Adrian Solvek (12 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: new. Paperback. Software Engineering with AI Coding AgentsAI coding agents can generate code in seconds-but how do you make sure that code is correct, secure, maintainable, and actually ready for production?If you understand the basics of software development but feel unsure about bringing AI coding agents into real projects, this book gives you a practical path forward.Software Engineering with AI Coding Agents takes you beyond autocomplete, isolated prompts, and impressive-looking generated code. Instead, you will learn how to build reliable AI-assisted software development workflows in which coding agents can inspect repositories, make bounded changes, run tests, respond to feedback, work with Git, and contribute safely to modern software delivery.You do not need previous experience with autonomous coding agents, agent frameworks, or advanced AI concepts. The material develops progressively, helping you build confidence one engineering practice at a time. Mistakes are treated as useful signals-not failures to fear-and each chapter turns complex ideas into practical habits you can apply to real or sample repositories.Key FeaturesPractical, tool-neutral guidance that remains useful as AI products evolveReal-world approaches to repository readiness and context engineeringVerification-first methods for testing and reviewing AI-generated codeSecure permission boundaries, sandboxing, and human approval controlsGit, pull request, CI/CD, deployment, and observability workflowsPractical exercises, checklists, templates, and reference materialA disciplined approach to increasing agent autonomy safelyWhat You Will LearnYou will discover how to: Prepare software repositories for reliable AI-assisted developmentGive coding agents precise, relevant, and maintainable contextTurn vague requirements into bounded, verifiable engineering tasksGenerate code without losing architectural intent or maintainabilityUse tests, type checking, static analysis, and CI as deterministic feedbackProtect secrets, dependencies, infrastructure, and production environmentsReview AI-generated diffs and integrate changes safely through GitBuild controlled CI/CD and deployment workflowsScale agentic development across teams with governance and measurable outcomesWho This Book Is ForThis book is ideal for developers, backend and full-stack engineers, software architects, DevOps and platform engineers, technical leads, engineering managers, and other software professionals who already understand basic programming and development workflows but are new to AI coding agents and agentic software development.Table of ContentsChapter 1 - Engineering for Agentic Software DevelopmentChapter 2 - Building Agent-Ready Software RepositoriesChapter 3 - Context Engineering for Coding AgentsChapter 4 - Specifications and Agent-Executable Task DesignChapter 5 - Reliable AI-Assisted Code GenerationChapter 6 - Verification-First EngineeringChapter 7 - Security, Permissions, and Execution BoundariesChapter 8 - Git, Code Review, and Controlled IntegrationChapter 9 - CI/CD and AI-Assisted Software DeliveryChapter 10 - Scaling Agentic Development Across Engineering TeamsAI coding agents become far more useful when they operate inside strong engineering boundaries. Start building those boundaries today-and turn AI-assisted coding from an experiment into a reliable part of professional software Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Software Engineering with AI Coding AgentsAI coding agents can generate code in seconds-but how do you make sure that code is correct, secure, maintainable, and actually ready for production?If you understand the basics of software development but feel unsure about bringing AI coding agents into real projects, this book gives you a practical path forward.Software Engineering with AI Coding Agents takes you beyond autocomplete, isolated prompts, and impressive-looking generated code. Instead, you will learn how to build reliable AI-assisted software development workflows in which coding agents can inspect repositories, make bounded changes, run tests, respond to feedback, work with Git, and contribute safely to modern software delivery.You do not need previous experience with autonomous coding agents, agent frameworks, or advanced AI concepts. The material develops progressively, helping you build confidence one engineering practice at a time. Mistakes are treated as useful signals-not failures to fear-and each chapter turns complex ideas into practical habits you can apply to real or sample repositories.Key FeaturesPractical, tool-neutral guidance that remains useful as AI products evolveReal-world approaches to repository readiness and context engineeringVerification-first methods for testing and reviewing AI-generated codeSecure permission boundaries, sandboxing, and human approval controlsGit, pull request, CI/CD, deployment, and observability workflowsPractical exercises, checklists, templates, and reference materialA disciplined approach to increasing agent autonomy safelyWhat You Will LearnYou will discover how to: Prepare software repositories for reliable AI-assisted developmentGive coding agents precise, relevant, and maintainable contextTurn vague requirements into bounded, verifiable engineering tasksGenerate code without losing architectural intent or maintainabilityUse tests, type checking, static analysis, and CI as deterministic feedbackProtect secrets, dependencies, infrastructure, and production environmentsReview AI-generated diffs and integrate changes safely through GitBuild controlled CI/CD and deployment workflowsScale agentic development across teams with governance and measurable outcomesWho This Book Is ForThis book is ideal for developers, backend and full-stack engineers, software architects, DevOps and platform engineers, technical leads, engineering managers, and other software professionals who already understand basic programming and development workflows but are new to AI coding agents and agentic software development.Table of ContentsChapter 1 - Engineering for Agentic Software DevelopmentChapter 2 - Building Agent-Ready Software RepositoriesChapter 3 - Context Engineering for Coding AgentsChapter 4 - Specifications and Agent-Executable Task DesignChapter 5 - Reliable AI-Assisted Code GenerationChapter 6 - Verification-First EngineeringChapter 7 - Security, Permissions, and Execution BoundariesChapter 8 - Git, Code Review, and Controlled IntegrationChapter 9 - CI/CD and AI-Assisted Software DeliveryChapter 10 - Scaling Agentic Development Across Engineering TeamsAI coding agents become far more useful when they operate inside strong engineering boundaries. Start building those boundaries today-and turn AI-assisted coding from an experiment into a reliable part of professi Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. Neuware.