Tag: observability

Traditional monitoring catches when systems are down but misses when AI is confidently wrong. Over 90 percent of models experience drift yet most teams do not detect it until users complain. Learn how to build AI observability monitoring with tools like Langfuse that catches problems before they compound.

Traditional monitoring tells you if your LLM is running. It does not tell you if it is delivering garbage to users. LangChain found 89% of agent teams now implement observability, but evaluation adoption lags at 52%. Here is how to build LLM monitoring that catches quality failures in production.