Building accurate AI workflows can get complicated fast. By explicitly defining and modularizing agent tasks, my AI flows have become more precise, consistent, and efficient—delivering improved outcomes consistently. But can we prove it? In this talk, I'll walk you through an agentic app built with Langflow, and show how giving agents narrower, well-defined tasks leads directly to more accurate, consistent results. We'll put that theory to the test using evals with Pytest and LangSmith, iterating across different agent setups, analyzing data, and tightening up the app. By the end, we'll have a clear, repeatable workflow that lets us have confidence in how future agent or LLM changes will affect outcomes, before we ever hit deploy.