> For all my efforts to get LLMs to work for me, they've failed. I figure it is because the tech space I work in (Industrial Automation) simply doesn't have the training data that someone building webapps does.
Putting safety critical aside (given that is a giant aside) for the moment, I'm a little surprised at this.
One of the most useful things I've found for AI Agent stuff is having an agent live on my home network and finally get the hundred or so random sensors and other IoT devices finally configured and talking together.
It's definitely not perfect though, and not anything I would trust my life to. This year however, I was able to set up enough sensors in my garden to have by far the largest hot pepper harvest I've ever had by min/maxing watering and fertilizing via way too many sensors and data fusing. This was mostly describing the sensors involved, the outcome and dashboarding desired, and pointing it to the various vendor API docs.
I could have done all that stuff manually, but never would have gotten around to it since it was just a tedious boring task. But with AI help I now can tell you precisely the soil moisture level, EC reading, ambient temp, etc. for each of my planters. And with some combining of my rain gauge data and sunlight sensing, tie that into automated irrigation runs. Then the bot just pings me when it thinks the next fertilizing cycle is due.
I don't know if that's revolutionary, but it took a project that would take me weeks to accomplish myself due to around a hundred sensors to configure/setup/etc. to a couple nights of messing around learning what all this talk was over these AI agent things.
I think there are plenty of use-cases where if AI gets things wrong 5% of the time it really doesn't matter a whole lot. I have plenty of these areas at my job.