How to Debug AI API Failures Across Multiple Models

Getting an AI API request to return a response is only the beginning.

For real AI products, the harder question is what happens when something goes wrong.

A chatbot may become slower. A RAG answer may stop using the right context. A structured extraction workflow may start returning invalid JSON. An agent may trigger the wrong tool. A fallback model may answer correctly, but at a much higher cost.

In a single-model prototype, debugging is usually simple.

You check one provider, one API key, one model, and one request format.