Apart from rediscovering all the problems with distributed systems, I think LM teams will also rediscover their own version of the mythical man-month, and very quickly too.
There were 3 core insights: adding people makes the project later, communication cost grows as n^2, and time isn't fungible.
For agents, maybe the core insight won't hold, and adding a new agent won't necessarily increase dev-time, but the second will be worse, communication cost will grow faster than n^2 because of LLM drift and orchestration overhead.
The third doesn't translate cleanly but i'll try: Time isn't fungible for us and assumptions and context, however fragmented, aren't fungible for agents in a team. If they hallucinate at the wrong time, even a little, it could be a equivalent of a human developer doing a side-project during company time.
An agent should write an article on it and post it on moltbook: "The Inevitable Agent Drift"
We’ve been building exactly this as an open-source ecosystem at consensus-tools. It’s a governance layer for multi-agent systems with a runtime wrapper that intercepts agent decisions before they execute: .consensus(fn, opts).
The coordination and consistency problems the paper describes are what the monorepo is designed around. Giving agents auditable stake in decisions. Happy to share more if anyone’s working in this space.
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.
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
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.
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.
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.
An agent is a way of performing an action that will generate context or a useful side effect without having to worry about the intermediate context.
People already do this serially by having a model write a plan, clearing the context, then having the same or a cheaper model action the plan. Doing so discards the intermediate context.
Sub-agents just let you do this in parallel. This works best when you have a task that needs to be done multiple times that cannot be done deterministically. For example, applying the same helper class usage in multiple places across a codebase, finding something out about multiple parts of the codebase, or testing a hypothesis in multiple places across a codebase.
> 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've played with this type of thing and I couldn't justify it vs just using a premium model, which seems more direct and error proof. Cheap models in my experience could really consume tokens and generate cost
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 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
> A "different agent" is just different context supplied in the query to the LLM. There is nothing more than that.
Yup, but context includes prompt which can strongly control LLM behavior. Sometimes the harness restricts some operations to help LLM stay in its lane. And starting with fresh context and clear description of a thing it should work on is great.
People get angry when their 200k or million token context gets filled. I can't ever understand why. Keeping such amount of info in the operational memory just can't work well, for any mind. Divide and conquer, not pile up all the crap till it overfills.
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 ;)
Apart from rediscovering all the problems with distributed systems, I think LM teams will also rediscover their own version of the mythical man-month, and very quickly too.
There were 3 core insights: adding people makes the project later, communication cost grows as n^2, and time isn't fungible.
For agents, maybe the core insight won't hold, and adding a new agent won't necessarily increase dev-time, but the second will be worse, communication cost will grow faster than n^2 because of LLM drift and orchestration overhead.
The third doesn't translate cleanly but i'll try: Time isn't fungible for us and assumptions and context, however fragmented, aren't fungible for agents in a team. If they hallucinate at the wrong time, even a little, it could be a equivalent of a human developer doing a side-project during company time.
An agent should write an article on it and post it on moltbook: "The Inevitable Agent Drift"
We’ve been building exactly this as an open-source ecosystem at consensus-tools. It’s a governance layer for multi-agent systems with a runtime wrapper that intercepts agent decisions before they execute: .consensus(fn, opts).
The coordination and consistency problems the paper describes are what the monorepo is designed around. Giving agents auditable stake in decisions. Happy to share more if anyone’s working in this space.
Yeah share for sure pls
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.
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
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.
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.
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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.
An agent is a way of performing an action that will generate context or a useful side effect without having to worry about the intermediate context.
People already do this serially by having a model write a plan, clearing the context, then having the same or a cheaper model action the plan. Doing so discards the intermediate context.
Sub-agents just let you do this in parallel. This works best when you have a task that needs to be done multiple times that cannot be done deterministically. For example, applying the same helper class usage in multiple places across a codebase, finding something out about multiple parts of the codebase, or testing a hypothesis in multiple places across a codebase.
> 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.
Would it be possible to give that single agent process twice as much compute, instead? Or do production systems not scale that way?
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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've played with this type of thing and I couldn't justify it vs just using a premium model, which seems more direct and error proof. Cheap models in my experience could really consume tokens and generate cost
Steelmanning the other side of this question:
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 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
> A "different agent" is just different context supplied in the query to the LLM. There is nothing more than that.
Yup, but context includes prompt which can strongly control LLM behavior. Sometimes the harness restricts some operations to help LLM stay in its lane. And starting with fresh context and clear description of a thing it should work on is great.
People get angry when their 200k or million token context gets filled. I can't ever understand why. Keeping such amount of info in the operational memory just can't work well, for any mind. Divide and conquer, not pile up all the crap till it overfills.
you have to own the inference layer
[dead]
I tend to agree. After seeing http://chatjimmy.ai, I think multi-agent systems are mostly just solving for LLMs being slow currently.
This is like saying “multi-core cpus are just solving cpus being slow”. Which yes, exactly.
Next up, LLMs as actors & processes in π-calculus.
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.
Is it web scale?
Abstractly? 100%. Realistically? Depends on how many trillions we can get from investors.
> 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 ;)
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Could it just be that it is happening behind closed doors due to multi agents being part of the secret sauce of post training LLMs.
That's all nice & well but which protocol & topology will deliver the most dollars from investors?
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Title has a typo: distrbuted
Fixed, thanks!
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