Agentic AI has its faults, but one thing I've found it to be very good at is surfacing the "unknown unknowns": things I didn't know I should have searched for but that are directly relevant to my problem.
Sometimes that is fine, sometimes it is not
I've got a pretty solid algorithm for checking correctness: I ask the LLM for its sources, I try to find 3-5 independent ones (that are not just copying each others' answers), and if they all agree, that's very likely to be the correct answer. Simple math here: if you have 5 sources and they are each 60% likely to be correct, then an LLM choosing at random from them would have a 60% success rate, while someone checking all 5 of them for agreement would have a 1 - (0.4^5) = 99% chance of being correct. It's a good algorithm for doing other things like verifying scientific papers, too: you look for indendent research groups that have all reproduced the same findings.
I did the same thing with ten-blue-links websearch as well, and hope this would be the habit of anyone else too. (Although I know it wasn't, because I worked on Google websearch 15 years ago, on a project to increase the credibility of search results, and we did cafeteria UX studies about "What makes a credible result?" and everybody said "Because it appears as the top result on Google.")
Say I want to look up some game from my childhood, which I barely remember any details for. Going to google and trying is likely going to be very difficult unless I happen to get lucky with some key element. But if an LLM can get it right even a minority of the time, it can lead to me quickly finding the game I'm looking for.
This does depend upon the ability to evaluate the answer, like checking against source or some other option where you know a good answer from bad. If you can't, then it does become much more dangerous. Perhaps part of the reason AI seem to empower experts more than novices in some domains?