Ran Aroussi’s publications

Build an AI-native company – not just AI features Most companies "do AI" and nothing changes. They add a chatbot, a copilot, an agent that automates a task. They get more efficient. They don't get fundamentally different – and six months later, the only thing that's changed is the software bill. The companies that win the next decade won't just use AI. They'll run on it. Company-Scale Agentic AI is the operator's guide to building one – no code, no hype, no black box. Just the method: how to make your business visible to the machines, where to place judgment and where to place rules, how to prove it's working in your own numbers, and how to build the one asset that compounds instead of decaying – a company that understands itself and gets sharper every week. You'll follow one company, Acme, through the complete transition – from scattered teams and a failed pilot to an AI-native operation – and learn why even the smartest AI gives wrong answers, why context beats model size, why memory is the difference between a tool and a teammate, and how to build systems that improve on their own instead of being rebuilt every year. This isn't about today's tools or today's prompts. It's about the principles that stay true long after this year's AI trends are forgotten. Inside you'll learn: Why your AI is "blind, not stupid" – and why that's the best news you'll get all year Why your AI is "blind, not stupid" – and why that's the best news you'll get all year The difference between automation and organizational intelligence – and where each belongs The difference between automation and organizational intelligence – and where each belongs How to build AI that remembers, learns, and improves over time How to build AI that remembers, learns, and improves over time Why context is a bigger competitive advantage than model capability Why context is a bigger competitive advantage than model capability How to place a human hand on the decisions that need one – and take it off the ones that don't How to place a human hand on the decisions that need one – and take it off the ones that don't How to prove it's working before you bet the business on it How to prove it's working before you bet the business on it How to build the compounding advantage that widens the gap while competitors run pilots How to build the compounding advantage that widens the gap while competitors run pilots Written for the founders, CEOs, COOs, and CFOs who have to make the AI decision – and can't afford to get it wrong. No AI team required. No engineering degree needed. Just the operator's playbook for becoming an AI-native company. You don't need better AI. You need a company that can actually use it.

The infrastructure handbook for building AI agents that survive real users. Move beyond prompt chains and learn the production principles that treat agents like the distributed systems they are – not another framework tutorial.“After 30 years building distributed systems, I've watched the AI industry repeat every mistake we made – and solved – decades ago. Those lessons are now applied to autonomous AI.”– Ran Aroussi, AuthorBook descriptionMost AI systems today fail in production. They chain prompts together and call it "agentic." They rely on a dozen stitched-together tools and collapse under real-world pressure. The gap between demo and production remains enormous.This book bridges that gap.It provides a comprehensive guide to the architecture, design principles, and infrastructure patterns needed to build autonomous AI systems that actually work in production. Moving beyond vendor-specific tutorials and framework documentation, you'll understand the universal principles that make agentic systems reliable, observable, and deployable at scale.What you'll learn The three pillars of true autonomy - What separates real agents from chatbots with tool access The three pillars of true autonomy - What separates real agents from chatbots with tool access Multi-tier memory architecture - Design systems that scale from buffer to persistent storage Multi-tier memory architecture - Design systems that scale from buffer to persistent storage Intelligent orchestration and multi-agent coordination - Task decomposition and adaptive workflows Intelligent orchestration and multi-agent coordination - Task decomposition and adaptive workflows Observability for non-deterministic behavior - Track, debug, and audit autonomous systems Observability for non-deterministic behavior - Track, debug, and audit autonomous systems Avoid vendor lock-in - Multi-model routing with automatic failover and resilience Avoid vendor lock-in - Multi-model routing with automatic failover and resilience Ship complete production examples - Real deployments, not toy demos Ship complete production examples - Real deployments, not toy demos About the AuthorRan Aroussi is a self-taught software engineer with 30+ years building production systems – from ad-serving engines delivering 3 billion ads daily to creating yfinance, one of the world's most widely adopted data libraries (10M+ monthly users).Frustrated by the gap between AI demos and production reality, he wrote this book for engineers tired of hype and ready to build infrastructure that actually works at scale.Through his companies, he continues to champion pragmatic engineering and open infrastructure that actually works at scale.