In evaluating AI products built with large language models (LLMs), Hex AI, led by Bryan Bischof, has developed a unique approach that focuses on breaking down the evaluation process into granular, user-centric components rather than relying on a single "god metric." This methodology ensures a comprehensive assessment of AI agents, which are designed to automate complex data analysis tasks by generating SQL queries and creating visualizations. The success of Hex's AI agents stems from a strategic system design that includes mapping tools to existing workflows, creating reactive directed acyclic graphs (DAGs) to track task dependencies, and keeping humans in the loop to correct AI actions. Instead of simplifying evaluations into one metric, Hex employs a suite of binary evaluators that align with the ideal user experience, ensuring the AI product delivers true value. Bischof emphasizes the importance of immersing oneself in data to uncover insights, advocating for regular team engagement with evaluation data to improve AI product performance. This approach, supported by platforms like Humanloop, which facilitates logging and observability, demonstrates that a thoughtful, data-driven evaluation can lead to reliable AI agent deployment.