workflows fit so you can build reliable, production-ready autonomous systems.
