The AI-Native SDLC Playbook

What the playbook says

Writing code is no longer the slow part of building software. Many teams now use AI agents, such as Claude Code, to write code very fast. But the steps around the code, like approvals, reviews, handoffs, and rules, still move at human speed. The playbook shows how to change each of the six stages of the software development lifecycle (SDLC), from planning to maintenance, so that AI is built into every step.

The old way and the new way

In the traditional SDLC, different people own different stages. A product manager writes requirements, an architect writes a design, engineers build it, QA tests it, a release team ships it, and operations watches it. Each stage passes work on through documents, tickets, and sign-offs. This was built for a time when writing code was the most costly step. In the AI-native version, a loop replaces the straight line. Requirements and design are captured in shared files that both people and AI agents can read. Tests and code are generated by AI, and checks run all the time instead of only at stage boundaries.

Key facts from the playbook

• The traditional SDLC has six stages: plan, design, build, test, deploy, and maintain.
• In the AI-native model, the planning file is called intent.md. It is meant to be read by humans and acted on by machines.
• Later stages use files such as spec.md, plan.md, diffs, pull requests, and incident records, all stored in version control (git).
• Agents can write most of the code changes, so people review only the risky or regulated parts.

Why it matters

If code is written in hours but approvals still take weeks, the speed gain is lost. Teams may end up with long review queues or ship code that no one has checked. Regulated companies cannot accept either result. So the checks and rules must be built to work at the speed of AI. The playbook argues that the process itself, not just the coding tools, must change.

What to watch

• Whether companies really replace weekly committees and sign-offs with checks that run automatically.
• How human reviewers handle much larger amounts of AI-written code.
• Whether the audit trail in version control is accepted by regulators in strict industries.