The AI-Native SDLC playbook

Summary

The article outlines an AI-native software development lifecycle that adapts traditional SDLC stages to agentic tools such as Claude Code. With code generation no longer the primary constraint, the authors argue that legacy approval gates, handoffs, and documentation rituals now create the main delays. The proposed model converts the linear sequence of planning, design, build, test, deploy, and maintain into a closed loop in which each stage ends by committing a machine-readable artifact that automatically triggers the next stage.

Central to the approach are explicit, version-controlled artifacts that serve both as workflow triggers and as an audit trail. An intent.md file captures the initial requirement or incident report and is reviewed by a product owner before it initiates requirements and design work. The resulting spec.md incorporates organizational policies through reusable skills, after which engineers move to Claude Code in plan mode to iterate on an implementation plan without editing files until the plan is accepted. Auto mode then allows longer autonomous sessions once guardrails are in place.

Governance is maintained through two complementary mechanisms. Skills encode institutional knowledge such as security, compliance, and UX conventions so that Claude applies them while generating or modifying code. Hooks provide deterministic enforcement at the file or command level, blocking unsafe actions or routing high-risk changes to human reviewers. Both mechanisms are themselves version-controlled, and their application is logged in session traces and git history.

The model further supports parallelism by allowing a single engineer to supervise multiple independent Claude Code sessions running in separate git worktrees. Continuous evaluation in CI, automated PR review loops, and production monitoring close the loop: detected anomalies generate new intent.md entries that restart the cycle. Human attention therefore shifts from performing each step to reviewing artifacts and exceptions at defined gates, preserving accountability while removing manual bottlenecks that previously limited the speed of agentic development.

Why it matters

Directly actionable for Product Teams and Builders seeking to embed AI in development processes with controls for compliance and observability. Addresses strategy, risks, and human accountability in regulated environments, relevant to Dutch/EU emphasis on ethical AI.