Jorge hodnett (24 resultados)

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware - Python for AI Agents: Practical Programming for Building Intelligent and Autonomous Agents gives you a clear, practical path from basic Python concepts to building complete AI agent applications.Rather than treating agents as mysterious systems, this book shows you how their essential components work together. You will learn how Python controls the model, tools, APIs, memory, retrieval, workflows, and external services that make an agent useful.Inside, you will learn how to: - Build the core agent loop that manages decisions, actions, and results- Connect Python applications to large language models- Create safe, reusable Python functions that agents can use as tools- Work with external APIs and services- Add short-term and persistent memory to your agents- Build knowledge-aware agents with embeddings, vector search, and RAG- Design multi-step agent workflows with state, retries, and failure handling- Use the Model Context Protocol (MCP) to connect agents with tools and resources- Build specialized agents that communicate, delegate, and coordinate tasks- Protect APIs, credentials, tools, memory, and sensitive data- Test agent decisions, tools, workflows, and unexpected behavior- Add logging and monitoring while controlling performance and model costs- Deploy and maintain a production-ready Python agentThe book focuses on the engineering behind AI agents instead of locking you into one framework. You will see practical Python patterns and code examples that help you understand what is happening beneath higher-level agent libraries. This gives you knowledge you can continue using even as models, frameworks, and development tools change.You will also learn an important principle of reliable agent development: the model can reason and propose actions, but your Python application should control what is actually allowed to happen. This approach helps you build agents that are easier to test, secure, debug, and maintain.Whether you are a Python developer exploring agentic AI, an AI developer moving beyond simple chat applications, or a programmer who wants to understand how autonomous agents are actually built, this book gives you a practical foundation you can build on.You will also receive useful quick-reference material covering Python for agent development, common agent patterns, troubleshooting, security and testing checks, and a production-readiness checklist.Stop piecing together disconnected tutorials and start understanding how complete AI agents are built. Get your copy of Python for AI Agents today and start building intelligent Python agents that can reason, use tools, retrieve knowledge, coordinate tasks, and operate as real software applications.…

Paperback. Condición: new. Paperback. AI Coding Assistants for Developers is a practical guide to using AI throughout the software development process without giving up the technical judgment that good programming requires. Rather than teaching you to copy whatever an AI tool produces, this book shows you how to give better coding instructions, provide useful project context, evaluate generated code, find mistakes, and verify results before they become part of a real application. The book treats AI as a development tool that can increase your capabilities, not as a replacement for programming knowledge.You will learn how AI coding assistants work, what they can and cannot reliably do with code, and how to choose an assistant that fits your development work. You will move from basic AI-assisted code generation to practical tasks involving functions, classes, APIs, databases, existing codebases, debugging, refactoring, testing, and code review.Inside, you will learn how to: Give AI clear programming instructions that reduce incorrect assumptionsProvide source code, requirements, constraints, errors, and project context effectivelyGenerate useful functions, classes, application logic, APIs, and database codeUnderstand unfamiliar projects and trace dependencies across existing codebasesUse AI to investigate errors, logs, stack traces, and difficult bugsFind root causes instead of accepting quick fixes that only hide problemsRefactor duplicated, complex, and older code while protecting existing behaviorGenerate unit and integration tests and identify important edge casesReview AI-generated code for bugs, security weaknesses, performance problems, and maintainability issuesWork effectively with AI assistants inside editors, IDEs, repositories, and terminalsUnderstand how coding agents plan tasks, edit files, run commands, and test their own changesProtect source code, credentials, private information, and other sensitive development dataDetect incorrect APIs, unsafe dependencies, weak code, and other convincing AI mistakesBuild a practical workflow that combines requirements, coding, debugging, testing, and reviewIncrease development productivity while keeping important decisions under developer controlThe explanations are practical and supported by programming examples that show how these techniques apply to real development work. You will see why generated code should be treated as a proposed implementation and checked against requirements, APIs, project conventions, tests, and actual application behavior before it is accepted. Whether you are a beginning developer learning how to use AI responsibly, an experienced programmer looking to work faster, or a technical professional introducing AI into an existing development workflow, this book gives you a structured way to use these tools with greater confidence and control.AI-assisted programming is becoming an important part of modern software development. The advantage will not simply belong to developers who can generate the most code. It will belong to developers who know how to give AI the right context, recognize weak output, verify important behavior, and turn AI assistance into reliable working software.Build those skills now. Get your copy of AI Coding Assistants for Developers and start using AI to write, debug, test, review, and improve code while keeping yourself in control of the final result. