These people really mis-understand how people like me use search. I don't want "an experience," I want a list of documents that match the search specifier.

"Agents, however, come with the ability to reason."

Citations needed. Calling recursive bullshit reason does not make it so.

I think Comet's search is really nice and worth the $20 a month, but not $200 a month that it currently costs although it is a little slow. My experience is similar to this article.

One interesting thing about the lack of click stream feedback is that you can generate it synthetically. If you've got your model evaluating the quality of search responses and adjusting its queries when there's a miss, you get to capture that adjustment and tune your search engine. In user click search you need scale to tune search, now you can generate it. The only problem is you need to trust your agent is doing the right thing as it keeps searching harder.

The doc string becoming part of the prompt is a nice touch.

It seems plausible and intuitive that simple tools dynamically called by agents would yield better results than complex search pipelines. But do you have any hard data backing this up?

If agents are making value judgments ostensibly on my behalf, why should I trust them to continue to be aligned with my values if in practice they're almost always running on someone else's hardware and being maintained on someone else's budget?

We'd be stupid to ignore the last 15+ years of big tech "democratization"-to-enshittification bait-and-switch.

Interesting approach. It might be helpful to give the agent more tools though. Some simple aggregations might give it a notion of what's there to query for in the catalog. And a combination of overly broad queries and aggregations (terms, significant terms, etc.) might help it zoom in on interesting results. And of course, relatively large responses are not necessarily as much of a problem for LLMs as they would be for humans.