From Prototype to Production — Where AI Systems Actually Break (Part 1)

Moving AI applications from prototype to production exposes a critical gap: while building has become trivially easy with tools like Figma Make and Replit, running AI systems reliably is still hard. Modern AI architectures involve machine-to-machine interactions at scale (human-to-machine identity ratios of 1:80 or even 1:500), where failures cascade quickly. The solution is an AI control plane — a governance layer sitting between applications and LLMs that handles guardrails, routing, cost control, runtime behavior, and observability. This is part one of a series that will explore each of these layers in depth.