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Browser AI Agent Engineering is a practical guide to designing and building intelligent agents that can interact with the web through controlled browser automation. Instead of focusing only on simple scripts that follow fixed steps, this book shows you how to build agents that can observe web pages, make decisions, choose tools, perform actions, remember useful information, verify results, and adapt when a workflow does not go exactly as planned.You will learn how browser agents differ from traditional browser automation and how the Observe, Decide, Act, and Verify loop gives an agent the ability to handle changing web tasks. The book explains how to work with the DOM, accessibility information, buttons, links, forms, dynamic pages, tabs, downloads, and other browser elements that agents must understand.You will also learn how to design browser tools, implement structured tool calling, manage task state and short-term memory, control model context, break large goals into smaller tasks, and build reliable multi-step web workflows. Practical Python and browser automation examples help connect the concepts to real implementation.Reliability and security receive serious attention. You will learn how to handle missing elements, timeouts, failed actions, retries, repeated loops, and verification failures. You will also see how to protect credentials and browser sessions, restrict dangerous actions, control agent permissions, require human approval when necessary, and defend browser agents against prompt injection and unsafe web content.Inside the book, you will learn how to: Build the core architecture of a browser AI agentConnect AI decision-making with browser automationRead and interpret web pages for agent useDesign safe and reusable browser toolsBuild autonomous agent loops with clear stopping conditionsManage state, memory, and model context across multiple stepsCreate reusable multi-page browser workflowsHandle failures with retries, recovery, and alternative actionsVerify results instead of assuming an action succeededProtect credentials, sessions, tools, and sensitive informationReduce risks from prompt injection and malicious web contentTest complete browser-agent workflowsLog decisions, tool calls, failures, and recovery attemptsMeasure task success, reliability, speed, and model costBuild and prepare a complete browser agent for productionWhether you are an AI developer, Python programmer, automation engineer, software engineer, or technical builder interested in autonomous agents, this book gives you a structured path from basic browser interaction to reliable agent engineering.AI is rapidly moving beyond chat interfaces toward systems that can take useful actions. Developers who understand how to combine AI reasoning with browsers, tools, memory, security, verification, and reliable software engineering will be better prepared to build the next generation of practical AI applications. Do not stop at building AI that can only tell users what to do. Learn how to engineer controlled agents that can navigate the web, use tools, adapt to changing situations, and complete useful browser workflows.Get your copy of Browser AI Agent Engineering today and start building browser agents that can observe, decide, act, verify, recover, and reliably turn user goals into real web actions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Python for AI Agents: Practical Programming for Building Intelligent and Autonomous Agents gives you a clear, practical path from basic Python concepts to building complete AI agent applications.Rather than treating agents as mysterious systems, this book shows you how their essential components work together. You will learn how Python controls the model, tools, APIs, memory, retrieval, workflows, and external services that make an agent useful.Inside, you will learn how to: - Build the core agent loop that manages decisions, actions, and results- Connect Python applications to large language models- Create safe, reusable Python functions that agents can use as tools- Work with external APIs and services- Add short-term and persistent memory to your agents- Build knowledge-aware agents with embeddings, vector search, and RAG- Design multi-step agent workflows with state, retries, and failure handling- Use the Model Context Protocol (MCP) to connect agents with tools and resources- Build specialized agents that communicate, delegate, and coordinate tasks- Protect APIs, credentials, tools, memory, and sensitive data- Test agent decisions, tools, workflows, and unexpected behavior- Add logging and monitoring while controlling performance and model costs- Deploy and maintain a production-ready Python agentThe book