The AI industry is obsessed with autonomy. Agents that plan, reason, delegate and dynamically decide what to do next are increasingly presented as the future of enterprise AI. But what if the most reliable AI systems are deliberately less autonomous? This session challenges the assumption that greater autonomy produces better enterprise outcomes. We'll explore why human-designed workflows, independently tested agents, explicit orchestration and organisational context may provide a more practical foundation for production AI than unrestricted agent collaboration. Through an engineering lens, we'll examine where deterministic control should end, where probabilistic reasoning genuinely adds value, and how to build AI systems that are both intelligent and accountable.
Harrison Kirby is the Co-Founder and Chief Technology & AI Officer of Great Wave AI, specialising in enterprise AI architecture, agentic systems and the engineering of reliable, production-ready AI platforms. With over 15 years of experience in engineering, enterprise technology and technical leadership, Harrison is a leading voice in the practical application of artificial intelligence. He is the author of Exploring GenAIOps and Reliability Is The Product: Engineering AI Systems People Can Depend On. An early pioneer of Retrieval-Augmented Generation (RAG) and agentic AI in the UK, Harrison focuses on bridging cutting-edge AI innovation with secure, scalable and dependable enterprise deployment, championing engineering excellence, governance and trust in AI systems.
