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Artículo: AMZ-B0FVBKP7W1
Production-Grade Agentic AI: From brittle workflows to deployable autonomous systems
Format:
Kindle
Detalles del producto
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Sin stock
Sin stock
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0.20 kg
0.20 kg
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No
No
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Nuevo
Nuevo
Producto de
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- 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. Those lessons are now applied to autonomous AI.” – Ran Aroussi, Author ----- 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 learnThe three pillars of true autonomy - What separates real agents from chatbots with tool accessMulti-tier memory architecture - Design systems that scale from buffer to persistent storageIntelligent orchestration and multi-agent coordination - Task decomposition and adaptive workflowsObservability for non-deterministic behavior - Track, debug, and audit autonomous systemsAvoid vendor lock-in - Multi-model routing with automatic failover and resilienceShip complete production examples - Real deployments, not toy demos 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 companies, he continues to champion pragmatic engineering and open infrastructure that actually works at scale.
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