AI-Native
Engineering
Building software with AI Agents and Spec-Driven Development
The practical guide for professional software engineers who want to integrate AI across the entire development lifecycle, not as a tool, but as a core engineering capability. Structured. Production-oriented. Tool-agnostic.
"The difference between engineers who thrive in the AI era and those who struggle isn't talent or experience; it's having a structured approach."
What this book covers
Context Engineering
Assemble the right context (code, docs, rules, examples) so AI tools produce consistent, high-quality outputs every time, not just occasionally.
Spec-Driven Development
Use specifications as durable artifacts that guide AI, keep implementations aligned with intent, and survive every tool change or model upgrade.
Agent Orchestration
Know when to use single agents versus multi-agent systems, apply proven orchestration patterns, and prevent hallucination propagation across pipelines.
AI-Native SDLC
Integrate AI across the full development lifecycle, from requirements and architecture through implementation, testing, code review, and maintenance.
What you'll walk away with
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The Pillars of AI-Native Engineering are a free, always-evolving collection of foundational principles, methodologies, and tools that define modern AI-assisted software development. A taste of what this book goes deep on.
Context Engineering
Design and runtime management of the context fed to LLMs: system prompts, memory, retrieval (RAG), and context delivery protocols.
Human-in-the-Loop & Collaboration
Processes, UIs and gating where humans review, refine and approve intermediate artifacts: specs, designs, generated code, model outputs.
Spec-Driven Development
Treat human-readable, testable specifications as the primary artefact; split work into small spec→task→PR cycles so agents implement against those specs.
Inside the book
6 parts · 22 chapters · Conclusion
The AI-Native Engineer
How the role is evolving from implementer to orchestrator, and what skills make you irreplaceable in the age of AI.
Understanding Large Language Models
How LLMs work under the hood, where they genuinely excel, and why they fail in predictable, exploitable ways.
From Tools to Agents
What defines an agent beyond a chatbot: the LLM + tools + context + agentic loop model that powers modern AI systems.
New Failure Modes
Hallucination, confident wrongness, requirement drift, and cascading errors: the risks unique to AI-assisted development.
Context Engineering
Design and assemble the right context at the right time so AI tools produce consistent, production-ready outputs, not occasional lucky ones.
The AI Tool Landscape
Navigate IDEs, CLI tools, and agent runners with a clear evaluation framework, without chasing the next shiny release.
Rules, Skills, and Custom Agents
Build instruction hierarchies, reusable skills, and custom agent personas tailored to your team's specific workflows.
Model Context Protocol
Understand MCP's architecture and security model, and learn how to operate MCP servers safely in real production environments.
Why Specifications Matter
Why specs are the durable artifacts that keep AI-generated code aligned with intent, and how they survive every tool change.
Writing Agent-Friendly Specs
Write specifications that reduce drift, with clear constraints, given/when/then criteria, and structure that agents can reason over.
The SDD Workflow
Apply the canonical Specify → Plan → Execute → Verify → Integrate loop and know exactly where humans must stay in the loop.
SDD Frameworks Compared
Compare GitHub Spec Kit, OpenSpec, and BMAD, and learn when to reach for each, or when no framework is the right answer.
Agent Orchestration Patterns
Design systems of agents using orchestrator patterns and the Ralph Loop, while avoiding cascading failures and over-coordination.
Verification and Quality Gates
Build CI pipelines, spec-to-test traceability, and review gates so AI-generated code meets the same bar as human-written code.
Requirements and Architecture
Use AI to gather requirements, explore architecture trade-offs, and write ADRs, without outsourcing accountability.
Implementation and Refactoring
Handle greenfield development, feature additions, bug fixes, and large-scale migrations with repeatable AI-assisted patterns.
Testing and Quality Assurance
Generate, validate, and maintain tests that actually test what they claim, including edge cases, negative paths, and property-based tests.
Code Review and Release
Use AI as a first-pass reviewer for hallucinations and spec adherence, while humans own the judgment calls that matter.
Maintenance and Evolution
Keep specs, docs, and code aligned over time, and pay down AI-amplified tech debt before it compounds.
Cross-Functional Collaboration
Use specs as the shared contract between engineering, product, and design, and stop the telephone game between disciplines.
Building AI-Native Teams
Create team playbooks, shared patterns, internal agent catalogs, and golden paths that make AI a multiplier, not chaos.
Organizational Transformation
Roll out AI-native practices using the Pilots → Champions → Platform → Policy adoption model and measure outcomes that matter.
Conclusion: The Path Forward
Where AI-native engineering is headed, and the enduring principles that will outlast any specific tool or model.
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Meet Alfonso Graziano
Alfonso Graziano is an AI Tech Lead at Nearform (Italy), where he builds AI agents and uses edge AI tools in production, and leads AI-Native Engineering adoption across the engineering department.
He built an MCP server for Node.js with over 100,000 downloads on Docker Hub, has delivered 20+ conference talks across Europe, and ranks in the top 1% of TypeScript developers on GitHub (Algora ranking).