How to build AI-native development

Litvinov came to AI-Assisted Engineering from thirteen years of experience in development, managing a department of more than a hundred engineers and implementing AI in large companies. The book grew out of notes that had to be used to explain certain practices over and over again to different teams. Its task is to establish a stable foundation: models change, but the connection of intention, context, constraints, feedback and verification already develops into a repeatable engineering discipline.

The author describes this evolution with a staircase of nine levels. First, a person writes the code himself, then uses chats, connects a CLI agent with manual confirmations, gives it full access in a limited loop, launches parallel executors and assembles a team of them with roles, memory and synchronization points. Above that are the orchestration and agent ecosystem.

Full access does not mean unconditional trust. For an engineering problem, the minimum expected change, prohibited actions, and observable result must be determined in advance. The agent may think that it is free, but the operating system, repository rights, sandbox and Git set the limits of damage. Rules, specifications and architectural context work as feedforward, and tests, linters, hooks and CI/CD work as feedback. Together they close the loop and return non-deterministic execution to the auditable corridor.

When the generation rate outpaces the team's ability to verify changes, verification debt occurs. Seniors turn into a living filter for the growing flow of diff, cease to understand the system and over time only confirm the result. The solution is to check not the amount of code, but the package of evidence: tests, contracts, screenshots, videos and observed behavior changes. In a complex domain, the reviewer may not be a programmer, but an accountant or lawyer. Green CI proves that known invariants hold, but does not confirm that the team solved the correct problem.

Litvinov calls the multi-agent system a time machine: it speeds up everything that is already happening. A well-tuned cycle improves the product faster, but weak constraints increase conflict, cost, and destruction faster. Therefore, you should start with one agent for whom the goal, exit criteria and feedback are clear. Only then is it worth designing the roles and communications of several performers. Such an organization cannot be copied from another company: its structure depends on the product, maturity, risks and economics of a particular business.

Automation is also changing how people work. In a growing product, it frees up bandwidth for the actual backlog; in a mature or dying product can become a cost cutting tool.