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Build Better Software by Replacing Assumptions with Evidence.Software teams are surrounded by frameworks, tools, methodologies, productivity claims, and technical opinions. But the strongest engineering decisions are not based on trends or whoever speaks the loudest-they are based on measurable evidence, rapid feedback, disciplined experimentation, and a clear understanding of trade-offs.The Evidence-Driven Software Engineer: Practical Systems for Quality, Speed, and Continuous Delivery is a practical guide for developers, technical leads, engineering managers, architects, students, and growing software teams who want to improve how software is designed, tested, delivered, operated, and continuously improved.Rather than prescribing one "best" methodology, this book teaches you how to evaluate engineering practices in your own environment and determine what actually works.Inside, you will learn how to: Make better technical and architectural decisions using evidence instead of assumptionsMeasure delivery performance without falling into vanity metrics or developer surveillanceImprove software quality, maintainability, reliability, security, and changeabilityDesign testing strategies that provide faster and more useful feedbackBuild effective continuous integration and continuous delivery systemsReduce deployment risk through progressive delivery and reliable recovery practicesUse observability, SLOs, error budgets, and incident evidence to strengthen production reliabilityIntegrate security throughout the software-development lifecycleEvaluate AI-assisted development without blindly trusting productivity claims or generated codeBuild engineering teams that support experimentation, learning, psychological safety, and accountabilityIdentify bottlenecks and improve the entire software-delivery systemCreate practical 30-, 60-, and 90-day engineering improvement plansThe book also includes practical diagrams, decision frameworks, case-study scenarios, checklists, review questions, engineering metrics guidance, Architecture Decision Record templates, experiment-planning tools, incident-review resources, and an Evidence-Driven Engineering Toolkit designed for real-world application.Every major practice is examined through four essential questions: What does it mean?Why does it matter?How should it be applied?How can you tell whether it is actually working?Whether you are improving an established engineering organisation, modernising a legacy system, building a growing software product, leading a technical team, or preparing for professional software-development work, this book provides a structured framework for turning engineering decisions into measurable learning.Stop copying practices simply because successful companies use them.Start building an engineering system that can prove what works in your context.The Evidence-Driven Software Engineer is a practical roadmap for creating software systems-and engineering teams-that can deliver quality, speed, reliability, and continuous improvement with greater confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Build Better Software by Replacing Assumptions with Evidence.Software teams are surrounded by frameworks, tools, methodologies, productivity claims, and technical opinions. But the strongest engineering decisions are not based on trends or whoever speaks the loudest-they are based on measurable evidence, rapid feedback, disciplined experimentation, and a clear understanding of trade-offs.The Evidence-Driven Software Engineer: Practical Systems for Quality, Speed, and Continuous Delivery is a practical guide for developers, technical leads, engineering managers, architects, students, and growing software teams who want to improve how software is designed, tested, delivered, operated, and continuously improved.Rather than prescribing one "best" methodology, this book teaches you how to evaluate engineering practices in your own environment and determine what actually works.Inside, you will learn how to: Make better technical and architectural decisions using evidence instead of assumptionsMeasure delivery performance without falling into vanity metrics or developer surveillanceImprove software quality, maintainability, reliability, security, and changeabilityDesign testing strategies that provide faster and more useful feedbackBuild effective continuous integration and continuous delivery systemsReduce deployment risk through progressive delivery and reliable recovery practicesUse observability, SLOs, error budgets, and incident evidence to strengthen production reliabilityIntegrate security throughout the software-development lifecycleEvaluate AI-assisted development without blindly trusting productivity claims or generated codeBuild engineering teams that support experimentation, learning, psychological safety, and accountabilityIdentify bottlenecks and improve the entire software-delivery systemCreate practical 30-, 60-, and 90-day engineering improvement plansThe book also includes practical diagrams, decision frameworks, case-study scenarios, checklists, review questions, engineering metrics guidance, Architecture Decision Record templates, experiment-planning tools, incident-review resources, and an Evidence-Driven Engineering Toolkit designed for real-world application.Every major practice is examined through four essential questions: What does it mean?Why does it matter?How should it be applied?How can you tell whether it is actually working?Whether you are improving an established engineering organisation, modernising a legacy system, building a growing software product, leading a technical team, or preparing for professional software-development work, this book provides a structured framework for turning engineering decisions into measurable learning.Stop copying practices simply because successful companies use them.Start building an engineering system that can prove what works in your context.The Evidence-Driven Software Engineer is a practical roadmap for creating software systems-and engineering teams-that can deliver quality, speed, reliability, and continuous improvement with greater confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…