🔗 AI agents keep failing. The fix is 40 years old.

AI agents often falter due to hidden dependencies in the code, leading to failures in production environments. This issue stems not from the models themselves but from the tight coupling and mutable states in the codebase that AI agents cannot effectively navigate. As Cyrus Radfar points out, while human developers build mental models over time, AI lacks that context, resulting in complicating hidden states that cause cascading errors. To remedy this, he suggests applying long-established principles of functional programming from the 1980s. By ensuring that all functions are pure, with explicit data flows and minimal side effects, AI agents can operate more reliably in scenarios where codebases contain numerous dependencies. This approach enables agents to modify functions without facing the risk of unintended consequences from hidden global states. The implementation of these principles could significantly enhance AI-driven development practices.