Agent swarms and the new model economics

https://cursor.com/blog/agent-swarm-model-economics

by jlaneve • 2 days ago

272 143 2 days ago

htrp• 2 days ago

The browser swarm from earlier this year peaked at roughly 1,000 commits per hour on Git. The new system peaks at around 1,000 commits per second.
To facilitate this rate of activity, we built a new version control system (VCS) from scratch. Throughput was not the only reason to own this layer. Every change in the system passes through the VCS, so it is where collisions first become visible, and several of the coordination mechanisms in the next section are implemented directly inside of it.

Talk about inventing the universe to make a button.

jgalt212• 2 days ago

It's hard not read such quotes and immediately think of the Infinite Monkey Theorem.

https://en.wikipedia.org/wiki/Infinite_monkey_theorem

whatever1• 2 days ago

If at the end Shakespeare-level literature is produced, does it matter whether we arrived there by random keystrokes?

notahan2• 1 day ago

Isn't that the core fundamental of AI? Constrain the results to a latent space which has all the probable answers and then pick up whichever token makes sense based on your random sampling?

I feel like the whole concept of harness and LLMs IS to pick random tokens which make sense. Think about this as a solution space (a very complex one), then the harness and the LLM are moving towards the local minima at which the code is complete. This is akin to how we think about AI anyways.

leoqa• 2 days ago

I mean why even use VCS at that point?

d5lt5• 2 days ago

imho, to be able to revert and recall as agents loose context due to new spawn or compaction.

ViscountPenguin• 2 days ago

Seems very reasonable to me tbh, the VCS was built to facilitate human work structures, rebuilding it to facilitate autonomous work seems appropriate.

gritzko4• 2 days ago

Making a high-throughput revision control system is doable, there have been experiments here on HN with git-in-postgres etc. The question is, what is the actual bottleneck here?

I made a VCS for LLM assisted work, bootstrapping it on its own code as it was developed. In fact, if it did not really work, it would not even exist! In my experience, keeping track of things and evaluating them was the bottleneck and I focused on that https://replicated.live/blog/worktree Effectively, the question is "how to leverage human evaluation and feedback".

The Cursor people concluded their last post with "taste, judgement, and direction came from humans". Sounded surprisingly sane after "don't compile it, commit it faster" some paragraphs earlier.

sawyers4• 2 days ago

People often think of tech/business as bottleneck issues ie whats my limiting factor. That's relevant to pre-ai business as humans are relatively self organizing.

The issue with AI is it has no real memory, context, long term reasoning, etc. These are a lot of what we use to determine how well we're doing something as well as why.

The "bottleneck" so to speak with ai is that it will just circularly build junk forever, expand scope infinitely, and just continue building.

At its best it will autobuild your unicorn saas. But on average it's a cancer cluster of ever expanding code with no plan.

The issue with unsupervised AI is that when you increase the throughput you decrease the amount of human supervision. So you have to have some way to deal with that.

If I'm going to give AI a gigantic task that's really complicated and it will probably mess it up 100s of times, how will I compensate for that without constantly looking over it's shoulder?

Schlagbohrer6• 2 days ago

They probably just vibe coded it with ChatGPT

h2aichat• 1 day ago

What would have happen if you do not commit at all?

handfuloflight2• 2 days ago

To test that progress, we returned to a task the old swarm had struggled with: building SQLite from scratch, in Rust, from nothing but its documentation.

Isn't SQLite's source code in its training data?

Romario77• 2 days ago

not in Rust. might be different enough ...

hiddendoom45• 2 days ago

There's Turso which is a sqlite rewrite in Rust with some additional features. It's open source so I'd imagine it would be in the training data.

kgeist2• 2 days ago

Even if no Rust code for it was seen during training, an LLM can trivially transpile SQLite's C codebase to Rust on the fly. For example, I just asked ChatGPT to write John Carmack's famous Fast Inverse Square Root algorithm in Erlang, without searching online or thinking, and it transpiled it immediately (while also extracting the knowledge in the same step). SQLite's semantics/code are stored in the middle layers of an LLM, and the last layers are able to convert it into any representation, as conditioned by the prompt. Cursor's experiment is deeply flawed because they merely extracted the model's compressed, lossy knowledge of SQLite's codebase and then just ran a bunch of tests/fixing rounds to make up for the lossiness. The claim that the agents built it from scratch is false.

overgard2• 2 days ago

Of course it is

anthonypasq• 2 days ago

Love to see these crazy kinds of experiments going on. Even if this doesn't 100% work or is prohibitively expensive for now, these are glimpses into the future in the same way people were talking about coding agents in 2023 when we just had tab complete.

sawyers4• 2 days ago

It shouldn't be prohibitively expensive because the more you break the work into smaller parts, theoretically the smaller a model can be that completes it.

