TL;DR


Distributed tracing reveals end-to-end behavior of AI systems across models, tools, and retrieval steps, enabling faster debugging, cost/latency optimization, and continuous quality monitoring. By instrumenting LLM-aware spans, correlating traces with evaluations, and closing the loop into datasets, engineering and product teams gain...