If an AI agent can't figure out how your API works, neither can your users

LLM-powered agents are beginning to look a lot like tireless junior developers. Hand them an API along with the docs and they’ll diligently read the reference, issue a request, parse the error, adjust parameters, and try again and again—on loop—until something works. Frameworks like LangChain and OpenAI function-calling helpers, as well as agents like Cursor, Claude Code, OpenAI Codex, and Codename Goose, make that cycle even easier to wire up and follow. But raw persistence of an agent isn’t always enough. The moment your API’s developer experience falters—an outdated example, a vague 400 payload, an undiscoverable required field—the agent stalls out. That stall is diagnostic. If an algorithm with perfect patience and zero ego can’t figure things out, then a human developer on a deadline is almost certainly hitting the same wall. That symmetry can be a powerful lever. Fix the friction points that trip up agents, and you clear the path for every user, silicon‑ or carbon‑based, to ha ... Read full article.