A lot of AI workflows nowadays are being advertised or sold as "agentic AI," but when you look deeper, they're often just a sophisticated script.
As we say in Spanish, they're selling you "gato por liebre," making something look like something it isn't.
I remember a few years ago, a post from someone made the rounds on Reddit because they had managed to have Claude water their plants.
Managing appliances and devices like that has been possible since the 90s. There are even animal feeders that are more sophisticated.
But because the post involved Claude, everybody was upvoting it.
I remember more than 10 years ago building monitoring systems with Nagios. People who work in infrastructure are probably familiar with it because it's been widely used for years.
Nagios is a very sophisticated infrastructure monitoring system. It removed the need to constantly check servers manually and helped identify and address problems proactively.
Nagios might be called AGI by some of the AI experts today. 🤣
I think much of the confusion comes from the fact that AI became mainstream, and now we have a lot of people who are experts in AI but were cab drivers before ChatGPT.
LLMs and automation work hand in hand, but automation has been around much longer than LLMs.
What LLMs have added is something traditional automation struggled with: the ability to reason about an unexpected situation and determine what to do next.
For example, Nagios could notify me about a failing hard drive or tell me that server utilization was getting too high.
But Nagios couldn't reason about the problem.
It could tell me something was wrong. I had to figure out what to do.
An LLM plugged into Nagios can take that additional step. Instead of simply sending me an alert and waiting for me to troubleshoot the problem, an LLM can investigate the issue, determine a course of action, execute the appropriate tools, and verify whether the problem was actually resolved.
And in SAFi, that record would be saved in the Audit Hub.
That's the distinction I'm making between traditional automation and agentic systems.
Automation follows configured rules, scripts, and predefined paths.
An agent can determine which path to take and act according to the situation.
Scripts and configured rules provide the deterministic foundation. The LLM provides the reasoning needed to determine what to do when the predefined paths aren't enough.
In many agentic workflows, most of the work can still be done by the first part, with systems like Nagios. The LLM is only needed when the system encounters a situation where predefined rules aren't enough.
