Production-Grade Agentic AI: From brittle workflows to deployable autonomous systems cover

Book

Production-Grade Agentic AI: From brittle workflows to deployable autonomous systems

By Ran Aroussi

Published 2025

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About this book

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.

Computers & TechnologyComputer ScienceAI & Machine LearningExpert Systems

About Ran Aroussi

Ran Aroussi, podcast guest

Founder of VarOps • Building software and Resident AI into companies that can't afford to guess.

35+ years Production coding experience30M+ Open-source downloads per month50K+ GitHub stars3B+ Ads delivered daily by systems he built

Ran Aroussi has spent 35 years building software infrastructure. Now he thinks we’re building AI for businesses backwards.

Most companies are adding copilots, chatbots, and agents, then asking employees to learn how to use them. Ran’s argument is almost the opposite: people shouldn’t have to adapt to AI. AI should adapt to the company.

He’s the founder of VarOps, where he’s building what he calls "Resident AI": AI that lives inside an organization, learns how it actually operates, and works through the tools and workflows people already use. No new destination. No constant prompting. Ideally, employees barely notice it’s there.

The idea grew out of a problem Ran believes the AI industry has underestimated: AI doesn’t understand organizations.

A chatbot might have access to every document, meeting, and message in a company and still not understand why a decision was made, which unwritten rule matters, who actually knows how something works, or that the official process hasn’t been followed in three years.

Retrieval gives AI information. It doesn’t necessarily give it understanding.

Ran’s work focuses on building that missing layer: a living model of the organization itself – its knowledge, decisions, relationships, processes, and unwritten operating context. He describes it as an "Organizational Language Model" (OLM) rather than another LLM with access to company data.

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How Invisible AI Agents Are Taking Over the Enterprise (With Ran Aroussi of VarOps) - Ep #248
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Future Tech And Foresight· Listen to Ran Aroussi on air