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• Oct 25 - Nov 7: $9.99+ (Early Access)
• Nov 8 - Nov 21: $14.99+ (Pre-Launch)
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• Dec 6+: $79.99 (Standard Price)

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Price increases every two weeks. ⏰

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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.

• Kindle edition: Amazon US
• Kindle edition: Amazon EU
• Paperback links coming soon