Ten hard problems between a working agent and a platform you can sell. Running one AI agent is a demonstration. Running thousands of them for hundreds of customers on shared infrastructure is a platform problem, and most of it is still unsolved. This book takes ten of those problems in turn: keeping prompts safe from injection, proving that one tenant's data cannot reach another, routing requests across model providers while cost and latency keep moving, sharing a memory index without leaking private vectors, resolving conflicts when agents act at the same moment, and watching the system closely enough to catch a failure before a customer reports it. Each chapter states a design, builds it, and measures it against the alternatives. It opens with a systematic review of the published work on scalable agent platforms and closes with an observability framework that treats agent behavior as something to be measured rather than assumed. Written for the engineers and architects who have to put such a platform into production.