Therefore your agent swarms don't have to be cutting edge API calls, they can be hosted on clusters of tiny cheap computers with small CPU GPU. For orgs you could basically invest in a local server that has 100s of small computers.

You can use engineers and frontier models to plan and do task subdivision. And then shit that out to the local cluster that's basically handling each small task without knowing what it's supposed to do.

The real issue is the middle. Testing, verifying interconnectivity of mid level abstractions, building that amount of tech debt at such a high rate and actually being aware in any way what it is that you've built.

I think for most MBAs, considering the US and a significant amount of global markets only care about short term market upside, and AI is still in the middle of the hype cycle, that's not a serious issue for anyone who is profit oriented.

It's only a real issue for the losers like you and me who care about sustainability and infrastructure that survives longer than one market hurricane season.

Schlagbohrer6• 2 days ago

The twin trends of AI slop being churned out at tremendous rate and deployed in everything, plus frontier AIs becoming really good at exploiting many small security holes to pwn boxen, is going to collide in an amazing cyberstorm that was predicted by every cyberpunk novel from the 80s X-D and we are gonna have front row seats 8-D

cookiengineer• 2 days ago

This so much!

For my current setup, the most efficient way is to use larger models for coordination, but a heretic'ed qwen 30B model for the implementations. If you build your agentic environment around specifications and test coverage tied to symbols, you can do a lot of parallelization of agent work.

If you then separate the filesystem and tool read/write access by agent roles and policies (e.g. coder not allowed to modify unit tests, tester not allowed to modify code files) then you can force them to use a centralized per symbol/per contract specification tool.

And then you can just let agents discuss issues, where the messaging threads are also tied to the same symbols.

[1] Exocomp, highly experimental: https://github.com/cookiengineer/exocomp

gritzko4• 2 days ago

I am afraid the folks are still busy playing with familiar toys. "Let's extract more code out of LLM's inner layers!" SQLite we already have. That's where their "spec" came from.

The interesting frontier is elsewhere: how to fit it into the pipeline to build new useful things. I felt motivated enough to write a follow-up/reply post http://replicated.live/blog/follow-up

reinitctxoffset• 1 day ago

It's cool stuff, but I think mostly in an aspirational way right now. The tired cliche hit on the tired cliche claim will be "but sqlite is in distribution" which will miss the point, that objection dissolves at the precise point where it would have operationalized: novel software of value. Is SQLite in the train set, yeah. Does that matter? Only if someone is willing to pay for Rust SQLite as something other than marketing.

It's cool but I'd rather know what their most successful users are doing, and how they measure that.

mccoyb• 2 days ago

I find these blog posts (and the originals, with Anthropic's C compiler and Cursor's browser) somewhat funny, as if they have this enormous power to build ... but they can't build something unique or new. Like the software sucks, but look how powerful the process is (the models are indeed powerful).

And it's a bit of a shame: by virtue of their position (their embedding in the fabric of venture capitalism), it seems like they can only make a subset of things -- what they can make is dictated enormously by capital, as they are engines of capital.

Not sure the point I'm trying to make, I just find it amusing.

Perhaps the point is that it might be more worthwhile to give independent creators a billion dollars to play around with agent swarms if we want to keep diversity in the evolutionary algorithm that is the software industry high.

brap2• 2 days ago

Because it's mostly a load of crap.

Most of these reusable, automated workflows sound really cool in theory, but in practice either they require a lot of customization (not really reusable) or a lot of babysitting (not really automated).

AI has improved my productivity significantly, I'm not a hater, but we also have to be honest about its (current) limitations. The whole "software factory" thing is just a pipe dream people sell to execs / investors / online courses.

thewhitetulip6• 2 days ago

Yes and this can be proven because what Anthropic says and does is vastly different

They say SaaS is dead and you can vibe code any SaaS using Opus and yet despite having Mythos with them they have to use JS terminal to run Claude Code.

Why don't they just vibe code CC? Why doesn't Dario have a 100000 agent swarm to run entirety of Anthropic or at least software department?

maherbeg• 2 days ago

Yes, that's true, but in the post, they even mention that building the spec is the scarce resource.

