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Zustand: New. In.

Zustand: New. In.

Hardcover. Zustand: Very Good. London, no publication date stated, c. 1881; VOLUME II ONLY; gold cloth covered boards; beveled edges; edge and corner wear; finger smudges on covers; no jacket; 8vo - over 7 3/4" to 9 3/4" tall; begins with chapter XXI; frontispiece detached but present; interior clean and unmarked; 360 pages.

Zustand: New. In.

Zustand: New. In.

Zustand: New.

Zustand: New. KlappentextrnrnThis scarce antiquarian book is a facsimile reprint of the original. Due to its age, it may contain imperfections such as marks, notations, marginalia and flawed pages. Because we believe this work is culturally important, we have.

Taschenbuch. Zustand: Neu. Neuware - Agentic AI Systems for DevelopersA Developer's Guide for Designing, Debugging, and Scaling Production-Ready Multi-Agent SystemsIntelligent 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 LearnDesign agent lifecycles with planning, execution, memory, and verification stagesImplement orchestration patterns including single-agent pipelines, multi-agent collaboration, and graph-based workflowsDebug and monitor agent communication, state transitions, and error cascadesIntegrate with real tools and data through APIs, embeddings, and external knowledge basesSecure and govern systems with role-based access, tool whitelisting, and human-in-the-loop checkpointsScale to production with fault tolerance, checkpointing, retries, and cost-optimized deploymentsWho This Book Is ForDevelopers building intelligent assistants or domain-specific AI toolsAI engineers designing agentic workflows for production systemsData scientists extending LLMs with orchestration, retrieval, and automationResearchers exploring communication, negotiation, and emergent behavior in agent teamsInside the BookReal-world case studies: customer support automation, SRE workflows, and research assistantsFully runnable Python implementations with LangGraph and LangChainBest practices checklists and common pitfalls with mitigation strategiesGuidance on testing, observability, and compliance for enterprise contextsIf 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.