LangChain explains eval-driven development for AI agents

Eval-driven development is a framework for building non-deterministic AI agents by measuring performance against a consistent set of metrics, test datasets, and evaluators. The process involves defining expected agent behaviors, writing an evaluation suite before building, running a baseline experiment, and iteratively comparing changes against regressions. This methodology helps developers catch unintended behavioral overrides and avoid overfitting their agents. Open article →