Practical Applications Intelligent Systems (138 results)
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Taschenbuch. Condition: Neu. Neuware - Building a useful AI application requires more than getting a language model to produce an impressive response. A dependable system needs clear instructions, controlled context, reliable tool use, useful retrieval, testing, guardrails, and production monitoring.Building Intelligent Applicat…ions with Claude AI provides a practical path from the first API request to production-ready AI software.You will learn how to design system prompts that produce consistent behavior, manage multi-turn conversations, work with growing context, and handle long-running sessions. The book then moves into tool use, multi-tool assistants, document retrieval, grounded responses, source citation, and the architecture behind modern AI agents.You will also learn how to design agentic workflows, coordinate multiple agents, create guardrails, introduce human checkpoints, handle escalation, and decide when autonomy is appropriate.The final chapters focus on the engineering work that determines whether an AI feature survives real users: testing non-deterministic behavior, separating infrastructure failures from reasoning failures, handling rate limits, reducing latency, managing token costs, monitoring quality, and preparing applications to scale.Designed for developers, technical founders, product engineers, AI builders, and software architects, this book focuses on practical AI application development rather than theoretical machine learning.From assistants and automation tools to retrieval systems and multi-step agents, the goal is to help you build AI software that is useful, testable, controlled, and ready for real-world use.
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Taschenbuch. Condition: Neu. Neuware.
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Taschenbuch. Condition: Neu. Neuware - AI Agent Projects with Python: Build Practical AI Assistants, Research Agents, Automation Tools, Multi-Agent Systems and Intelligent ApplicationsYou have learned some Python. You have tried AI APIs. Maybe you have even built a chatbot. But when it is time to build an AI agent that can actua…lly use tools, remember information, research a topic, or complete a task, everything suddenly feels more complicated.Which tools should you use How do you connect an AI model to Python functions How does an agent know what action to take next How do you add memory, RAG, automation, or multiple agents without getting lost in confusing documentation and scattered tutorials AI Agent Projects with Python gives you a practical way forward. Instead of filling pages with theory, this book teaches AI agent development by helping you build working projects step by step. You start with the basic structure of an agent and gradually move toward more capable intelligent applications.Inside, you will learn how to build a Python AI assistant, connect language models to tools, manage conversation history, create structured responses, and handle common errors. You will work with Retrieval-Augmented Generation (RAG) to connect agents to external knowledge and add memory so they can preserve useful information across interactions.As your skills grow, you will build research agents, automation agents, data analysis agents, and multi-agent systems. You will see how specialized agents can share work, manage information, and cooperate on larger tasks. You will also bring tools, retrieval, memory, application state, and user interaction together in a more complete intelligent application.The practical examples show you what the code is doing and why it works. You will also learn important development habits that can save you from costly mistakes, including input validation, permissions, execution limits, error handling, human approval, and safe API key management. The book does not stop when your agent starts working. You will learn how to test, evaluate, secure, monitor, and prepare AI agent applications for production. Topics such as logging, tracing, failures, retries, rate limits, cost management, and reliability help you understand what it takes to move beyond a small experiment.You do not need to be an AI researcher. Basic Python knowledge is enough to get started, and important concepts are explained as they appear. The goal is not to make you copy large blocks of code without understanding them. It is to help you develop practical skills you can reuse in your own AI projects. AI agents are quickly becoming an important part of modern software development. Do not spend months jumping between tutorials and trying to piece everything together on your own.Get your copy of AI Agent Projects with Python today and start building practical AI agents one project at a time.
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Intelligent Systems and Sustainable Computational Models : Concepts, Architecture, and Practical Applications
Nagarajan, Rajganesh (EDT); Narayanasamy, Senthil Kumar (EDT); Thirunavukarasu, Ramkumar (EDT); Raj, Pethuru (EDT)
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Language: English
Published by Independently published, 2026
Series: Book 1 of 2 - Practical Claude AI Development Series
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Intelligent Systems and Sustainable Computational Models : Concepts, Architecture, and Practical Applications
Nagarajan, Rajganesh (EDT); Narayanasamy, Senthil Kumar (EDT); Thirunavukarasu, Ramkumar (EDT); Raj, Pethuru (EDT)
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Intelligent Systems and Sustainable Computational Models : Concepts, Architecture, and Practical Applications
Nagarajan, Rajganesh (EDT); Narayanasamy, Senthil Kumar (EDT); Thirunavukarasu, Ramkumar (EDT); Raj, Pethuru (EDT)
- Softcover
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- Softcover
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- Softcover
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Taschenbuch. Condition: Neu. Neuware - Most AI books teach prompts.This book teaches systemsAI agents are no longer simple chatbots. Modern AI systems can reason, plan, use tools, collaborate with other agents, and execute complex workflows with minimal human intervention. Building these systems requires a new engineering discip…line-one that combines software architecture, orchestration, memory design, reliability engineering, and autonomous decision-making.What Readers Learn- In this book you'll learn: - What makes a system truly agentic- Agent architecture design patterns- Memory systems and retrieval architectures- Tool-using AI agents- Multi-agent orchestration strategies- Reliability, observability, and safety engineering- Production deployment patterns- Real-world automation blueprints- Research, content, business, productivity, and sales agent systemsWho This Book Is For- This book is ideal for: - Software Engineers- AI Engineers- Architects- Technical Founders- Product Leaders- AI ConsultantsWhether you're building your first autonomous workflow or designing enterprise-scale multi-agent systems, this book provides the practical frameworks, implementation patterns, and production lessons needed to build reliable AI agent systems that work beyond the demo.
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- Softcover
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Intelligent Systems and Sustainable Computational Models : Concepts, Architecture, and Practical Applications
Nagarajan, Rajganesh (EDT); Narayanasamy, Senthil Kumar (EDT); Thirunavukarasu, Ramkumar (EDT); Raj, Pethuru (EDT)
- Softcover
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- Softcover
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- Softcover
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- Softcover
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Published by Independently Published Aug 2026, 2026
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Neuware - Build smarter AI applications from the ground up.Large Language Models are transforming software development, but knowing how to use an LLM API is only the beginning. Building reliable, intelligent, production-ready AI applications requires a deeper understanding of retrieval, agents, workf…lows, tools, evaluation, security, and deployment.LLM APPLICATION ENGINEERING provides a practical introduction to designing and building modern LLM-powered applications with technologies such as LangChain and LangGraph.Inside, you'll learn how to: Understand the foundations of LLM application engineeringBuild Retrieval-Augmented Generation (RAG) systemsWork with embeddings and vector databasesCreate AI agents and agentic workflowsBuild stateful workflows with LangGraphDesign multi-agent AI systemsImplement human-in-the-loop applicationsEvaluate LLM applications and improve reliabilityHandle errors, hallucinations, and unexpected behaviorApply security and responsible AI principlesOptimize AI applications for cost, speed, and performanceDeploy and monitor applications in productionBuild practical AI projects, including research assistants, knowledge assistants, autonomous agents, and multi-agent workflowsMove an AI prototype toward a production-ready solutionWhether you're exploring LLM development for the first time or looking to strengthen your understanding of modern AI application architecture, this book focuses on the concepts and patterns that can help you turn powerful language models into useful software systems.Learn the architecture. Build the workflow. Connect the tools. Create intelligent applications.
- Softcover
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- Softcover
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- Softcover
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- Softcover
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