Large Language Models are inherently probabilistic systems, yet many of the environments adopting them demand determinism, auditability, and accountability. In these contexts, “mostly correct” is indistinguishable from failure, and reliability cannot be achieved through prompt tuning or model alignment alone. This talk argues that determinism is not a model property but an architectural one: control must be engineered through orchestration, validation layers, policy enforcement, and structured human oversight. It further explores how scaling reliable AI systems is less about adding workflow branches and more about standardizing governed agent primitives, shared contracts, constraints, and evaluation frameworks that allow flexibility within certified boundaries.
Harrison Kirby is CTO and Co-Founder of Great Wave AI and Founding Director of the Centre for GenAIOps. With over 15 years of experience across engineering, operations, and technical leadership, he focuses on building and scaling AI systems that bridge research and real-world deployment. Based in the UK, he works at the intersection of GenAI, platform architecture, and applied innovation.