  For that to work, the swarm has to actually follow the spec, which is what much of this post is about. We gave the swarm 835 pages of prose and it came back with a database. What was scarce in this experiment, and what we expect to be scarce in software engineering going forward, is the right description of intent.

inciampati• 2 days ago

but that's what we use the agent "swarm" to do .. it's also not scarce. what's scarce is imagination.

SpicyLemonZest• 2 days ago

I mean, how long are these pages? Assuming 40 or so lines per page, that would be 33400 lines of spec turned into 200,000 lines of Rust, or about 6 lines of code per line of spec. That's a very detailed spec, much more than any software specification I have ever written in my career. It's not obvious to me that writing this spec would meaningfully save work over incrementally building individual features, especially when you think about what it would take for human beings to meaningfully review whether those 835 pages contain the right intent.

viccis3• 2 days ago

We're rediscovering the "architect" job role a decade or so after switching to agile processes and staff engineers largely replaced it. God help us when they rediscover UML for agents.

arjie• 2 days ago

Well, the tool works well. I am grateful that Nature has seen fit that the same kind of person who can make it doesn’t also have the ability to identify what needs exist in the market because then all software would flow out of OpenAI and Anthropic and the rest of us would run about their legs and peep about. Whether product development will survive the next generation of models is still unknown.

dakolli3• 2 days ago

bro it can't even make a button that doesn't look like it was designed in 2018, everything is ass. Product development isn't going anywhere.

viccis3• 2 days ago

The bottleneck has always been on the product side for 99% of cases. For the 1% where it's engineering, LLMs don't help as much.

ta93754829• 2 days ago

it feels kind of like a concept car a large manufacturer would show off at a motor show. Everyone knows there is no intention to actually ship that thing, but they're exploring concepts and technology and trying to make inroads in what comes next.

whinvik• 2 days ago

I would have loved to see more of the harness engineering shared as code. Instead we are left with only the outcome.

I guess that makes sense since the harness is the product in the case of Cursor.

dakolli3• 2 days ago

I call this junk "meta-agentic engineering", it reminds me of people who have the coolest nvim configs, spend hundreds of hours customizing it but ultimately get less work done than the guy with minimal workflows, if any at all.

I look on twitter and it's just people building tools for agents to use agents, some weird customization loop going on in the LLM space right now. Ultimately these are trends pushed on us from model providers because they 10x token consumption. Its literally just BS trends to increase revenue at these companies, most of it is largely useless.

svachalek• 2 days ago

Some of this stuff is ludicrous. I finally tried /loop last week and discovered every loop iteration passes the entire context history to the model. So pretty quickly you're running a full 1M context window, without cache, likely just to check if something is ready or needs to be done. It's miserably terrible engineering unless your entire and only goal is to burn tokens.

brap2• 1 day ago

I think many of us engineers (including myself) are suffering from AI-induced procrastination. Fixing that pesky bug in production isn't fun. Building a harness/workflow that detects and fixes pesky bugs in production on its own is way more fun (especially when your employer pays for tokens), and it feels just as important, if not more! So what if it didn't actually fix that pesky bug in production on its own...

ubercore• 1 day ago

I never liked the term "ricing" with window managers, but this is definitely veering into LLM ricing territory sometimes. And people are getting rich off of it.

blmarket• 2 days ago

I don't think there is a lot test harness, given there is a golden implementation (sqlite3), with clear interface (SQL, and sqlite3 db files). Once you slop crappy implementation, then you can create one group of agents finding gap where slop doing wrong vs golden, and another group of agents filling the gap meeting the conformance.

I assume they did similar, as long as test case(or spec) can be parallelized. Having parallel execution / testing environment is crucial if we need 1000 commits/second throughput.

I would not surprised they have 1+ millions of specs.

I would bet they won't release generated specs - actually this will be a valuable asset like https://sqlite.org/th3.html

shay_ker• 2 days ago

How do we know if these models weren’t trained on Turso’s rewrite of SQLite in Rust?

It seems both likely that they were and impossible to remove that code from pretraining. Doesn’t that make this just about LLM memorization of the training set? What am I missing?

handfuloflight2• 2 days ago

This is more about automated long horizon work.

adamtaylor_13• 2 days ago

Wouldn't that be in the authors' interest to disclose, if true?

IanCal• 2 days ago

What am I missing?

