Observability and Evals for AI Agents: A Simple Breakdown

- You don't know what your agents will do until you actually run them — which means agent observability is different and more important than software observability
- Agents often do complex, open-ended tasks, which means evaluating them is different than evaluating software
- Because traces document where agent behavior emerges, they power evaluation in a multitude of ways

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.

In this video, Harrison walks through how agent observability powers agent evaluation.

Read more: https://www.langchain.com/conceptual-guides/agent-observability-powers-agent-evaluation/?utm_medium=social&utm_source=youtube&utm_campaign=q1-2026_langsmith-fh_aw