Learn how to implement metrics, logs, and centralized monitoring to keep your AI agents robust and production-ready
Building AI agents is an exciting challenge, but simply deploying them isn’t always enough to ensure a smooth, robust experience for users. Once deployed, AI applications need effective monitoring and logging to keep them running optimally. Without proper observability tools, issues can go undetected, and even minor bugs can snowball into major production problems.
In this guide, we’ll walk you through how to set up monitoring and logging for your AI agent, so you can maintain complete visibility over its behavior and performance. We’ll explore how to collect essential metrics, gather logs, and centralize this data in one platform. By the end of this tutorial, you’ll have a foundational setup that allows you to detect, diagnose, and address issues early, ensuring a more stable and responsive AI application.
Full code is available here: https://github.com/CVxTz/opentelemetry-langgraph-langchain-example
