book

Learning AI-Native Software Engineering

Name: Learning AI-Native Software Engineering
Author: Alfonso Graziano
ISBN: 9798341674738

by Alfonso Graziano

February 2027

Intermediate

300 pages

6h 28m

English

O'Reilly Media, Inc.

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Brief Table of Contents (Not Yet Final)
1. The AI-Native Engineer
The Vibe-Coding TrapTwo Engineers, One TaskWhat AI-Native Engineering Is NotThe Role Transformation: From Implementer to OrchestratorWhy the Traditional Workflow No Longer ScalesThe New Center of Gravity: Intent, Constraints, and VerificationIntentConstraintsVerificationHuman-in-the-Loop: The Nonnegotiable CheckpointWhat AI-Native Engineers Actually DoBefore the workDuring the workThroughoutThe New Skill Stack: Eight Skills That Make You IrreplaceableThe Cost of Not AdaptingCareer Implications: Why Software Engineering is Becoming a Leadership RoleSummary
2. From LLMs to Agents
How LLMs Work: An Engineer’s-Eye ViewTokensThe Transformer and AttentionIn-weights Knowledge Versus In-context KnowledgeThe Context Window is Your Working-Memory BudgetWhy LLMs Are Good at CodeTraining with verifiable outcomes: RLHF and RLVRChain-of-thought and reasoning modelsNondeterminism, Temperature, and SamplingThe Model LandscapeOpen source versus closed sourceSmall language modelsMultimodal modelsEmbeddings and semantic searchWhere LLMs Excel and Where They FailBenchmarks: What They Do and Don’t MeasureThe API Layer: How Engineers Talk to LLMsWhat Makes an Agent: LLM, Tools, Context, LoopThe LLM: The reasoning coreTools: How agents act on the worldContext: Accumulated stateThe agentic loop: Reason, act, observe, adjustBeyond single agentsBuilding an Agent in 50 Lines of CodeStopping conditions and iteration limitsAgent frameworksWhy this matters for users of coding agentsThe Agent Execution Model: Reason, Act, ObserveThe self-correction loop: Tests as observationsTool Use and Function CallingHow function calling works under the hoodMemory and StateIn-context memoryLong-term memory through filesRetrieved memory with RAGAgent-generated memoryContext rotAgent Economics: Cost, Latency, and ScopeHuman-in-the-LoopHITL as a design principleFeedback as context enrichmentThe agent decides when it needs youCalibrating HITL to riskThe AI Tool LandscapeIDE-integrated coding assistantsCLI and terminal-native agentsCloud and background agentsCode review assistantsDesign and UI generation toolsChoosing tools without chasing hypeSummary
3. Context Engineering Fundamentals
From Prompt Engineering to Context EngineeringThe Core Problem: Right Context, Right Time, Limited WindowThe Attention ProblemThe Lost-in-the-Middle PhenomenonContext RotThe Human Working-Memory ParallelThe Right-Time ProblemThe Two Failure ModesMandatory and Optional Context ComponentsA Taxonomy of Context ComponentsSystem PromptsUser Input and User-Provided ContextRules, Style Guides, and ConstraintsSkillsToolsEnvironment Context: Runtime and MetadataConversation History and Memory ManagementThe Context Assembly ProcessAutonomous Context Discovery: How Agents Explore Before They WriteCode Indexing and Semantic SearchCLI-based ExplorationLanguage Servers and Symbolic Code UnderstandingWhat Autonomous Discovery Does and Doesn’t ReplaceThe Agent Runtime Pipeline: From Request to ActionContext Assembly, Planning, and Tool ExecutionThe Iterative Loop: Reason, Act, Observe, AdjustThe Golden Balance: Too Little Focus, Too Much DistractionAvoiding Context RotSession ManagementToken Efficiency for Large PayloadsSummary
4. Model Context Protocol
What MCP Is and Why It MattersWhat MCP actually doesWhy the MCP standard matters for engineersThe MCP Architecture: Clients, Servers, and TransportsHosts, clients, and serversTransportsLocal servers versus remote serversHow it connects in your daily workflowThe MCP EcosystemConfiguring a server: a typical exampleThe Economics of MCPWhat you pay for in an MCP sessionControlling tool output sizeMCP Security ModelThreatsWhat Real Incidents Teach Us About MCP SecurityMCP Security Best PracticesFour Security PrinciplesOperating MCP in ProductionMCP Approved RegistryDebugging MCPThe server won’t startThe server connects but shows no toolsThe agent doesn’t use the tool you expectA tool call fails with an errorReading tool-call logsMCP and Context PollutionWhy Too Many MCP Servers Degrade PerformanceAgent-Specific MCP Server ConfigurationPracticeBuilding Your Own MCP ServerWhen to build rather than useHow hard is it?Writing good tool descriptionsMCP in Agentic PipelinesWhat changes when there’s no human in the loopPractical use cases for MCP in pipelinesThe reliability constraintSkills and CLI Scripts: When You Don’t Need an MCP ServerThe patternSkills that reference project scriptsThe CLI-first movementWhen to use skills and CLI tools versus MCPAdvanced Tool Use: Scaling Beyond Static Tool ListsThe three bottlenecksTool search: Dynamic discovery instead of static loadingProgrammatic tool calling: Code as orchestrationTool-use examples: Teaching by showingMatching the solution to the bottleneckWhere the industry is headingSummary
