How to safely connect LLMs to real APIs without triggering costly mistakes

A software developer writing for DEV Community outlines hard-learned lessons from deploying LLM-powered function calling in production systems. A key incident involved an LLM retrying a refund three times due to a timeout, exposing how poor system design—not model failure—causes real-world errors. The author recommends treating all model-generated arguments as untrusted user input and enforcing business rules through runtime schema validation rather than relying on prompts alone. Additional guidance includes scoping tool access by context, separating read and write capabilities, and giving models only the minimum set of tools a specific session actually requires. The central analogy offered is that designing tools for an LLM is like building an API for a brilliant but fearless intern who will eventually press every available button at the worst possible time.