"The AI-Native SDLC Playbook" details a comprehensive framework for transforming the software development lifecycle (SDLC) from a human-centric, sequential process to an agentic, loop-based model. The central thesis is that as agentic AI (like Claude Code) accelerates coding—no longer making code generation the bottleneck—the traditional, manual, and process-heavy SDLC creates friction that stalls productivity. To resolve this, the paper proposes shifting focus to the non-build phases and embedding automated, version-controlled governance into every stage.

Core Methodology

The AI-native SDLC replaces siloed, hand-off-heavy phases with a continuous loop of version-controlled markdown artifacts. Each stage produces an artifact that serves as the input for the next, creating an automated chain of custody and an inherent audit trail.

#### The Six-Stage Lifecycle

1. Plan: Requirements shift from scattered tickets to a version-controlled intent.md file, created through brainstorming with AI and reviewed by product owners.
2. Design: Requirements and design specifications are compressed into a single, AI-driven session that produces a spec.md, constrained by organizational policies (skills).
3. Build: Utilizing Claude Code in "plan mode," engineers convert specs into a plan.md before code is generated. This creates a deterministic path that the AI follows, ensuring that governance occurs before implementation.
4. Test: Continuous evaluation is woven into the development process rather than existing as a final QA gate.
5. Deploy: Human review is reserved for critical/regulated paths. Automated agentic reviews compare diffs against the plan.md and policy-driven hooks.
6. Maintain: Agents monitor production and automatically trigger a loop back to the planning phase (writing new intent.md files) when control bands are breached.

#### Technical Enforcement and Governance
The methodology relies on three specific technical mechanisms to maintain control:

• CLAUDE.md: A repository-level file that acts as a persistent instruction set. It captures team conventions, architecture, and recurring mistakes, effectively providing "long-term memory" for the agent at the start of every session.
• Skills: Version-controlled folders containing SKILL.md files. These encode organizational policies (e.g., security, branding, compliance). These are applied as constraints during the design or build phases, ensuring policy is "read and applied" during creation rather than checked later during review.
• Hooks: Deterministic guardrails triggered by specific build-time actions. While skills are advisory, hooks provide the hard enforcement layer, preventing prohibited edits or ensuring mandatory formatting.

#### Operational Dynamics
The paper emphasizes "Parallel Sessions" and "Subagents." Engineers manage multiple streams of work concurrently using separate git worktrees, each driven by independent AI agents. This scales individual productivity. Furthermore, the relationship between these stages is defined as an automated trigger loop where:
$$\text{Artifact}_{n} \xrightarrow{\text{Action}} \text{Gate} \xrightarrow{\text{Trigger}} \text{Artifact}_{n+1}$$
If the transition between stages is successful (e.g., merging a PR), the downstream process initializes automatically.

Summary of Transition

The transition to this model requires reconciling legacy systems with the new "artifact-first" approach. Organizations are advised to choose a single "Source of Truth" for every artifact—either the repository itself (using markdown files) or a legacy tool (Jira, ServiceNow)—linked via commit SHAs. By treating the chain of commits as the audit trail, the AI-native SDLC ensures that governance remains rigorous while human attention is shifted exclusively toward high-level judgment and policy oversight rather than repetitive, low-level execution.