LangChain shares five evaluation patterns learned from shipping four deep agent applications. Key insights include: each test case needs bespoke success criteria rather than uniform evaluation logic; single-step evals (constraining the agent loop to one iteration) are efficient for validating decision-making at specific points; full agent turns provide end-to-end trajectory, final response, and artifact testing; multi-turn simulations require conditional logic to handle agent deviations gracefully; and reproducible test environments (e.g., temporary directories, Docker containers, mocked HTTP requests) are essential for reliable results. Code examples use LangSmith's Pytest integration with LangGraph's interrupt_before feature.