focuses on the engineering behind AI agents instead of locking you into one framework. You will see practical Python patterns and code examples that help you understand what is happening beneath higher-level agent libraries. This gives you knowledge you can continue using even as models, frameworks, and development tools change.You will also learn an important principle of reliable agent development: the model can reason and propose actions, but your Python application should control what is actually allowed to happen. This approach helps you build agents that are easier to test, secure, debug, and maintain.Whether you are a Python developer exploring agentic AI, an AI developer moving beyond simple chat applications, or a programmer who wants to understand how autonomous agents are actually built, this book gives you a practical foundation you can build on.You will also receive useful quick-reference material covering Python for agent development, common agent patterns, troubleshooting, security and testing checks, and a production-readiness checklist.Stop piecing together disconnected tutorials and start understanding how complete AI agents are built. Get your copy of Python for AI Agents today and start building intelligent Python agents that can reason, use tools, retrieve knowledge, coordinate tasks, and operate as real software applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. AI Coding Assistants for Developers is a practical guide to using AI throughout the software development process without giving up the technical judgment that good programming requires. Rather than teaching you to copy whatever an AI tool produces, this book shows you how to give better coding instructions, provide useful project context, evaluate generated code, find mistakes, and verify results before they become part of a real application. The book treats AI as a development tool that can increase your capabilities, not as a replacement for programming knowledge.You will learn how AI coding assistants work, what they can and cannot reliably do with code, and how to choose an assistant that fits your development work. You will move from basic AI-assisted code generation to practical tasks involving functions, classes, APIs, databases, existing codebases, debugging, refactoring, testing, and code review.Inside, you will learn how to: Give AI clear programming instructions that reduce incorrect assumptionsProvide source code, requirements, constraints, errors, and project context effectivelyGenerate useful functions, classes, application logic, APIs, and database codeUnderstand unfamiliar projects and trace dependencies across existing codebasesUse AI to investigate errors, logs, stack traces, and difficult bugsFind root causes instead of accepting quick fixes that only hide problemsRefactor duplicated, complex, and older code while protecting existing behaviorGenerate unit and integration tests and identify important edge casesReview AI-generated code for bugs, security weaknesses, performance problems, and maintainability issuesWork effectively with AI assistants inside editors, IDEs, repositories, and terminalsUnderstand how coding agents plan tasks, edit files, run commands, and test their own changesProtect source code, credentials, private information, and other sensitive development dataDetect incorrect APIs, unsafe dependencies, weak code, and other convincing AI mistakesBuild a practical workflow that combines requirements, coding, debugging, testing, and reviewIncrease development productivity while keeping important decisions under developer controlThe explanations are practical and supported by programming examples that show how these techniques apply to real development work. You will see why generated code should be treated as a proposed implementation and checked against requirements, APIs, project conventions, tests, and actual application behavior before it is accepted. Whether you are a beginning developer learning how to use AI responsibly, an experienced programmer looking to work faster, or a technical professional introducing AI into an existing development workflow, this book gives you a structured way to use these tools with greater confidence and control.AI-assisted programming is becoming an important part of modern software development. The advantage will not simply belong to developers who can generate the most code. It will belong to developers who know how to give AI the right context, recognize weak output, verify important behavior, and turn AI assistance into reliable working software.Build those skills now. Get your copy of AI Coding Assistants for Developers and start using AI to write, debug, test, review, and improve code while keeping yourself in control of the final result. