Over the past year, I’ve noticed that a lot of conversations about agentic AI happen before anyone has to run the system in production. The assumptions often sound reasonable at first. More agents should make a workflow smarter. Memory should make the agent more useful. Better models should solve most of the hard problems. Then the system gets deployed and some of those assumptions don’t hold up the way people expected. For those who’ve spent time building or operating agentic systems, what’s the biggest misconception you’ve changed your mind on? What sounded true when you started that turned out to be much less important once the agent had to do real work? submitted by /u/Meher_Nolan
Originally posted by u/Meher_Nolan on r/ArtificialInteligence
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