LLMs are non-deterministic, but production AI agents still need to feel predictable. If two users ask similar questions, or an agent works through the same kind of problem twice, the exact wording may change, but the decisions, actions, and overall outcome should stay reasonably consistent.

This talk looks at how to build that kind of functional consistency around non-deterministic models. I’ll cover where variability enters an agentic system, how it compounds through reasoning and tool use, and some practical ways to keep behaviour within predictable bounds using constrained execution, state management, validation, retries, tool design, and evaluation.