The current fad for "agent swarms" or "model teams" seems misguided, although it definitely makes for great paper fodder (especially if you combine it with distributed systems!) and gets the VCs hot.
An LLM running one query at a time can already generate a huge amount of text in a few hours, and drain your bank account too.
A "different agent" is just different context supplied in the query to the LLM. There is nothing more than that. Maybe some of them use a different model, but again, this is just a setting in OpenRouter or whatever.
Agent parallelism just doesn't seem necessary and makes everything harder. Not an expert though, tell me where I'm wrong.
LLMs mostly do useful work by writing stories about AI assistants who issue various commands and reply to a user's prompts. These do work, but they are fundamentally like a screenplay that the LLM is continuing.
An "agent" is a great abstraction since the LLM is used to continuing stories about characters going through narrative arcs. The type of work that would be assigned to a particular agent can also keep its context clean and distraction-free.
So parallelism could be useful even if everything is completely sequential to study how these separate characters and narrative arcs intersect in ways that are similar to real characters acting independently and simultaneously, which is what LLMs are good at writing about.
Seems like the important thing would be to avoid getting caught up on actual "wall time" parallelism
Once you run more than one agent in a loop, you inevitably recreate distributed systems problems: message ordering, retries, partial failure, etc.
Most agent frameworks pretend these don’t exist. Some of them address those problems partially. None of the frameworks I've seen address all of them.
This is how we design at HewesNguyen AI. We are both MIS so once LLMs came out we where like sweet whole teams that can be tasked for one thing done well. Thank you Unix Philosophy
i cant wait for the world to catch up to process, session, et al. calculii. the closest i’ve seen is all this “choreo” stuff that is floating around nowadays, which is pretty neat in itself.
> Next up, LLMs as actors & processes in π-calculus.
You jest, but agents are of course already useful and fairly formal primitives. Distinct from actors, agents can have things like goals/strategies. There's a whole body of research on multi-agent systems that already exists and is even implemented in some model-checkers. It's surprising how little interest that creates in most LLM / AI / ML enthusiasts, who don't seem that motivated to use the prior art to propose / study / implement topologies and interaction protocols for the new wave of "agentic".
Ten years ago at my old university we had a course called Multi-Agent Systems. The whole year built up to it: a course in Formal Logic with Prolog, Logic-Based AI (LBAI) with a robot in a block world, also with Prolog, and finally Multi-Agent Systems (MAS).
In the MAS course, we used GOAL, which was a system built on top of Prolog. Agents had Goals, Perceptions, Beliefs, and Actions. The whole thing was deterministic. (Network lag aside ;)
The actual project was that we programmed teams of bots for a Capture The Flag tournament in Unreal Tournament 3.
So it was the most fun possible way to learn the coolest possible thing.
The next year they threw out the whole curriculum and replaced it with Machine Learning.
--
The agentic stuff seems to be gradually reinventing a similar setup from first principles, especially as people want to actually use this stuff in serious ways, and we lean more in the direction of determinism.
The main missing feature in LLM land is reliability. (Well, that and cost and speed. Of course, "just have it be code" gives you all three for free ;)
Regardless of whether it's framed as old-school MAS or new-school agentic AI, it seems like it's an area that's inherently multi-disciplinary where it's good to be humble. You do see some research that's interested in leveraging the strengths of both (e.g. https://www.nature.com/articles/s41467-025-63804-5.pdf) but even if news of that kind of cross pollination was more common, we should go further. Pleased to see TFA connecting agentic AI to amdahls law for example.. but we should be aggressively stealing formalisms from economics, game theory, etc and anywhere else we can get them. Somewhat related here is the camel AI mission and white papers: https://www.camel-ai.org/
An LLM running one query at a time can already generate a huge amount of text in a few hours, and drain your bank account too.
A "different agent" is just different context supplied in the query to the LLM. There is nothing more than that. Maybe some of them use a different model, but again, this is just a setting in OpenRouter or whatever.
Agent parallelism just doesn't seem necessary and makes everything harder. Not an expert though, tell me where I'm wrong.
LLMs mostly do useful work by writing stories about AI assistants who issue various commands and reply to a user's prompts. These do work, but they are fundamentally like a screenplay that the LLM is continuing.
An "agent" is a great abstraction since the LLM is used to continuing stories about characters going through narrative arcs. The type of work that would be assigned to a particular agent can also keep its context clean and distraction-free.
So parallelism could be useful even if everything is completely sequential to study how these separate characters and narrative arcs intersect in ways that are similar to real characters acting independently and simultaneously, which is what LLMs are good at writing about.
Seems like the important thing would be to avoid getting caught up on actual "wall time" parallelism
You jest, but agents are of course already useful and fairly formal primitives. Distinct from actors, agents can have things like goals/strategies. There's a whole body of research on multi-agent systems that already exists and is even implemented in some model-checkers. It's surprising how little interest that creates in most LLM / AI / ML enthusiasts, who don't seem that motivated to use the prior art to propose / study / implement topologies and interaction protocols for the new wave of "agentic".
In the MAS course, we used GOAL, which was a system built on top of Prolog. Agents had Goals, Perceptions, Beliefs, and Actions. The whole thing was deterministic. (Network lag aside ;)
The actual project was that we programmed teams of bots for a Capture The Flag tournament in Unreal Tournament 3.
So it was the most fun possible way to learn the coolest possible thing.
The next year they threw out the whole curriculum and replaced it with Machine Learning.
--
The agentic stuff seems to be gradually reinventing a similar setup from first principles, especially as people want to actually use this stuff in serious ways, and we lean more in the direction of determinism.
The main missing feature in LLM land is reliability. (Well, that and cost and speed. Of course, "just have it be code" gives you all three for free ;)