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If an AI agent can't figure out how your API works, neither can your users
Discover how LLM‑powered agents act as tireless API testers—exposing friction in docs, errors, and design—and learn six essential strategies (consistency, comprehensive docs, rich errors, example‑driven tutorials, simplicity, and feedback loops) to elevate your API’s developer experience for both humans and machines alike.
That’s why teams now talk about Agent Experience (AX) as the next DX frontier: the fundamentals—clear reference, solid examples, consistent patterns—remain the same, but an LLM ruthlessly exposes and logs any gaps. In short, when you streamline an API to minimize look-ups, hops, and payload bloat, you’re not only polishing DX, you’re also improving AX for agents that can’t improvise around those hurdles. If you see repeated calls with obsolete parameters or missing version prefixes, proactively add targeted errors(“Did you mean X?”) or gracefully handle legacy inputs.
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