THE BOOK FOR ENGINEERS SHIPPING REAL SYSTEMS

Production-Grade Agentic AI

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.

“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

665 Pages: Deep technical content

21 Chapters: Foundation to deployment

Production examples: Not toy demos

Framework-agnostic: Universal principles

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

Table of Contents

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.

More about me:

Production-Grade Agentic AI

From brittle workflows to deployable autonomous systems

Book description

What you'll learn

About the Author

Get the first three chapters