Half the AI agents in production are if-statements with a GPU bill

A recent article on Dev.to highlights the growing issue of 'resume-driven AI engineering,' where developers implement complex AI agents for tasks that could be solved with simpler, deterministic code. The author argues that many production systems are over-engineered, using expensive LLMs for tasks like regex-based pattern matching, SQL queries, or basic decision trees. This approach introduces unnecessary latency, costs, and failure modes. The piece provides a practical decision checklist to help engineers determine when to use AI versus traditional programming. The core message is that AI should be reserved for tasks involving genuine ambiguity, such as unstructured text or fuzzy matching, rather than being the default solution for every problem. By prioritizing simplicity and maintainability, developers can avoid the technical debt associated with bolting LLMs onto workflows that do not require them.