AI-Native SDLC Maturity Model — The AI-Native SDLC Maturity Model is a five-level framework (L0–L5) describing how deeply an engineering organization has adopted AI-native software delivery — from L0 (AI as autocomplete) to L5 (intent-native, agent-orchestrated, fully-remembered delivery).
Adopting AI in the SDLC isn't binary. The maturity model gives organizations a shared language for where they are and what 'better' looks like: L0 Assisted (AI autocompletes, no process change), L1 Accelerated (agents write code, humans coordinate manually), L2 Orchestrated (work dispatched to agents from structured specs), L3 Governed (gates, blast-radius, and drift guard every change), L4 Remembered (an engineering memory graph makes context self-serve), and L5 Intent-Native (teams express intent; the system delivers and remembers).
Most teams today sit at L1: they've adopted agents but haven't changed the operating model, so they feel the Orchestration Gap acutely. The model's value is diagnostic — it shows which capability to add next rather than prescribing a rip-and-replace.
Skaftor maps to L2–L5 and offers a free AI-Native SDLC Maturity Assessment that scores an organization and recommends the next level's practices.
Most are at L1 (Accelerated): they've adopted AI coding agents but kept their old planning, review, and memory practices, so velocity outpaces coordination. Moving to L2+ means adding orchestration, governance, and engineering memory.
AI-native software delivery is a way of building software in which humans express intent a…
The Orchestration Gap is the widening distance between how fast AI can generate code and h…
Engineering memory is an organization's persistent, queryable record of how and why its so…
Release confidence is a composite readiness score that answers 'should we ship this?' by c…
Last updated July 28, 2026 · https://skaftor.com/glossary/ai-native-sdlc-maturity-model