9781808342035 - beyond code: build reliable ai-assisted software with context engineering, mechanical gates, and ai agent control di jeremy mcentire (7 risultati)
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Paperback. Condizione: new. Paperback. Discover why AI changes engineering by moving attention from code output to durable decisions, safer boundaries, and shared understandingKey FeaturesApply context engineering to control what AI coding agents see and produceReplace subjective code review with mechanical gates and executable…acceptance criteriaDesign and coordinate multi-agent workflows that close the build-test-deploy loop reliablyBook DescriptionThe advent of AI coding agents has triggered an identity crisis in tech. But the core of software engineering was never just about writing syntax. It is about solving problems, defining constraints, and translating business reality into scalable software solutions. Beyond Code is the practitioner's survival guide to the new landscape of software development, teaching you how to stop competing with the machine and start directing it.The book covers the forces that determine whether AI assistance produces reliable software: context discipline, which shapes what agents see and what they ignore; mechanical gates, which replace advice-based review with verifiable pass-fail conditions; and loop closure, which keeps multi-agent coordination from drifting off-mission through Goodhart traps and proxy decay. You will work through input design, information filtering, decomposition as constraint topology, hierarchical agent coordination, and multi-pass thinking for output verification.By the end of this book, you will be able to manage the information environment your AI agents operate within, enforce the constraint structures that keep them aligned, and build multi-agent workflows that close the build-test-deploy loop without fragile handoffs or compounding failures.What you will learnEngineer context to control what AI coding agents produceFilter irrelevant information that degrades model output qualityUse decomposition to create verifiable, independently testable seamsReplace code review opinions with executable mechanical gatesIdentify and escape Goodhart traps in developer metrics and evalsCoordinate AI agents hierarchically to reduce overhead and driftApply multi-pass thinking to catch failures before they compoundTranslate software decisions into terms that align engineering teamsWho this book is forThis book is for developers, software engineers, tech leads, engineering managers, and architects who want to stay effective as code generation becomes a default part of the development workflow. It is particularly useful for those who have started using AI coding tools in production and are noticing where the outputs break down in system coherence, review quality, or team alignment. Readers should have experience building, reviewing, or leading software projects. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condizione: new. Paperback. Discover why AI changes engineering by moving attention from code output to durable decisions, safer boundaries, and shared understandingKey FeaturesApply context engineering to control what AI coding agents see and produceReplace subjective code review with mechanical gates and executable…acceptance criteriaDesign and coordinate multi-agent workflows that close the build-test-deploy loop reliablyBook DescriptionThe advent of AI coding agents has triggered an identity crisis in tech. But the core of software engineering was never just about writing syntax. It is about solving problems, defining constraints, and translating business reality into scalable software solutions. Beyond Code is the practitioner's survival guide to the new landscape of software development, teaching you how to stop competing with the machine and start directing it.The book covers the forces that determine whether AI assistance produces reliable software: context discipline, which shapes what agents see and what they ignore; mechanical gates, which replace advice-based review with verifiable pass-fail conditions; and loop closure, which keeps multi-agent coordination from drifting off-mission through Goodhart traps and proxy decay. You will work through input design, information filtering, decomposition as constraint topology, hierarchical agent coordination, and multi-pass thinking for output verification.By the end of this book, you will be able to manage the information environment your AI agents operate within, enforce the constraint structures that keep them aligned, and build multi-agent workflows that close the build-test-deploy loop without fragile handoffs or compounding failures.What you will learnEngineer context to control what AI coding agents produceFilter irrelevant information that degrades model output qualityUse decomposition to create verifiable, independently testable seamsReplace code review opinions with executable mechanical gatesIdentify and escape Goodhart traps in developer metrics and evalsCoordinate AI agents hierarchically to reduce overhead and driftApply multi-pass thinking to catch failures before they compoundTranslate software decisions into terms that align engineering teamsWho this book is forThis book is for developers, software engineers, tech leads, engineering managers, and architects who want to stay effective as code generation becomes a default part of the development workflow. It is particularly useful for those who have started using AI coding tools in production and are noticing where the outputs break down in system coherence, review quality, or team alignment. Readers should have experience building, reviewing, or leading software projects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condizione: new. Paperback. Discover why AI changes engineering by moving attention from code output to durable decisions, safer boundaries, and shared understandingKey FeaturesApply context engineering to control what AI coding agents see and produceReplace subjective code review with mechanical gates and executable…acceptance criteriaDesign and coordinate multi-agent workflows that close the build-test-deploy loop reliablyBook DescriptionThe advent of AI coding agents has triggered an identity crisis in tech. But the core of software engineering was never just about writing syntax. It is about solving problems, defining constraints, and translating business reality into scalable software solutions. Beyond Code is the practitioner's survival guide to the new landscape of software development, teaching you how to stop competing with the machine and start directing it.The book covers the forces that determine whether AI assistance produces reliable software: context discipline, which shapes what agents see and what they ignore; mechanical gates, which replace advice-based review with verifiable pass-fail conditions; and loop closure, which keeps multi-agent coordination from drifting off-mission through Goodhart traps and proxy decay. You will work through input design, information filtering, decomposition as constraint topology, hierarchical agent coordination, and multi-pass thinking for output verification.By the end of this book, you will be able to manage the information environment your AI agents operate within, enforce the constraint structures that keep them aligned, and build multi-agent workflows that close the build-test-deploy loop without fragile handoffs or compounding failures.What you will learnEngineer context to control what AI coding agents produceFilter irrelevant information that degrades model output qualityUse decomposition to create verifiable, independently testable seamsReplace code review opinions with executable mechanical gatesIdentify and escape Goodhart traps in developer metrics and evalsCoordinate AI agents hierarchically to reduce overhead and driftApply multi-pass thinking to catch failures before they compoundTranslate software decisions into terms that align engineering teamsWho this book is forThis book is for developers, software engineers, tech leads, engineering managers, and architects who want to stay effective as code generation becomes a default part of the development workflow. It is particularly useful for those who have started using AI coding tools in production and are noticing where the outputs break down in system coherence, review quality, or team alignment. Readers should have experience building, reviewing, or leading software projects. 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.
