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SKU/Artículo: AMZ-B0FSSYVHQ2
Agentic AI Systems for Developers: A Developer’s Guide for Designing, Debugging, and Scaling Production-Ready Multi-Agent Systems
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.64 kg
0.64 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Agentic AI Systems for Developers A Developer’s Guide for Designing, Debugging, and Scaling Production-Ready Multi-Agent Systems Intelligent agents are no longer a research experiment, they are the foundation of modern AI applications. But building production-ready agentic systems requires more than wiring a large language model to a few APIs. Developers need architectures, orchestration strategies, and debugging methods that scale. This book shows you how to design and deploy multi-agent systems that communicate, collaborate, and complete real-world workflows. You will learn how to move beyond toy demos into robust, enterprise-ready pipelines using frameworks like Claude Subagents, LangGraph, LangChain, and AutoGen. What You Will Learn Design agent lifecycles with planning, execution, memory, and verification stages Implement orchestration patterns including single-agent pipelines, multi-agent collaboration, and graph-based workflows Debug and monitor agent communication, state transitions, and error cascades Integrate with real tools and data through APIs, embeddings, and external knowledge bases Secure and govern systems with role-based access, tool whitelisting, and human-in-the-loop checkpoints Scale to production with fault tolerance, checkpointing, retries, and cost-optimized deployments Who This Book Is For Developers building intelligent assistants or domain-specific AI tools AI engineers designing agentic workflows for production systems Data scientists extending LLMs with orchestration, retrieval, and automation Researchers exploring communication, negotiation, and emergent behavior in agent teams Inside the Book Real-world case studies: customer support automation, SRE workflows, and research assistants Fully runnable Python implementations with LangGraph and LangChain Best practices checklists and common pitfalls with mitigation strategies Guidance on testing, observability, and compliance for enterprise contexts If you are ready to move beyond prompt engineering and build agentic AI systems that work together as teammates, this book will show you the way.
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U$S 43,98
55% OFF
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