Determinism Was Never the Point

Developing AI agents or skills for applications often involves balancing the need for deterministic behavior with the inherent value of non-determinism, which allows AI to handle complex, unpredictable scenarios. While stakeholders from various departments like sales, customer success, and marketing may seek more deterministic performance to meet specific business goals, this often masks underlying alignment issues among team members regarding the AI's functionality. The challenge lies not in achieving full determinism, which can stifle an AI's capabilities, but in creating objective evaluation frameworks that codify expected behaviors without compromising flexibility. This involves using evaluation methods that focus on the traits of generated solutions rather than exact outcomes, allowing for a structured approach to incremental improvements while maintaining a repository of tests to ensure ongoing functionality. By fostering consensus and codifying expectations, teams can optimize AI performance while navigating the complexities of non-determinism, ultimately leveraging AI's unique strengths without succumbing to inflexible rule systems.

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