Describe a situation where your team had a strong belief about how an AI agent or multi-agent system should be built, but you used evidence to prove that assumption wrong. How did you design experiments using MLflow Agent Evaluation, LLM-as-Judge, or offline/online evaluation to compare approaches such as DBRX or Databricks Foundation Model APIs, and what did you do when the data contradicted senior opinions or your own instincts? Focus on how you pursued the truth, communicated it, and changed the direction of the work.