Nolan m stark (10 Ergebnisse)

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Taschenbuch. Zustand: Neu. Neuware - AI becomes far more useful when it is treated as a working partner rather than a machine that simply answers questions.Mastering Claude AI for Professional Work is a practical, non-technical guide to using Claude for thinking, learning, writing, research, problem-solving, decision-making, and everyday professional work.You will begin with a clear understanding of what Claude is, what it does well, where it can fail, and how to communicate with it effectively. The book teaches you how to provide context, break large requests into smaller pieces, iterate on answers, and treat the first response as a draft rather than an unquestioned result.The book then shows how to use Claude as a reasoning partner. You will learn how to stress-test ideas, examine alternative perspectives, identify blind spots, learn new concepts, create better content, summarize information without losing meaning, work with data, organize research, and build useful documents and reports.The later chapters focus on real professional problem-solving. Learn how to identify the underlying problem, compare options, pressure-test decisions, manage uncertainty, avoid overconfidence, and develop daily and weekly habits that make AI a natural part of your work.This book does not require programming knowledge. It is designed for professionals who want to use Claude effectively while keeping their own judgment, accountability, and voice.For managers, analysts, consultants, educators, researchers, writers, entrepreneurs, and other knowledge workers, this is a practical guide to making AI a useful part of everyday professional thinking and work. …

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Taschenbuch. Zustand: Neu. Neuware - AI assistants answer questions. Autonomous AI agents are designed to pursue goals, make decisions, use tools, observe results, and continue working across multiple steps.Building Autonomous AI Agents with Claude AI is a practical guide to designing, building, evaluating, and operating AI agents that can perform complex tasks with controlled autonomy.You will begin by learning what actually makes a system an AI agent, how agents differ from chatbots and ordinary API calls, and when an agent is genuinely necessary. The book introduces the autonomy spectrum and shows how to choose an appropriate level of independence based on risk, reversibility, and the cost of human review.You will then learn the reason-act-observe loop behind autonomous agents. The book explains how agents decide what to do, call tools, receive results, continue or stop, and handle failures. You will learn how to design purpose-built tools, create reliable schemas, use the Model Context Protocol, and test tools before allowing an agent to operate with them.The next stage covers AI agent orchestration and complex multi-step tasks. Learn how to break goals into executable steps, delegate work to sub-agents, choose between plan-then-execute and interleaved planning, manage persistent state, distinguish memory from context, prevent state drift, resume long-running tasks, and handle failures without creating worse outcomes.The book then focuses on AI agent safety and production engineering: permissions, least privilege, approval gates, sandboxing, human-in-the-loop controls, audit logs, evaluation datasets, regression testing, human baselines, observability, cost management, latency, incremental deployment, model upgrades, and behavioral drift.This book is written for developers, AI engineers, software architects, technical founders, and builders who want to move beyond simple prompts and create autonomous AI agents capable of performing real multi-step work.The goal is not maximum autonomy.The goal is useful autonomy that can be understood, evaluated, controlled, and trusted. …