AI Native Engineering

AI-Native Engineering Assessment

Find out how AI-native your engineering organization really is — and where you stand on the path from AI-assisted development to a fully AI-native delivery lifecycle.

8 questions · Under 3 minutes · Get your AI-Native Maturity Score

0 of 8 answered

01Where does AI sit in your delivery lifecycle?

Code completion and generation only1Codegen plus testing or documentation2Requirements through code through test, with gaps3Specification through verification as a continuous pipeline4

02Where is human judgment mandatory in your AI-assisted work?

Not defined; reviewer discretion1Informal norms, unwritten2Written policy for high-risk changes only3Documented mandatory-review points, enforced in the pipeline4

03How structured are your requirements before AI touches them?

Prose tickets and conversation1Templated tickets, human-oriented2Structured acceptance criteria on most work3Machine-actionable specs with explicit contracts and constraints4

04How coupled is your pipeline to a single AI vendor?

Fully dependent on one vendor's tooling1One primary vendor, others experimentally2Multiple tools, integration is manual3Vendor-abstracted pipeline; tools are interchangeable components4

05In a two-week sprint, how many merged PRs are produced by agents under a single developer's supervision?

Fewer than 10; essentially human-authored pace110–40; agents contributing at scale, developer still authoring240–120; developer operating primarily as supervisor3120+; developer running a fleet, throughput bounded by verification4

06If an AI-originated defect shipped, could you trace its provenance?

No1Reconstructable manually with effort2Partial traceability on some workstreams3Full traceability: prompt, model, review, approval — auditable4

07How often do you replace the underlying LLM, and does output quality hold?

Never changed it1Changed once; quality impact not measured2Change periodically; quality checked informally3Change regularly against a measured baseline; quality holds4

08What happens to what you learn on a project?

Stays with individuals1Documented but rarely reused2Shared assets exist, adoption is uneven3Codified into reusable assets that measurably improve the next engagement4

Inspiring Code, Creating Value.