Feb 5
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When something goes wrong in traditional software, you know what to do: check the error logs, look at the stack trace, find the line of code that failed. But AI agents have changed what we're debugging. When an agent takes 200 steps over two minutes to complete a task and makes a mistake somewhere along the way, that’s a different type of error. There’s no stack trace - because there’s no code that failed. What failed was the agent’s reasoning. You can't build reliable agents without understanding how they reason, and you can't validate improvements without systematic evaluation. Read more in our new conceptual guide on how agent observability and evaluation differ from traditional software observability and evaluation ➡️ https://www.langchain.com/conceptual-guides/agent-observability-powers-agent-evaluation Sign up for LangSmith to help you observe, evaluate, and deploy your agents ➡️ https://smith.langchain.com/?utm_medium=social&utm_source=youtube&utm_campaign=q1-2026_langsmith-fh_aw