Cassani alexio (13 resultados)

Paperback or Softback. Condición: New. Code Revealed: A practical guide to AI agents, workflows, and modern application practices. Book.

Condición: New.

Paperback. Condición: New. Bridge the gap between AI hype and software reality by learning how to evaluate agents, redesign team responsibilities, and introduce structured controls that improve delivery without sacrificing clarity, safety, or maintainability.Key FeaturesUse RACM to match AI capabilities to SDLC tasks and supervision needsApply Execution Plans and Logbooks to make agent work visible and auditableSet autonomy limits, guardrails, and review practices for safer adoptionBook DescriptionCode Revealed is a practical guide for teams learning to work with AI agents in real delivery environments. Rather than treating AI as a coding shortcut, it shows how to introduce it as a managed capability across the software lifecycle.It explains how agents differ from assistants, why evaluation matters when selecting tools, and how development changes when intent, supervision, and validation become more important than manual implementation.You will learn how to use frameworks such as RACM to assess capability, Context Engineering to improve reliability, and PAIP to introduce repeatable integration patterns. The book also explains why Execution Plans and Logbooks matter when delegating work to agents, giving teams a way to align before action and review what happened afterward.Beyond process, the book examines team redesign, new specialist roles, and the shift from directing people alone to orchestrating human and artificial contributors together.It also addresses difficult issues often overlooked in AI adoption, including code churn, weak oversight, security exposure, opaque decisions, and the long-term cost of unmanaged speed. The result is a practical roadmap for adopting AI with discipline, transparency, and measurable intent.What you will learnDistinguish agents from simpler AI coding assistantsAssess tool fit using capability and autonomy criteriaStructure prompts through richer Context EngineeringUse plans and logs to supervise non-trivial AI tasksDesign workflows for prototyping, refactoring, and QAPrevent hidden risk from churn, bias, and hallucinationsReorganize teams around emerging AI-native rolesBuild skills for orchestration, review, and governanceWho this book is forThis book is for developers, tech leads, architects, and engineering managers who are actively building and delivering software while adapting to AI-driven change. It is especially valuable for mid-level and senior developers working across web, backend, and platform systems who want to stay relevant as their role shifts from writing code to guiding and validating AI-generated work.…

Paperback. Condición: Brand New. 334 pages. 6.14x0.76x9.21 inches. In Stock.

Paperback. Condición: Brand New. 334 pages. 6.14x0.76x9.21 inches. In Stock.

Paperback. Condición: New. Bridge the gap between AI hype and software reality by learning how to evaluate agents, redesign team responsibilities, and introduce structured controls that improve delivery without sacrificing clarity, safety, or maintainability.Key FeaturesUse RACM to match AI capabilities to SDLC tasks and supervision needsApply Execution Plans and Logbooks to make agent work visible and auditableSet autonomy limits, guardrails, and review practices for safer adoptionBook DescriptionCode Revealed is a practical guide for teams learning to work with AI agents in real delivery environments. Rather than treating AI as a coding shortcut, it shows how to introduce it as a managed capability across the software lifecycle.It explains how agents differ from assistants, why evaluation matters when selecting tools, and how development changes when intent, supervision, and validation become more important than manual implementation.You will learn how to use frameworks such as RACM to assess capability, Context Engineering to improve reliability, and PAIP to introduce repeatable integration patterns. The book also explains why Execution Plans and Logbooks matter when delegating work to agents, giving teams a way to align before action and review what happened afterward.Beyond process, the book examines team redesign, new specialist roles, and the shift from directing people alone to orchestrating human and artificial contributors together.It also addresses difficult issues often overlooked in AI adoption, including code churn, weak oversight, security exposure, opaque decisions, and the long-term cost of unmanaged speed. The result is a practical roadmap for adopting AI with discipline, transparency, and measurable intent.What you will learnDistinguish agents from simpler AI coding assistantsAssess tool fit using capability and autonomy criteriaStructure prompts through richer Context EngineeringUse plans and logs to supervise non-trivial AI tasksDesign workflows for prototyping, refactoring, and QAPrevent hidden risk from churn, bias, and hallucinationsReorganize teams around emerging AI-native rolesBuild skills for orchestration, review, and governanceWho this book is forThis book is for developers, tech leads, architects, and engineering managers who are actively building and delivering software while adapting to AI-driven change. It is especially valuable for mid-level and senior developers working across web, backend, and platform systems who want to stay relevant as their role shifts from writing code to guiding and validating AI-generated work.…

