Developer shares structured workflow for using AI agents in large codebases

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A developer has published a practical workflow designed to reduce AI-related biases, such as confirmation bias, when contributing to large, structured codebases. The process begins with thoroughly reading project specifications and conventions before writing any code, ensuring changes align with the existing architecture. During implementation, the developer follows strict repository rules covering DTOs, status values, and traceability citations, while keeping changes small and focused. Verification involves running module and mutation tests, with failures reported transparently rather than concealed. The workflow concludes with clean commit practices, updating status documentation only after owner approval, and filing an Architecture Decision Record whenever established conventions are intentionally bypassed.

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