An AI application can return a perfectly normal response and still have something seriously wrong underneath.<\/p>\n\n\n\n
Maybe the model is taking too long to answer. Maybe token usage has quietly doubled. A RAG application could be pulling irrelevant documents, or an agent could be making several unnecessary tool calls before reaching the answer. From the user’s side, you might only see the final response.<\/p>\n\n\n\n
That makes debugging production AI different from debugging a normal application. You need to see what happened between the user’s request and the final output.<\/p>\n\n\n\n
That’s what LLM observability tools are built for. They let teams inspect model calls, traces, retrieval steps, agent actions, costs, latency, and evaluation results in one place. The question is no longer whether you need visibility. It’s how much visibility you need, and which platform fits the way your AI system is built. If you want to see where observability fits into the wider architecture, our guide to what an AI stack is<\/a> breaks down the different layers that make up a modern AI system.<\/p>\n\n\n\nTable of Contents<\/h2>