THE BOOK FOR ENGINEERS SHIPPING REAL SYSTEMS
From brittle workflows to deployable autonomous systems
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.
“No fluff, no hand-wavy explanations. Just practical patterns, real architecture decisions, and hard-won lessons for building agents that actually work in production. This is what production AI actually looks like.”
– Reviewer
Book description
Most 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 - Multi-tier memory architecture
Design systems that scale from buffer to persistent storage - Intelligent orchestration and multi-agent coordination
Task decomposition and adaptive workflows - Observability for non-deterministic behavior
Track, debug, and audit autonomous systems - Avoid vendor lock-in
Multi-model routing with automatic failover and resilience - Ship complete production examples
Real deployments, not toy demos
Introduction Why demos worked perfectly but fails in production Part I / Foundations: Why Agentic AI is Inevitable 1. The rise of agentic systems 2. What agentic really means 3. The anatomy of a real agent 4. Production requirements for agentic systems 5. The infrastructure blueprint Part II / The Hard Problems of Agentic Infrastructure 6. Memory isn't optional 7. Context and domain knowledge 8. Grounding decisions with structured memory 9. Task decomposition and planning 10. Working with multiple LLMs Part III / Architecting Agentic Systems 11. Multi-agent coordination 12. DAGs, orchestration, and control loops 13. Observability, debugging, and audit trails 14. Tooling and integration via MCP 15. Synthesis: Complete production architectures 16. Production deployment patterns Part IV / Complete Production Examples 17. A recursive content agent 18. A multi-agent sales pipeline 19. Migration case study Part V / The Agentic Future 20. The future of automation is agentic 21. Building your infrastructure strategy Appendices A. Building Custom MCP Servers B. Further reading and references C. Glossary of agentic architecture
About the Author
Ran 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 company Automaze, he continues championing pragmatic engineering and open infrastructure that actually works at scale.
4.8/5 on ›
“The book offers Cutting-edge insights into AI's potential! This book bridges theory and practice, offering real-world examples and strategies for implementing agentic AI. A valuable resource for tech pros and curious minds alike.”
“This book breaks down complex concepts into clear, actionable steps focusing on how to design, implement, and scale agentic AI in production environments.”
“It’s a valuable read for developers, engineers, and tech leaders who want to understand how to build AI systems that actually work in production.”
“The book offers Cutting-edge insights into AI's potential! This book bridges theory and practice, offering real-world examples and strategies for implementing agentic AI. A valuable resource for tech pros and curious minds alike.”
“After 30 years building distributed systems, I've watched the AI industry repeat every mistake we made – and solved – decades ago. This is those lessons applied to autonomous AI. ”
– Ran Aroussi, Author