Ten questions. Two minutes. A clear read on how AI-native your delivery really is — and the exact practices that define your next level.
0 / 10 answered
Intent capture
1. How does work start for your team?
Agent adoption
2. How do engineers use AI coding agents?
Orchestration
3. How is agent work coordinated across the team?
Governance
4. What must a change pass before it ships?
Review capacity
5. Can review keep pace with how fast agents generate code?
Blast-radius awareness
6. Before merging, do you know a change's blast radius?
Spec drift
7. How do you handle specs diverging from code?
Engineering memory
8. When someone needs the context behind a system, they…
Traceability
9. Can you trace a production change back to the intent behind it?
Release decisioning
10. How do you decide it's safe to ship?
Frequently asked questions
What is the AI-Native SDLC Maturity Model?
It's 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). This assessment scores you against it in about two minutes.
What level are most engineering teams at?
Most sit at L1 (Accelerated): they've adopted AI coding agents but kept their old planning, review, and memory practices, so velocity outpaces coordination — the Orchestration Gap. Moving to L2+ means adding orchestration, governance, and engineering memory.
Is the assessment free and private?
Yes. It's free, needs no signup, and runs entirely in your browser — none of your answers are sent anywhere.