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Browser AI Agent Engineering is a practical guide to designing and building intelligent agents that can interact with the web through controlled browser automation. Instead of focusing only on simple scripts that follow fixed steps, this book shows you how to build agents that can observe web pages, make decisions, choose tools, perform actions, remember useful information, verify results, and adapt when a workflow does not go exactly as planned.You will learn how browser agents differ from traditional browser automation and how the Observe, Decide, Act, and Verify loop gives an agent the ability to handle changing web tasks. The book explains how to work with the DOM, accessibility information, buttons, links, forms, dynamic pages, tabs, downloads, and other browser elements that agents must understand.You will also learn how to design browser tools, implement structured tool calling, manage task state and short-term memory, control model context, break large goals into smaller tasks, and build reliable multi-step web workflows. Practical Python and browser automation examples help connect the concepts to real implementation.Reliability and security receive serious attention. You will learn how to handle missing elements, timeouts, failed actions, retries, repeated loops, and verification failures. You will also see how to protect credentials and browser sessions, restrict dangerous actions, control agent permissions, require human approval when necessary, and defend browser agents against prompt injection and unsafe web content.Inside the book, you will learn how to: Build the core architecture of a browser AI agentConnect AI decision-making with browser automationRead and interpret web pages for agent useDesign safe and reusable browser toolsBuild autonomous agent loops with clear stopping conditionsManage state, memory, and model context across multiple stepsCreate reusable multi-page browser workflowsHandle failures with retries, recovery, and alternative actionsVerify results instead of assuming an action succeededProtect credentials, sessions, tools, and sensitive informationReduce risks from prompt injection and malicious web contentTest complete browser-agent workflowsLog decisions, tool calls, failures, and recovery attemptsMeasure task success, reliability, speed, and model costBuild and prepare a complete browser agent for productionWhether you are an AI developer, Python programmer, automation engineer, software engineer, or technical builder interested in autonomous agents, this book gives you a structured path from basic browser interaction to reliable agent engineering.AI is rapidly moving beyond chat interfaces toward systems that can take useful actions. Developers who understand how to combine AI reasoning with browsers, tools, memory, security, verification, and reliable software engineering will be better prepared to build the next generation of practical AI applications. Do not stop at building AI that can only tell users what to do. Learn how to engineer controlled agents that can navigate the web, use tools, adapt to changing situations, and complete useful browser workflows.Get your copy of Browser AI Agent Engineering today and start building browser agents that can observe, decide, act, verify, recover, and reliably turn user goals into real web actions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Python for AI Agents: Practical Programming for Building Intelligent and Autonomous Agents gives you a clear, practical path from basic Python concepts to building complete AI agent applications.Rather than treating agents as mysterious systems, this book shows you how their essential components work together. You will learn how Python controls the model, tools, APIs, memory, retrieval, workflows, and external services that make an agent useful.Inside, you will learn how to: - Build the core agent loop that manages decisions, actions, and results- Connect Python applications to large language models- Create safe, reusable Python functions that agents can use as tools- Work with external APIs and services- Add short-term and persistent memory to your agents- Build knowledge-aware agents with embeddings, vector search, and RAG- Design multi-step agent workflows with state, retries, and failure handling- Use the Model Context Protocol (MCP) to connect agents with tools and resources- Build specialized agents that communicate, delegate, and coordinate tasks- Protect APIs, credentials, tools, memory, and sensitive data- Test agent decisions, tools, workflows, and unexpected behavior- Add logging and monitoring while controlling performance and model costs- Deploy and maintain a production-ready Python agentThe book focuses on the engineering behind AI agents instead of locking you into one framework. You will see practical Python patterns and code examples that help you understand what is happening beneath higher-level agent libraries. This gives you knowledge you can continue using even as models, frameworks, and development tools change.You will also learn an important principle of reliable agent development: the model can reason and propose actions, but your Python application should control what is actually allowed to happen. This approach helps you build agents that are easier to test, secure, debug, and maintain.Whether you are a Python developer exploring agentic AI, an AI developer moving beyond simple chat applications, or a programmer who wants to understand how autonomous agents are actually built, this book gives you a practical foundation you can build on.You will also receive useful quick-reference material covering Python for agent development, common agent patterns, troubleshooting, security and testing checks, and a production-readiness checklist.Stop piecing together disconnected tutorials and start understanding how complete AI agents are built. Get your copy of Python for AI Agents today and start building intelligent Python agents that can reason, use tools, retrieve knowledge, coordinate tasks, and operate as real software applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…