Claude and other LLMs struggle designing LLM-based tools

The author argues that large language models struggle to design software that effectively uses other LLMs, frequently resorting to complex symbolic solvers or regex checks instead of simple API calls. While various explanations exist, including training data limitations and reinforcement learning environments, the author suggests this stems from ordinary software design errors where AI agents pick the wrong tool for the job. Open article →