Why Building an Eval Platform Is Harder Than It Looks — Braintrust

This post details the evolution of AI agent evaluation platforms, moving from simple spreadsheets to sophisticated production flywheels. It highlights the challenges of non-deterministic LLMs necessitating both pre-production evals and post-production observability. A key contribution is the introduction of BTQL, a specialized query language designed to handle the massive and complex data generated by agent traces, which are unsuitable for traditional data warehouses. The talk also explores the concept of agents running their own evals and identifying novel failure modes.