book

AI-Native Software Delivery

by Nick Durkin, Eric Minick, Chinmay Gaikwad

July 2025

Intermediate to advanced

212 pages

English

O'Reilly Media, Inc.

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Who Should Read This BookWhy We Wrote This BookNavigating This BookConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
Development + Operations = DevOpsA Short History of DevOpsAgile in the AughtsContinuous Integration and Continuous DeliveryMilestones in Early DevOpsDevOps 1.0DevOps 2.0Summary
Introducing Source Control ManagementA Short History of Source Control ManagementGitOps and Source Control ManagementMonorepos and Remote CachingAI in Source Control ManagementSource Control Management in the Delivery PipelineCode Repository ConsiderationsComprehensive IntegrationsAI-Powered FeaturesEfficiency and Transparency Through Open SourceA Platform ApproachAccess Control, an ExampleDefining Roles, a Platform ApproachSummary
A Short History of Building and Testing SoftwareStructured Software Development and Waterfall MethodologiesAgile and Test-Driven DevelopmentEnter Continuous IntegrationContinuous Integration TodayContinuous Integration in the CI/CD PipelineThe Essential Build StepPrioritizing Quality and Security with Static AnalysisAutomated Testing: Test Early, Test OftenThe Test PyramidContinuous Integration ToolsJenkins ConsideredBeyond JenkinsModern Features to Accelerate Software BuildsSummary
Establishing a Unified Deployment ProcessDeploy Consistently to Every EnvironmentLeverage Git Workflows with GitOpsContinuous Delivery, Deployment, and Test in the CI/CD PipelineTypes of TestingIntent-Based Functional and End-to-End TestingTraditional Testing Versus a Hollowing-Out-the-Middle ApproachPromotion Between EnvironmentsFrom Decisions by Committee to Automated DecisionsFrom Manual to Automated PromotionBreak the Environment BottleneckSummary
Modern Applications and the Cyberthreat LandscapeThe Growing Threat of Software Supply Chain AttacksRegulatory Compliance Frameworks That Apply to Software Supply ChainsSecuring Modern Applications with Shift LeftThe Need for Developer-Friendly Shift-Left SecurityApplication Security ScannersSecuring the Software Supply ChainIdentifying Top CI/CD Security RisksIdentifying Top OSS RisksEnsuring Integrity with Supply Chain Levels for Software ArtifactsSLSA OverviewUsing SLSA to Ensure IntegrityAddressing Zero-Day Vulnerabilities with Software Bill of MaterialsUsing SBOMs to Remediate Dependency IssuesAdopting DevSecOps PrinciplesEstablish a Collaborative Culture and Break Down Functional SilosAdopt and Enforce Secure Coding Methodologies and Shift-LeftSummary
Getting Started with Chaos EngineeringPrinciples of Chaos EngineeringStarting Small and ScalingStarting in Production-Like EnvironmentsLeveraging Modern ToolsLearning from OthersService-Level Objectives and Service ResiliencyEstablishing Reliability TargetsShared Ownership of System ReliabilityError Budgets and Their Role in Reliability and InnovationMonitoring to Inform Chaos Testing ExperimentsStrategic Use of Error Budgets for Chaos Testing ExperimentsIntegrating Chaos Engineering and SLOs into CI/CD PipelinesScaling Your Chaos Engineering PracticesAdding Chaos Engineering Experiments and SLOs to Your CI/CD PipelineSecurity and Governance for Chaos EngineeringThe Future of AI-Native Chaos Engineering in Service ReliabilitySummary
Governing Production DeploymentsTraditional Approaches to Deployment GovernanceModern Approaches to Deployment GovernanceSafeguarding the Deployment ProcessFuture Trends in Deployment GovernanceReconciling Traditional and Modern ApproachesProduction Deployment StrategiesThe Traditional Big-Bang DeploymentUsing a Progressive Delivery StrategyChoosing the Right ToolsVerifying Production DeploymentsObservability in DeploymentsModernizing the War RoomTesting Production DeploymentsSummary
Benefits of Feature Management in Modern Software DevelopmentSpeeding Up Development Cycles with Feature FlagsDecoupling Teams to Reduce Coordination OverheadSupporting Progressive Delivery with Phased RolloutsManage Tech Debt with Feature FlagsOptimizing Results Through ExperimentationBuilding Well-Structured ExperimentsIntegrating Experimentation with Progressive DeliveryEstablishing GuardrailsLife Without Mature Feature Management ToolsLow-Quality Tools Impede Effective Feature Flag ManagementMinimal Support for Experimentation Limits Your LearningLack of Integrations Slows You DownFragile Implementations Distract Your TeamScaling Feature Management and ExperimentationUnify with a Single Feature Management ImplementationReduce Manual Steps with Smart IntegrationsSimplify Governance with Automated Audit Trails and EnforcementLeverage Your Existing Identity Management InfrastructureChoose a Platform Built to ScaleSummary
The Evolution of Cloud Cost ManagementEarly Cloud Adoption and Initial ChallengesThe Rise of FinOpsModern Cloud Cost Management ChallengesAI-Driven Cloud Cost Optimization StrategiesRight-Sizing Cloud ResourcesLeveraging Commitment-Based Pricing and Spot InstancesUsing AI to Manage Container CostsAligning Cost Savings Goals with Business ObjectivesAutomating Cloud Governance and ComplianceImplementing Cloud Governance PoliciesEnforcing Budget Guardrails with AutomationEnsuring Tag Compliance Through AutomationAllocating the Costs of Shared Platforms and ServicesCloud Cost Management to Meet Environmental Sustainability GoalsThe Future of AI in Cloud Cost ManagementSummary

Why Platform Engineering?The Developer Cognitive Load Crisis From Toolchains to Platform as a Product The Business Case for Platform Engineering Supporting a Collaborative DevOps CultureCreating and Operating Platform TeamsCritical Characteristics of a Platform Team Engagement Models That WorkEffective Operating ModelsDefining Your Platform StrategySetting Platform Principles Platform Antiprinciples and Resolving ConflictsUnderstanding Your Platform Audience Selecting Platform Scope A Practical Road Map ExampleBalancing Standardization and Flexibility Platform Measurement and EvolutionMeasuring Platform Success Driving Platform Adoption Leveraging Internal Developer Portals for Platform SuccessSustainable Platform Evolution A Practical Example: Platform Engineering in ActionConclusionLooking ForwardGetting Started

Overview

AI coding assistants are helping teams create software faster than ever. But to turn that speed into real innovation, organizations must go beyond writing code and deliver software quickly, securely, and reliably.

While AI-assisted coding is now mainstream, what happens after the code is written is still catching up. AI-Native Software Delivery is your practical guide to applying AI across the entire delivery lifecycle, from commit to production and beyond.

Written for software engineers, DevOps leaders, and tech executives, this book explores how leading teams are using AI to streamline CI/CD, manage cloud costs, strengthen security, and eliminate operational toil. The book also uncovers the risks of brittle automation and shows you how to avoid building systems that don't scale.

You'll learn how to:

Integrate AI across delivery workflows to accelerate time to value
Avoid common pitfalls of fragmented automation strategies
Adopt DevSecOps principles that scale with your team
Apply real-world practices in AIOps, chaos engineering, and SRE
Future-proof your delivery with intelligent pipelines and feedback loops

Whether you're evolving a legacy delivery process or designing a new platform, this guide will help you lead your organization into the AI-native future of software delivery.