A prototype is one path through one model on a good day. A system is everything else.

The capability frontier is no longer what stops an enterprise deploying generative AI. What stops it is everything around the model: retrieval that stays honest as the corpus changes, routing that stays economical as prices move, privacy that survives contact with an inference API, memory that can be audited, and failure that can be localized before it cascades.

This book treats those as engineering problems with measurable answers. It opens with a systematic review of how production generative AI is actually being built, then develops nine frameworks in detail: a semantic privacy budget with formal guarantees, variational meta-routing across providers, causal bandits for workflow routing, verifiable neural-symbolic memory, contrastive graph learning for multi-agent failure recovery, topological consensus for adversarial retrieval, spectral paging for long-context serving, cost-aware cascading, and self-healing streaming indices.

Each chapter carries its own method, evaluation and references. It is written for the architects and platform engineers who are asked when the demonstration can go live.