They’re testing the same models on the same task but different ways of organising the swarms and the new approach works better.

pianopatrick• 2 days ago

I wonder what would happen if the agents did not have the spec. Like if you just said "build a simple SQL database that works as a single file", what would the agent swarm come up with.

bofadeez• 2 days ago

The issue is that the only models that could be trusted to work autonomously cost more than a human employee

uf00lme• 2 days ago

Cursor is saying we need to get improve the harness and tooling, but the models are getting better. One would have to assume that will continue and that cheaper/smaller models will continue to become even more useful.

onion2k2• 2 days ago

If there's a human who could write SQlite in an hour we should be paying them at least that much.

vessenes• 2 days ago

This is super fascinating, and I loved seeing testing of where exactly you need frontier intelligence -- looks like coordination / planning, but not coding right now -- the article's a bit of a tease, as we can't play with such a harness, or their new version control system or get a workable artifact out of it.

That said, I love the work on figuring out these harness coordination jobs. While there are analogs to human management there is also this enticing feeling that, since the models are broadly deterministic, we might be able to get repeatable science-type lessons about managing them with enough testing.

doctoboggan• 2 days ago

I feel bad for the engineers at Cursor who have to use Grok in these sorts of experiments.

ryankuykendall• 2 days ago

They received $60 billion to use grok and make it better. I think they going to be okay.

dougSF702• 2 days ago

Can a swarm of agents write the 835 page manual?

overgard2• 2 days ago

Good news, the new manual will be 83500 pages.

lubujackson• 2 days ago

That's 100x productivity right there.

dymk• 2 days ago

This post describes ideas found in existing agent orchestrator systems since they've existed. You'll find the same "top level agent that breaks a problem down into spec / plan / implement / verify" pattern in get-shit-done or superpowers.

Is the original part the VCS that works at 1000 commits/second? Or spending a bunch of money comparing models?

smoyer2• 2 days ago

This is almost a year behind Steve Yegge's first post on beads. Gas Town and Gas City provide orchestration for the swarm. So far I haven't seen a perfect implementation but this idea isn't new.

nullsanity• 2 days ago

Does every peice of prose published here have to be novel? is it not enough for you to read a new. perspective?

sudb• 2 days ago

I don't think Gas Town was actually all that much ahead of the curve - see Cursor's swarm post from January this year, mentioned at the very top of this blog post.

I also don't know that beads is a very strong example that Yegge knows what he's doing - it's famously been described as (pseudo)malware.

IceDane3• 2 days ago

Everything Steve yegge has done has been trash. That's why nobody is talking about beads or gas town. It was clear even in the beginning that it was a borderline AI-psychosis-fueled trash fire.

anentropic• 1 day ago

At the same time a lot of the stuff in this Cursor post sounds like things that Gastown was doing, albeit more sober and thought through

smoyer2• 1 day ago

Agreed ... I'm not talking about the implementation but rather the idea that this research is novel.

timcobb• 2 days ago

One reading is that it was more productive. Another is that most of those commits were busywork (thrash, contention, churn).

Kudos

alienbaby• 2 days ago

Tinfoil hat: what if the prices are artificially inflated deliberately to price out casual users being able to field large swarms of frontier model agents because it could be just too dangerous.

onion2k2• 2 days ago

The fact that the swarm needs high quality docs to work from will keep most of the tech industry safe.

trevswarm• 2 days ago

Even at a much smaller scale of a few dozen parallel agents working I've found real benefit to a structured hierarchy of agents. Not just keeping the context clean and focused at the right level, but if you find that something has just gone off the rails, you can more easily rip out or even just fully delete a leg of work without damaging the original design. It's also nice when you find a design change is needed and you can communicate that to the top level and have it efficiently propagate down where it matters.

I like it so much I even recorded a video walking through the process!

https://www.youtube.com/watch?v=efUmcCiRoDU (I do walk through how I do this in our app DevSwarm here)

agustechbro• 2 days ago

Wow, people have been working hard for a long time in Turso: https://turso.tech/blog/introducing-limbo-a-complete-rewrite... to do the same cursor did in justo no time?

edg50002• 2 days ago

I initially thought that getting agents to work for longer and in large groups was the future, but I'm increasingly thinking that, at least for engineering, just one thread makes more sense. The agent pulls things into context as needed. One thing that I've been experimenting with is also letting the agent remove things from context, such as files. But just adding to the context and compacting when it's full seems like it might beat a lot of more advanced options. Because the model is good, it knows what to put in the summary; just enough for the model to be able to rebuild the context from that seed (e.g. pulling in relevant files/data into the context).