5. Spec-Driven Development
The Root Problem: Why Do We Need SDD?Garbage In, Garbage Out: At ScaleThe Compounding ProblemThe Human ParallelYou Already Write SpecsWhen These Documents Are MissingThe Case for Spec-Driven DevelopmentThe Power of Learning by Writing a SpecUncovering Unclear Requirements with a SpecThe Economics of Writing SpecsWhen SDD Is Not the Right FitSpecs as Durable Artifacts That Survive Tool ChangesSpecs and Organizational KnowledgeSDD and Human-in-the-LoopWhy Engineers Resist Writing SpecsPlan Mode: The Bridge Between Vibe Coding and SDDPlan Mode in PracticePlan Mode and Spec-Driven DevelopmentThree Levels of SDD Maturity1. Spec-First: Writing Specs Before Implementation2. Spec-Anchored: Keeping Specs During Evolution3. Spec-as-Source: The Spec as Primary ArtifactWhat Good Specs Look LikeSummary
6. The SDD Workflow
The Canonical Loop: Specify, Plan, Execute, Verify, Integrate, LearnWhere Humans Must Stay in the LoopStop Conditions and Escalation RulesBefore the Loop: When You Need a Prototype FirstThe Throwaway Branch PatternScratch Repo PrototypesA Concrete ExampleConsolidating Learnings into the SpecWhen to Skip the PrototypeThe Core Artifact Setspec.md: Intent, Constraints, Acceptance Criteriaplan.md: Approach, Trade-Offs, Sequencingtasks.md: Atomic Tasks with Done ChecksOptional Artifacts: risks.md, rollback.md, adr.mdFrom Idea to Spec: Turning Intent into a DocumentFrom Spec to Plan: Turning Intent into ActionFrom Plan to Tasks: Decomposition and Dependency ManagementExecution Patterns and PromptsVerification as Hard Rails: CI Gates That Make Output ShippableIntegration and DeploymentLearning and Iteration: Closing the LoopThe Spec Lifecycle: What Happens After Implementation?The Two-Tier Model: What Works in PracticeThe Learning Phase as the BridgeSDD in Brownfield ProjectsThe Core Challenge: Specs for Code That Was Never SpecifiedStarting Small: The Spec Island StrategyReverse-Engineering Intent with AI AssistanceHandling Undocumented Behavior and Implicit ContractsPrioritizing What Gets Specified FirstSummary
7. SDD Frameworks Compared
Why Frameworks Exist: Structure, Consistency, Team AlignmentGitHub Spec KitOpenSpecBMAD MethodRoles, Personas, and Guided WorkflowsBMAD in PracticeBMAD in Brownfield ProjectsCustom Workflows, Custom Agents, and Adapting BMAD for Your Use CaseOther Tools Worth KnowingKiroAgent SkillsChoosing the Right FrameworkFrequently Asked Questions on Using SDD in ProductionSummary
About the Author

Content preview from Learning AI-Native Software Engineering

About the Author

Alfonso Graziano is a hands-on AI Tech Lead at Nearform who builds AI agents and uses edge AI tools in production every day. He is driving the adoption of AI-Native Engineering across his company’s entire engineering department, helping teams move from ad-hoc AI use to structured, production-ready workflows.

Alfonso built an MCP server for Node.js that has over 100,000 downloads on Docker Hub, giving developers a practical way to integrate AI into their environments. He has several years of experience and has delivered more than 20 conference talks across Europe on topics such as career development, deep tech topics and AI. He is active in the community, with thousands of followers on LinkedIn, where he shares practical insights on AI and software engineering.

Based in Italy, Alfonso writes and speaks from the perspective of someone who ships AI-powered systems, designs agent workflows, and coaches teams on AI-Native Engineering, making him uniquely positioned to write a book that bridges theory and production practice for professional engineers.

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Publisher Resources

ISBN: 0642572352530Errata Page

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Learning AI-Native Software Engineering

by Alfonso Graziano

About the Author

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.