2026 / Free online book · The AI-Native Canon
The AI-Native SDLC
What software development looks like when the machine writes it
Access
Free
Chapters
10
Read time
105 min
Alpesh Nakrani
CRO at Devlyn · former CTO & COO
Version control, review, and testing were designed for human authors. Rebuilding the development lifecycle around a machine that does the writing.
This edition is free to read onsite. Each chapter has its own URL, so readers can bookmark, share, and return to the exact section they need.
Table of contents
FM Front Matter: The AI-Native SDLC What software development looks like when the machine writes it 2 min 01 The Generated PR That Passed The pull request was green. Unit tests passed. 11 min 02 Specs, Prompts, and Provenance as Lifecycle Artifacts The engineer deleted the prompt after the code worked. Six weeks later, nobody could explain why the service accepted a fallback value that contradicted the product spec. 10 min 03 The ACCEPT Loop The team tried to write an AI coding policy and produced a thirty-page document nobody used. The useful version became a loop written on one page: Articulate, Create with provenance, Check mechanically, Evaluate behavior, Prove ownership, Trace in production. 10 min 04 Risk-Classifying Generated Diffs The small diff touched the pricing calculation. The large diff updated comments and tests. 10 min 05 AI-Native CI The team added AI to writing code before it added AI to checking code. That was backwards. 10 min 06 Human Review When the Author Is a Model The reviewer wrote "looks good" because the code looked good. It used the right abstractions, matched style, and came with tests. 11 min 07 Agent Permissions, Sandboxes, and Supply Chain Trust The coding agent did exactly what it was asked to do and one thing nobody noticed it could do: it read a local environment file while debugging a failing integration test. Nothing leaked that day. 10 min 08 Testing Behavior, Not Just Code Paths The model generated tests for every branch it created. Coverage improved. 10 min 09 Release, Observability, Rollback, and Incident Learning The change looked small enough to ship without a feature flag. It had been generated, reviewed, tested, and merged. 10 min 10 The Operating Playbook The head of engineering asked for one document new teams could use before adopting AI coding agents. It could not be philosophical. 10 min END Conclusion: Closing Note The AI-native SDLC does not worship generated code. It treats code as one artifact inside a larger chain of intent, provenance, evidence, ownership, and production learning. 1 min