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.
> Agent parallelism just doesn't seem necessary and makes everything harder. Not an expert though, tell me where I'm wrong.
I use parallel agents for speed or when my single agent process loses focus due to too much context. I determine context problems by looking at the traces for complaints like "this is too complicated so I'll just do the first part" or "there are too many problems, I'll display the top 5".
If you're trying a "model swarm" to improve reliability beyond 95% or so, you need to start hoisting logic into Python scripts.
Where we've had some success is with heterogeneous agents with some cheap quantised/local models performing certain tasks extremely cheaply that are then overseen or managed by a more expensive model.
I also really appreciate the point about using LLM teams for fault tolerance protocols in the future (in addition to improving efficiency). Since agents tend to hallucinate and fail unpredictably, then coordinating multiple of them to verify and come to a consensus etc could reduce those errors
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
I find depth to be far more interesting than breadth with these models.
Descending into a problem space recursively won't necessarily find the best solution, but it's going to tend to find some solution faster than going wide across a swarm of agents. Theoretically it's exponentially faster to have one symbolically recursive agent than to have any number of parallel agents.
I think agent swarm stuff sucks for complex multi-step problems because it's mostly a form of BFS. It never actually gets to a good solution because it's searching too wide and no one can afford to wait for it to strip mine down to something valuable.
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.
Everyone wants to be the CEO of their own megacorp managing thousands of AI engineers I guess. Just like microservices, there’s probably a ton of overhead doing things this way vs monolithic / single agent. Certain types of engineers just love over-engineering hugely complex stuff to see it work. Goldberg architecture was already prevalent and bad enough in enterprise before the AI boom.
Struggling to find anything interesting or non-obvious about this article. You give a bunch of LLMs various parallelizable task and some models manage to do it well but others don't. No insights as to why. As someone with a distributed systems background the supposed 'insights' from distributed computing are almost trivial.
I think that's kind of the point.. Many people deploying these teams don't have a strong systems background, and they're empirically documenting where teams break down / making decisions inspired by human orgs rather than first principles. If people begin from a basic intuition for systems thinking, the tradeoffs should become obvious.
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 ;)
I have an example from 2023, when Auto-GPT (think OpenClaw but with GPT-3.5 and early GPT-4 — yeah it wasn't great!) was blowing up.
Most people were just using it for the same task. "Research this stuff and summarize it for me."
I realized I could get the same result by just writing a script to do a Google search, scrape top 10 results and summarize them.
Except it runs in 10 seconds instead of 10 minutes. And it actually runs deterministically instead of getting side tracked and going in infinite loops and burning 100x as much money.
It was like 30 lines of Python. GPT wrote it for me.
My takeaway here was, LLMs are missing executive function. The ability to consistently execute a plan. But code runs deterministically every time. And - get this - code can call LLMs!
So if your LLM writes a program which does the task (possibly using LLMs), the task will complete the same way every time.
And most of the tasks people use LLMs for are very predictable, and fit in this category.
People are now repeating the exact same thing Auto-GPT thing with OpenClaw. They're using the slow, non-deterministic thing as the driver.
It actually kinda works this time — it usually doesn't get stuck anymore, if you use a good model — but they're still burning a hundred times more money than necessary.
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.
I use parallel agents for speed or when my single agent process loses focus due to too much context. I determine context problems by looking at the traces for complaints like "this is too complicated so I'll just do the first part" or "there are too many problems, I'll display the top 5".
If you're trying a "model swarm" to improve reliability beyond 95% or so, you need to start hoisting logic into Python scripts.
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
Descending into a problem space recursively won't necessarily find the best solution, but it's going to tend to find some solution faster than going wide across a swarm of agents. Theoretically it's exponentially faster to have one symbolically recursive agent than to have any number of parallel agents.
I think agent swarm stuff sucks for complex multi-step problems because it's mostly a form of BFS. It never actually gets to a good solution because it's searching too wide and no one can afford to wait for it to strip mine down to something valuable.
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 ;)
Most people were just using it for the same task. "Research this stuff and summarize it for me."
I realized I could get the same result by just writing a script to do a Google search, scrape top 10 results and summarize them.
Except it runs in 10 seconds instead of 10 minutes. And it actually runs deterministically instead of getting side tracked and going in infinite loops and burning 100x as much money.
It was like 30 lines of Python. GPT wrote it for me.
My takeaway here was, LLMs are missing executive function. The ability to consistently execute a plan. But code runs deterministically every time. And - get this - code can call LLMs!
So if your LLM writes a program which does the task (possibly using LLMs), the task will complete the same way every time.
And most of the tasks people use LLMs for are very predictable, and fit in this category.
People are now repeating the exact same thing Auto-GPT thing with OpenClaw. They're using the slow, non-deterministic thing as the driver.
It actually kinda works this time — it usually doesn't get stuck anymore, if you use a good model — but they're still burning a hundred times more money than necessary.