Theory
AI-Native Engineering is the practice of building software with AI integrated as a core capability across the entire development lifecycle: from design and specification through implementation, testing, and maintenance. It is about how professional engineers build any software when AI is part of the stack.
Scope
- Not building AI applications or fine-tuning models
- Not using AI only for one-off experiments or autocomplete
- Yes treating AI as a partner across the SDLC: design, implementation, testing, review, deployment, and maintenance
- Yes using structured approaches (context engineering, spec-driven development, verification) so outputs are consistent and production-ready
Why the name
"AI-Native" signals that AI is built into how you work by default, the same way "cloud-native" describes systems designed for the cloud from the start. The goal is control, quality, and accountability while gaining speed and leverage.
Practice
Run this in a repository you already know, not a toy project. The point is to feel where the practice helps and where it gets in the way on code that has history.
Take some notes on the referenced video and share it within your team.