AI observability

How to make a production AI system explainable after the fact: instrument every model call, tool execution, and agent step as timed spans inside a trace; turn traces into metrics, dashboards, and alerts; join them to quality signals; detect drift before it becomes an incident; and protect the sensitive content they carry. For engineers operating LLM-backed systems who need to answer "what happened, and why" for a run they can never reproduce.