From Traces to Insights: Understanding Agent Behavior at Scale

Analyzing AI agent behavior at scale is challenging because agents generate thousands of unstructured traces daily, making manual review impossible. Unlike deterministic software, agents are non-deterministic, sensitive to small prompt changes, and have unbounded natural language inputs, so their behavior is unpredictable before production. Traditional product analytics fall short because they are not built for analyzing unstructured conversations to find unknown patterns. LangSmith Insights Agent is a tool that addresses this by using clustering to automatically discover usage patterns and failure modes from large volumes of traces, enabling developers to iterate on agents using production data.