Evaluating and Debugging Non-Deterministic AI Agents

Non-determinism in Generative AI presents unique challenges for production-ready software, but it can be managed through established engineering principles rather than simply zeroing out model temperature. While lowering temperature increases predictability, it often stifles the creativity and value of LLMs. Effective management requires shifting focus from strict determinism to ensuring "reasonable" responses through granular evaluation at every stage of an agentic flow. For instance, a reservation agent should verify data extraction and API tool outputs before proceeding to final execution. Robust logging is equally critical to transform a "black box" into a debuggable system, allowing developers to track specific tool parameters and intermediate steps. Ultimately, building reliable AI applications relies on standard software practices: catching errors early, implementing human-in-the-loop overrides for complex failures, and designing graceful fallbacks when unexpected outputs occur.