Learn how to implement metrics, logging, and centralized monitoring to keep your ai agents robust and production-ready.
Creating ai agents is an exciting challenge, but simply implementing them is not always enough to ensure a smooth and robust experience for users. Once deployed, ai applications need effective monitoring and logging to keep them functioning optimally. Without proper observability tools, issues can go unnoticed and even minor errors can become major production issues.
In this guide, we'll explain how to set up monitoring and logging for your ai agent, so you can maintain complete visibility into its behavior and performance. We will explore how to collect essential metrics, collect logs, and centralize this data in one platform. By the end of this tutorial, you will have a basic setup that will allow you to detect, diagnose, and address issues early, ensuring a more stable and responsive ai application.
The complete code is available here: https://github.com/CVxTz/opentelemetry-langgraph-langchain-example
