From Vibes to Agents: How You Can Build Enterprise Software With AI — and Make It Actually Work

A non-engineer built enterprise software with AI. Not a prototype. Not a demo. A production platform. This is the system that made it possible.Ethan Owen is co-founder and COO of Limn, a B2B design, fabrication, sourcing and logistics company specializing in high-end furniture, fixtures and materials for commercial and luxury residential projects. Limn blends traditional craftsmanship with advanced tooling across premium hardwoods, metals, stone, fabrics, ceramics and fiber-reinforced concrete. He has a degree in cultural anthropology from Cornell University. He's never been a software engineer. And he built an enterprise platform — CRM, production tracking, client portals, document management, quality control workflows, AI-powered search — using AI agents governed by a system he designed from scratch.The hard part wasn't the AI. It was everything around the AI.The first two weeks were magic. Features appeared in hours instead of weeks. The gap between domain expertise and working software closed overnight. Then the system started breaking. The AI forgot previous decisions. It claimed features were complete when they weren't. It changed one file and broke fifteen others it couldn't see. It guessed at database columns that didn't exist and built three features on the guess.Sound familiar?From Vibes to Agents is the story of what came next — the journey from "vibe coding" to a disciplined governance system that made AI reliable enough to ship enterprise software. Not theory. Practice. Every rule in the system exists because something broke. Every verification type exists because a claim turned out to be false. Every automated hook exists because a manual check was forgotten.What you'll learn:The 7 structural failure modes of ungoverned AI — and why better prompts can't fix themHow to write rules AI will actually follow (not suggestions — enforceable law)A corrections system where your exact frustrated words become permanent rules — AI mistakes corrected once never recur across hundreds of future sessionsThe verification mindset: replacing "trust but verify" with "never trust, always verify"The Incident Loop — how every failure makes the system stronger, foreverA pattern scanner that encodes every past failure as a detection rule — plus 32 hooks that enforce quality without human interventionHow to plan before building so AI doesn't waste your timeContext management — why AI quality degrades in long sessions, and the recovery protocol that rebuilds everything when the system compresses your conversationA tech intelligence platform that turns 150+ article reviews into competitive advantage — with a scoring system that drove 78 concrete implementation plansMulti-agent orchestration — parallel worktree agents, each governed identically, that turn a large plan into simultaneous implementationHow one person with a governance system produces enterprise-quality outputA step-by-step playbook to build your own governance system, starting todayWho this book is for:Solo founders building with AI who can't figure out why everything keeps breaking. Technical leaders watching vibe coding create debt across their teams. Non-engineers with deep domain expertise who want to build real software — not prototypes. CTOs evaluating AI agents in production.If you've been through the wizard phase and you're looking for what comes next, this book is for you.Based on a real system, built under real pressure, with real results. 48 canonical rules. 48 incident post-mortems. 50+ verification types. 32 automated enforcement hooks. A knowledge graph with 64 structured tools. All extracted from a production platform serving real clients.The model isn't the variable. The system around the model is. Build the system. Read more