LangChain has published a guide to observability for production LLM agents. The post argues that agent monitoring needs traces, evaluations, and feedback loops designed for multi-step behavior rather than simple request logs.
That distinction matters because agents can fail in ways that are hard to see from the final answer alone. Tool calls, retrieval choices, intermediate plans, and retries all shape the outcome.
As more teams deploy agents beyond demos, observability is becoming core infrastructure for reliability, cost control, and debugging.