From Traces to Insights: Understanding Agent Behavior at Scale

Monitoring AI agents in production is a significant challenge because manually reviewing thousands of daily traces is not scalable. Unlike deterministic software, AI agents are non-deterministic, sensitive to minor prompt changes, and accept unbounded natural language inputs, making their behavior unpredictable. Traditional product analytics fall short as they are built for discrete events, not for analyzing unstructured conversations to find unknown failure patterns. LangSmith Insights Agent addresses this by using clustering to automatically discover and surface key usage patterns and error modes from large volumes of trace data.