FinanceHub #2: When the code compiles but is still wrong
Introducción En el artículo anterior conté cómo decidimos migrar FinanceHub de una plataforma no-code a un backend propio, y que la parte difícil no fue escribir código: fue decidir cómo construirlo. Dije que antes de pedirle cualquier tarea a un agente de IA, definimos la arquitectura, el modelo de datos, el contrato de API y un plan por fases. Esta es la parte de cómo eso se sostuvo en la…
The article discusses the experience of using an AI agent to write code for FinanceHub's backend. The team defined the architecture, data model, API contract, and phased plan before starting, and treated the API contract and database schema as fixed inputs. The AI agent wrote most of the code, but the team implemented a 6-step verification process to ensure it worked correctly, including compiling, running tests, and manual testing.
This process revealed several bugs, including issues with authentication, data deletion, security policies, and time zone reporting, which were not caught by unit tests or superficial reviews. The team's discipline and verification process, rather than the AI agent itself, ensured the code worked correctly. The article concludes that the next challenge will be maintaining the frontend and dealing with new issues that arise.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.