About The Speaker
AI agents are changing how systems behave — but how do you actually monitor them?
This session explores the shift from traditional monitoring to AI observability in a simple and practical way. Through a guided exercise, attendees will walk through a real-world scenario to understand what needs to be monitored, where risks appear, and how to maintain reliable AI systems.
Using this approach, the session shows how tools like Nagios XI can play a key role in these environments, helping teams build visibility and keep AI-driven operations stable.
Behind the Session Title
I spend my time in the AI space. However, I still have to worry about capacity, and if my Nagios machine tells me that my machines are overloaded, wouldn’t it be nice to predict possible issues leveraging the Time Series model I use? I have a small proof of concept (and it should be better as I find more and more time to work on this); my Type 1 diabetes data experimentation inspired this talk, and it’s only natural to start looking at the machines I care about. My goal for this session is to give people a different paradigm of what AI can do with the tools they already have and to be a supporting character for people’s work. tl;dr wouldn’t it be nice to have a 24×7 monitor that can tell you possibly weeks ahead of time that you’re going to have issues so you can plan accordingly? The art of the possible is here with this.
What I Hope You Learn
Sign up to get the latest on #NWC2026 speakers, sessions, and registration announcements.