SDLC for the Age of AI

AI writes plausible code faster than any review can keep honest. That is the problem this book solves, and it does not solve it with trust. You never took a developer's word either. "Works on my machine" was never proof. Tests, reviews and pipelines exist because trust in software was always supposed to be earned with evidence. This book takes that instinct to its conclusion and rebuilds the software development lifecycle around it: people define what must be true and judge what ships, machines build the change and prove it against those criteria, and anything unproven does not get in. Inside: how to write a specification a machine can be held to, why every acceptance criterion should declare what would falsify it, how independent review works when the reviewer is also a machine, what a green gate has to mean before you trust it, how to run a fleet of agents without them building the same thing twice, what an incident looks like when the responder can read production but cannot touch it, and how to price the effort of work that no longer takes human hours. The method in this book is implemented, released and running. Not a proposal. Code got cheap. Certainty didn't.