Technical Debt in the AI

Описание и применение

AI ускоряет написание кода, но усиливает проверку и выпуск, увеличивая существующий долговой интерес. В примере команда использовала AI для обновления Django и быстро создала набор компонентных тестов, что позволило разбить крупный PR на мелкие и убрать ручную проверку.

Задача

AI adoption speeds code writing but increases review, QA, and release friction, amplifying existing verification debt.

Результат

The test suite eliminated a manual verification step, reduced release risk, and demonstrated that AI can cheaply produce both code and its safety checks.

Рабочий процесс

• 1. AI generated Django upgrade code.
• 2. Team identified missing tests and wrote component tests using AI.
• 3. Tests ran locally in ~16s and could be executed by AI agents.
• 4. PRs were split into six small stacked PRs.
• 5. Each PR merged and released sequentially, guarded by the component tests.

Доказательства

AI wrote the upgrade fast. Proving it was safe to release was the Bottleneck.

We wrote Component Tests, which test the whole service through its public interface only: send event request to the API, assert the right update goes out. The suite runs with one command in about 16 seconds, locally or on QA, and anyone can run it, AI agents too. It took less than a day to build, with AI doing most of the writing.

With the check in place, we stopped batching the work into one big, risky PR. We shipped six small stacked PRs and six production releases one after the other.