Paperback. Condición: new. Paperback. Bridge the gap between AI hype and software reality by learning how to evaluate agents, redesign team responsibilities, and introduce structured controls that improve delivery without sacrificing clarity, safety, or maintainability.Key FeaturesUse RACM to match AI capabilities to SDLC tasks and supervision needsApply Execution Plans and Logbooks to make agent work visible and auditableSet autonomy limits, guardrails, and review practices for safer adoptionBook DescriptionCode Revealed is a practical guide for teams learning to work with AI agents in real delivery environments. Rather than treating AI as a coding shortcut, it shows how to introduce it as a managed capability across the software lifecycle.It explains how agents differ from assistants, why evaluation matters when selecting tools, and how development changes when intent, supervision, and validation become more important than manual implementation.You will learn how to use frameworks such as RACM to assess capability, Context Engineering to improve reliability, and PAIP to introduce repeatable integration patterns. The book also explains why Execution Plans and Logbooks matter when delegating work to agents, giving teams a way to align before action and review what happened afterward.Beyond process, the book examines team redesign, new specialist roles, and the shift from directing people alone to orchestrating human and artificial contributors together.It also addresses difficult issues often overlooked in AI adoption, including code churn, weak oversight, security exposure, opaque decisions, and the long-term cost of unmanaged speed. The result is a practical roadmap for adopting AI with discipline, transparency, and measurable intent.What you will learnDistinguish agents from simpler AI coding assistantsAssess tool fit using capability and autonomy criteriaStructure prompts through richer Context EngineeringUse plans and logs to supervise non-trivial AI tasksDesign workflows for prototyping, refactoring, and QAPrevent hidden risk from churn, bias, and hallucinationsReorganize teams around emerging AI-native rolesBuild skills for orchestration, review, and governanceWho this book is forThis book is for developers, tech leads, architects, and engineering managers who are actively building and delivering software while adapting to AI-driven change. It is especially valuable for mid-level and senior developers working across web, backend, and platform systems who want to stay relevant as their role shifts from writing code to guiding and validating AI-generated work. 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.

Paperback. Condición: new. Paperback. Bridge the gap between AI hype and software reality by learning how to evaluate agents, redesign team responsibilities, and introduce structured controls that improve delivery without sacrificing clarity, safety, or maintainability.Key FeaturesUse RACM to match AI capabilities to SDLC tasks and supervision needsApply Execution Plans and Logbooks to make agent work visible and auditableSet autonomy limits, guardrails, and review practices for safer adoptionBook DescriptionCode Revealed is a practical guide for teams learning to work with AI agents in real delivery environments. Rather than treating AI as a coding shortcut, it shows how to introduce it as a managed capability across the software lifecycle.It explains how agents differ from assistants, why evaluation matters when selecting tools, and how development changes when intent, supervision, and validation become more important than manual implementation.You will learn how to use frameworks such as RACM to assess capability, Context Engineering to improve reliability, and PAIP to introduce repeatable integration patterns. The book also explains why Execution Plans and Logbooks matter when delegating work to agents, giving teams a way to align before action and review what happened afterward.Beyond process, the book examines team redesign, new specialist roles, and the shift from directing people alone to orchestrating human and artificial contributors together.It also addresses difficult issues often overlooked in AI adoption, including code churn, weak oversight, security exposure, opaque decisions, and the long-term cost of unmanaged speed. The result is a practical roadmap for adopting AI with discipline, transparency, and measurable intent.What you will learnDistinguish agents from simpler AI coding assistantsAssess tool fit using capability and autonomy criteriaStructure prompts through richer Context EngineeringUse plans and logs to supervise non-trivial AI tasksDesign workflows for prototyping, refactoring, and QAPrevent hidden risk from churn, bias, and hallucinationsReorganize teams around emerging AI-native rolesBuild skills for orchestration, review, and governanceWho this book is forThis book is for developers, tech leads, architects, and engineering managers who are actively building and delivering software while adapting to AI-driven change. It is especially valuable for mid-level and senior developers working across web, backend, and platform systems who want to stay relevant as their role shifts from writing code to guiding and validating AI-generated work. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: new. Paperback. Bridge the gap between AI hype and software reality by learning how to evaluate agents, redesign team responsibilities, and introduce structured controls that improve delivery without sacrificing clarity, safety, or maintainability.Key FeaturesUse RACM to match AI capabilities to SDLC tasks and supervision needsApply Execution Plans and Logbooks to make agent work visible and auditableSet autonomy limits, guardrails, and review practices for safer adoptionBook DescriptionCode Revealed is a practical guide for teams learning to work with AI agents in real delivery environments. Rather than treating AI as a coding shortcut, it shows how to introduce it as a managed capability across the software lifecycle.It explains how agents differ from assistants, why evaluation matters when selecting tools, and how development changes when intent, supervision, and validation become more important than manual implementation.You will learn how to use frameworks such as RACM to assess capability, Context Engineering to improve reliability, and PAIP to introduce repeatable integration patterns. The book also explains why Execution Plans and Logbooks matter when delegating work to agents, giving teams a way to align before action and review what happened afterward.Beyond process, the book examines team redesign, new specialist roles, and the shift from directing people alone to orchestrating human and artificial contributors together.It also addresses difficult issues often overlooked in AI adoption, including code churn, weak oversight, security exposure, opaque decisions, and the long-term cost of unmanaged speed. The result is a practical roadmap for adopting AI with discipline, transparency, and measurable intent.What you will learnDistinguish agents from simpler AI coding assistantsAssess tool fit using capability and autonomy criteriaStructure prompts through richer Context EngineeringUse plans and logs to supervise non-trivial AI tasksDesign workflows for prototyping, refactoring, and QAPrevent hidden risk from churn, bias, and hallucinationsReorganize teams around emerging AI-native rolesBuild skills for orchestration, review, and governanceWho this book is forThis book is for developers, tech leads, architects, and engineering managers who are actively building and delivering software while adapting to AI-driven change. It is especially valuable for mid-level and senior developers working across web, backend, and platform systems who want to stay relevant as their role shifts from writing code to guiding and validating AI-generated work. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book shows how to move from experimental AI use to structured delivery. Learn to evaluate tools, assign autonomy, create traceable workflows, and build collaboration models that keep teams effective as AI takes on a larger role.…

Taschenbuch. Condición: Neu. Code Revealed | A practical guide to AI agents, workflows, and modern application practices | Alexio Cassani | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781807789312 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …