I can only speak for my own mind ;) but the most advanced thing I'd seen prior in this regard was Google Sheets' =AI function, which is pretty convenient (if awkward) when you want to map values to LLM output.
What I specifically found "mind-bending" about this is that I don't have a clear concept of the limits of what an agent can do. In the limit case, it's basically like an independent employee, right?. So the concept of having a dedicated person sitting on each row of my database and transactionally performing any task I can describe is ... well, it IS a bit boggling to me.
Another way to look at it is: this is an extremely powerful construct for managing fleets of agents. I trust Postgres to execute all the stored procedures I ask it to. So with this tool I can easily spin up arbitrarily many agents. And state management is very simple, because they can directly edit their associated row!
IDK, the more I think about it the more fascinated I am. I'm sure there is some open source SAAS or something that has similar semantics and can do all this more efficiently, but now I know that this is a category of thing one could potentially build/use. Pretty nifty!
ok nice reply. i think i was where you were in 2021 around doing stuff in sprocs. i think pple generally follow a cycle of going overexcited about throwing everything in the database and then going "actually the database is a pretty bad production compute environment" and re-separating concerns back to different levels.
use sprocs lightly for simple fast stateless things. every other attempt at stuffing a lot of compute into the database that i'm aware of has basically failed to gain adoption (the personal awesomeness/happiness of the guy who created it aside)
Oh, like, I wouldn't actually use this specific implementation. I used to work at a shop with thousands of lines of Oracle triggers that you had to edit inline in the web browser with no version control and I shudder to think of returning again.
I'm more interested in the data flow. exa.ai got famous for promising search with massively parallel execution of LLMs on candidate results. In practice, it's never worked that well for me, but the model is very cool and has worked for me e.g. in open source work, searching for bugs across files.
Mapping N items to "N agents with state" feels like an absurdly powerful construct to me. Maybe this is just a well-known pattern that everyone has seen already, but given how much better agents have gotten in the past year, crossing the threshold from "toy" to "arguably superhuman" on many tasks, I think it just hits different.
ok gotcha. yeah i guess my background with temporal.io got me used to "every workflow instance can have a shit ton of long running state that gets persisted and rehydrated at will". check those out if you like N:N+state, whether or not it includes agents is an impl detail
Haha, I guess we see this in reverse: I see the specific framework as the implementation detail (I use, and enjoy, temporal!) I think it's the part about automatically launching a metric ton of agents that is (to use the term again) mind-bending to me.
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