How do you monitor AI agents once they're in production?
Shipping an AI agent is only the beginning. Once it's being used by real users, monitoring becomes just as important as development.
Traditional software monitoring focuses on uptime, latency, and errors, but AI agents introduce additional questions. Are they making good decisions? Are tool calls succeeding? Are they completing tasks efficiently? Are they getting stuck or behaving inconsistently?
I'm interested in how different teams monitor AI agents after deployment.
What signals do you pay attention to? Are there particular metrics, dashboards, or observability tools that have become essential for your workflow?
Have you ever caught a major issue through monitoring that wouldn't have been obvious otherwise?
I'd love to hear what has worked well—and what hasn't—from teams building and operating AI agents in production.
Replies
I see daily new product popping up being it observability tool, monitoring tool, telemetry etc. etc..
The question is what problem needs to be solved, i.e. is giving me at least the answer of what software I use.
Example, I want to know the telemetry of my Claude Code Agents, i.e. observing token usage, cost and so on. For that @LangWatch is my choice. I hook it into my Claude Code CLI and monitor via their UI.
Instead when it comes to Agents being and acting autonmous based on their LLM decisions, an interception on runtime level or even OS is necessary. Agentgatenway is one opetion as it is open source, alternatively you can build your own governance locally around that.
In my case i`m using heavily Claude Code, hence the hook of Claude is a starting point on application level and then locally i use a own developed interception