LangChain Publishes Agent Evaluation Readiness Checklist — A Practical Guide for Production Evals
Shipping agent evals is hard — not because the tooling doesn't exist, but because most teams don't know where to start. LangChain's deployed engineering team has tackled that problem head-on with a new, opinionated checklist for building production-grade evaluation pipelines, drawn from real experience shipping agents at scale rather than from abstract principles.
The guide opens with a step many teams skip entirely: before writing a single line of eval code, manually inspect 20 to 50 real execution traces. It's tedious, but it's the fastest way to understand what your agent actually does versus what you think it does. From there, the checklist walks through success-criteria definition, dataset construction, and regression gating — each phase building on the last. A particularly useful distinction the guide draws is between capability evals (does the agent do this thing at all?) and regression evals (did the latest change break something that previously worked?). Teams that conflate the two end up with test suites that give false confidence.
The playbook pairs tightly with LangSmith's annotation queues and trace tooling, but the underlying methodology applies to any eval infrastructure. The core message is practical: start with the simplest end-to-end test that produces a real signal, resist layering complexity until simpler tests demonstrably miss real failures, and treat eval debt as the production risk it actually is. For teams whose agent projects have stalled before reaching reliable deployment, this checklist is a concrete place to restart.