LangChain's annual conference focuses on the challenges of scaling AI agents from production validation to enterprise-wide deployment, revealing how major companies build platforms, evaluate performance, and structure teams.
LangChain argues that building better AI agents hinges on improving their 'harness' rather than the model itself, and shares a systematic method using evals as training signals for iterative improvement.
LangChain shares its core philosophy for building AI agent evaluation systems: more evals aren't better; instead, precisely define and measure the agent behaviors you care about to guide its evolution.