I think AI agents are a lot like a chainsaw - they can save you a lot of time vs other tools for the same job, but... you can also screw up with them rather easily.
Fortunately, I just write software with LLM agents, if it comes out badly I can start over with nothing lost but time. My experience of the last few months has been: it actually writes some pretty good software, if you know anything about writing good software with teams of people and apply that knowledge to managing the LLM agents. If you're just some guy who doesn't really know how to write software, LLM agents aren't always going to save you from yourself, just like a chainsaw won't.
The advantage I perceive with the AI agent vs junior co-workers both going down their respective rabbit holes, the AI agent costs less time and money to get their "in depth report" and you don't have to kindly explain why it's off track when it gets horribly distracted by something irrelevant, you can just flush the context and start over.
I do find myself a bit upset when I'm in the middle of a long context development with an LLM agent and that context gets lost for some reason - re-explaining everything from scratch can be exhausting, to avoid having to do that you can actually instruct the agent to create a file explaining to future agents what it "knows" about the discussion at the moment. Some agents are starting to do this automatically like Claude's MEMORY.md file and similar. Another semi-cool thing is: you can read these files yourself (if you have the patience) and if you see anything in there that the agent "got wrong" you have the opportunity to fix it. Asking a junior colleague to thoroughly explain everything they think they know about a project gets to be a very wearisome endeavor both for the junior and whoever is reviewing their "understanding" of the project.