How to Build AI-Native Software Development

AI agents speed up code, but engineering work moves into framing, constraints, and verification. Litvinov maps nine maturity levels—from chat and a CLI agent to parallel workers, orchestration, and an ecosystem. The next stage makes sense only when the previous one works repeatedly.

Full access is not trust. Define a minimum change, prohibited actions, and an observable outcome; the OS, sandbox, permissions, and Git bound damage. Specifications provide feedforward, while tests, linters, and CI/CD provide feedback that keeps nondeterministic execution inside a verifiable corridor.

When generation outruns review, verification debt appears: senior engineers filter diffs and lose system understanding. Review examines evidence—tests, contracts, screenshots, and behavior; sometimes the reviewer may be an accountant or lawyer. Green CI proves known invariants, not that the task was right.

Multi-agent work is a “time machine”: a sound loop accelerates the product, while a weak one accelerates damage. Begin with one agent whose goal and feedback are clear, then design roles around business risk and economics. Leaders help engineers choose methods and accept outcomes; future interviews may resemble a shared brownfield task, not trivia.