Schlagbohrer6• 1 day ago

Have you used this method yourself for long workflows and with contexts approaching 1 million tokens? It doesn't work very well. LLM context is nearly half unusable.

noosphr• 1 day ago

I've found that model performance starts substantially degrading after 10%.

edg50002• 14 hours ago

I always let GPT 5.5/5.6 sol reach compatction. I'm only offered a 258K window, but I think once they release 1M to normal plan users, it will be usable across the whole 1M. At least with Opus I could use the whole 1M. I'd argue that the more context I use, the better performance gets. It has more info already available. I don't notice degradation.

asim• 2 days ago

Anything architectural like this is super interesting. We keep getting more powerful models sure. We're getting agents ok. But this coordination of many agents. This is the part that's really going to scale very fast and across many many machines in parallel. Very cool.

Schlagbohrer6• 2 days ago

Amazing test. I wish I could spend $2000 of compute on having my own swarm build some weird piece of software with 1000 commits per second.

lifty• 1 day ago

Perhaps you can get hired by a company to do that. You can't compare yourself as an individual with a company with a large budget.

kademolu2• 1 day ago

If we take coding off the table i don't see the case for large agent swarms. More is not always better in my experience experimenting with agents

s08148692• 1 day ago

If you can't see cases for it you may need some glasses. Replace the graph in the docs of Top-level planner -> mid-level planners -> worker leaf nodes and you get an org chart of CEO -> executives, N-levels of management, and ICs at the leafs. It directly maps to almost any office-based company that can operate completely digitally.

It's obviously in its infancy but fully agentic companies are becoming a possibility, given the right harness, models and tools

kademolu2• 15 hours ago

Lets separate "workers" from "decision seats", you might need a lot of workers which typically don't actual require inference and might actually just be cheap solvers but actually less decision seats. So i guess it depends on what you are calling agents in your scenario.

dctwin• 1 day ago

Am I reading this right? Opus + Composer did a comparable job to Fable, at ~1/19th the price, and half the LoC?

LostMyLogin• 1 day ago

I'm struggling to use Fable correctly. Everything I do ends up getting flagged and reverted back to Opus. Need to do some digging.

Footprint0521• 1 day ago

Anthropic wants it that way so they can’t be sued… the correct answer is don’t, switch to Kimi K3 for much more usage, the same quality of model, and no hidden practices :/

KyleTheDev• 1 day ago

Yea, looks like it (at least for their v2). Fable did much better in V1, but once they added their tooling around it, Opus + Composer ended up doing better (>0.8 Grade on the SQLite chart) for the final product. Granted, Fable reached a 'good' result (0.7~) in a much faster timeframe.

espetro• 1 day ago

Point is, frontier models don't necessarily are better than a swarm of non-frontier ones for these scoped problems. IMO frotnier models excel at underspec'd or more complex problems where ideation and exploration are key (and that's where they become crazy expensive).

notahan2• 1 day ago

These tests are interesting but I can't help feeling that these are benchmaxxing results. "We made our harness make sqlite in rust" sounds like a much easier probelm statement than having to recreate an application like facebook. I think the concept of having to integrate is the real challenge. Re-writing code in new languages based on decent docs is a difficult but less useful measure of how AI can help replace engineers.

Imagine this: you need to build a new CRUD app but your AI agent spends 20M toks on just writing it's own custom login function, that's a waste of time and money. I would not like to have a new system developed for me in such cases. I think we need a test where the agents are more specifically tasked to find technologies worth integrating and hence write the fewest lines of code.

lumost• 1 day ago

The nature of ML is to reach for easily measured proxy problems which can then be optimized. Why worry about a backoffice integrated micro-service you have no data on when you aren't sure that you can design and implement a rewrite of an OSS project?

paraschopra• 1 day ago

I think experiments like these are exciting, but they only work when you have the full suite against which you calibrate.

What I'd love to see is a small group of humans steering a million agents.

stillpointlab• 1 day ago

Today it is SQLite, tomorrow Linux?

I'm not sure of the long-term viability of these brute force swarm approaches, but clearly the AI companies dig the marketing they get out of it. But the fact that they get so far at this stage suggests we have a very interesting 2 years coming up.

thisismyopinion• 1 day ago

1000 commits of slop per second.

dougSF702• 1 day ago

They have invented software time travel: Write software => then write manual => then feed manual to cursor to write the software.

cregy• 3 hours ago

Seems an extreme system looking for a use case, but it's great to see research being done in this area

Agent swarms and the new model economics

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