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You can use this page to email Ran Aroussi about Production-Grade Agentic Al.

About the Book

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This comprehensive book is being written in public. Already 40% done!

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About the book

Most AI systems today fail in production. They chain prompts together and call it "agentic". They rely on a dozen stitched-together tools. They collapse under real-world pressure. The gap between demo and production remains enormous, not because the technology isn't ready, but because the infrastructure doesn't exist.

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

Foundation: Understanding True Agency

The three pillars that distinguish autonomous agents from chatbots with tools
Why most "agentic" systems are just brittle prompt chains
The architectural decisions that enable real autonomy

Memory Systems That Scale

Multi-tier memory architecture: working, episodic, and semantic
Production-ready semantic search with vector databases
Memory lifecycle management and retrieval strategies
Handling context windows and memory pruning

Intelligent Orchestration

Goal decomposition and task planning that adapts to complexity
Multi-agent coordination patterns and communication protocols
Failure handling, retries, and graceful degradation
Loop detection and infinite recursion prevention

Production Observability

Reasoning transparency: understanding what agents are thinking
Cost attribution across agent interactions and model calls
Audit trails for compliance and debugging
Performance monitoring and bottleneck identification

Deployment at Scale

Declarative configuration for reproducible deployments
Automatic failover and circuit breakers
Horizontal scaling patterns for agent workloads
Resource management and rate limiting

Multi-Model Infrastructure

Avoiding vendor lock-in through abstraction layers
Intelligent routing based on task complexity and cost
Fallback strategies when primary models fail
Testing across multiple model providers

From Theory to Practice

Real-world production examples and case studies
Common failure modes and how to prevent them
Performance optimization techniques
Cost management strategies for production deployments

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Who This Book Is For

This book is for engineers, architects, and technical leads building AI systems beyond the prototype stage. Whether you're scaling an existing agent system or starting fresh, you'll gain the patterns and practices needed to build reliable autonomous AI infrastructure.

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About the Author

Written by Ran Aroussi, a 30+ year tech veteran, entrepreneur, and founder of Automaze. His open-source libraries receive over 10 million downloads monthly, including yfinance (the de facto standard for financial data in Python) and an upcoming AI infrastructure tooling.

Ran has led engineering teams across finance, ad-tech, data infrastructure, and AI deployment. He's built production systems that handle billions of requests, managed distributed teams, and architected infrastructure that scales. This book distills decades of experience into practical patterns for building AI systems that work in the real world.

This version:

Breaks content into scannable sections with clear headers
Adds more specific technical details under each bullet
Includes a "Who This Book Is For" section
Enhances author credibility with specific achievements
Maintains readability while adding depth

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Prefer a Hard Copy?

While this LeanPub edition gives you immediate access to the book as it's written, a print edition will be available on Amazon. Pre-order the hardcover now to secure your physical copy when it launches in November.

About the Author

Ran Aroussi

@aroussi

Helping people work smarter. CTO as a Service @ automaze.io.

Ran Aroussi, a 30+ year tech veteran, entrepreneur, and founder of Automaze. His open-source libraries receive 10M+ downloads monthly. Ran led engineering teams across finance, data infrastructure, and AI deployment, and has extensive experience building production systems that scale.