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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
Ran Aroussi is a self-taught software engineer with 30+ years of experience 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.
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.
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