I don't even buy that the linked paper justifies the claim. All the paper does is draw multiple samples from an LLM and take the majority vote. They do try integrating their majority vote algorithm with an existing multi-agent system, but it usually performs worse than just straightforwardly asking the model multiple times (see Table 3). I don't understand how the author of this article can make that claim, nor why they believe the linked paper supports it. It does, however, support my prior that LLM agents are snake oil.

>Has anyone heard of that actually playing out practically in real-world applications?

Yes. I've build tools which do this.

Unfortunately the price tag for building a system that makes agent swarms dynamic is too much for anyone to bear in the current market.

Originally term "agency" was used to indicate inability of computers to act autonomously, intentionally or with purpose.

Then few developers published joke projects of automated browsers that pointlessly doomscroll random webpages saying "here are your agents".

Then few companies transformed this joke into "rag" agents - but there automated browsers don't act autonomously, they plagiarize web content according to prompt.

Then many websites started to block rag agents, and to hide it companies fell back to return data from LLMs, occasionally updating responses in the background (aka "online models").

The idea of plagiarizing content is also mixed with another idea: if LLM rewrites plagiarized content multiple times, it becomes harder to proof.

Obviously, none of involved companies will admit to plagiarizing. Instead, they will cover themselves with the idea that it leads to superintelligence. For example, if multiple neural networks repeat that 9.11 > 9.9, it will be considered more accurate[1].

[1] https://www.reddit.com/r/singularity/comments/1e4fcxm/none_o...

Thirty recursive loops of LLMs perform better than one prompt? I should hope so!

But how much power does it need?

I love this paper though I think their use of "agent" is confusing. My takeaway is ensembling LLM queries is more effective than making a single query. We used a similar approach at work and got a ~30% increase in [performance metric]. It was also cost efficient relative to the ROI increase that came with the performance gains. Simpler systems only require the number of output generations to increase, meaning input costs stay the same regardless of the size of the ensemble.

Thanks for this paper! Still early so not quite production, but I’ve seen positive results on tasks scaling the number of “agents”. I more or less think of agents as a task focused prompt and these agents are slight variations of the task. It makes me think I’m running some kind of Monte Carlo simulation.

"agent" is a buzzword. All it is, is a bunch of LLM calls in a while loop.

- 'rag' is meaningless as a concept. imagine calling a web app 'database augmented programming'. [1]

- 'agent' probably just means 'run an llm in a loop' [1]

[1] https://x.com/atroyn/status/1819396701217870102

I bet they spend 95% of their time in agile refinement meetings.

I spent the last year engineering to the point I could try this and it was ___massively___ disappointing in practice. Massively. Shockingly.

The answers from sampling every big model, then having one distill, were not noticeably better than just from Gpt-4o or Claude Sonnet, and the UX is so much worse (2x wait) that I tabled it for now.

I assumed it would be obviously good, even just from first principles.

I didn't do my usual full med/law benchmarks because given what we saw from a small sample, only 8 questions, I can skip adding it and proceed down the TODO list for launch.

I've also done the inverse, reproduced better results on med with gpt4o x one round RAG x one answer, than Google's Gemini Med insanely complex "finetune our biggest model on med, then do 2 round RAG with 5 answers + a vote on each round, and an opportunity to fetch new documents in round 2". We're both near-saturation, but I got 95% on my random sample of 100 Qs, Med Gemini was 93%.

I would check out this company, Swarms (https://github.com/kyegomez/swarms) who's working with enterprises to integrate multi-agents. But definitely a great point to focus on, the research paper mentions that the scaling of performance reduces with complexity of the task, which is definitely true for SWE

It was a common theme in Apple WWDC conferences in the late 80's. Alan Kay has an interesting talk about agents.

I think it could be argued that the low hanging fruit aspect of agents where fulfilled by microservices and web based businesses. The concept of webpages populated with relevant data like google search pages, or Amazon populating products you'd like, could be called agent based. Netflix could be an example of an agent based service.

Or CORBA based intelligent agents in the 1990s

Clippy?

I mean they are truly the dream of every capitalist. Intelligent agents basically mean free money. Of course this is something Microsoft would be talking about for decades.

> Where you specify a top-level objective, it plans out those objectives, it selects a completion metric so that it knows when to finish, and iterates/reiterates over the output until completion?

I built Plandex[1], which works roughly like this. The goal (so far) is not to take you from an initial prompt to a 100% working solution in one go, but to provide tools that help you iterate your way to a 90-95% solution. You can then fill in the gaps yourself.

I think the idea of a fully autonomous AI engineer is currently mostly hype. Making that the target is good for marketing, but in practice it leads to lots of useless tire-spinning and wasted tokens. It's not a good idea, for example, to have the LLM try to debug its own output by default. It might, on a case-by-case basis, be a good idea to feed an error back to the LLM, but just as often it will be faster for the developer to do the debugging themselves.

1 - https://plandex.ai

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To this date, ChatGPT Code Interpreter is still the most impressive implementation of this pattern that I've seen.

Give it a task, it writes code, runs the code, gets errors, fixes bugs, tries again generally until it succeeds.

That's over a year old at this point, and it's not clear to me if it counts as an "agent" by many people's definitions (which are often frustratingly vague).

Well, you have Cognition AI and Devin that became a recent unicorn startup (partnerships with Microsoft and stuff) but true, I can't think of an agent that actually lives up to the hype (heard Devin wasn't great).

That's how words form

"Agentic" is a term of art from psychology that's diffused into common usage. It dates back to the 1970s, primarily associated with Albert Bandura, the guy behind the Bobo doll experiment.

From ChatGPT: Other examples include "heuristic," "cognitive dissonance," "meta-cognition," "self-actualization," "self-efficacy," "locus of control," and "archetype."

Seems to have been for a while.

https://en.wiktionary.org/wiki/agentic

It's the new cerebral valley slang dude

Definitely not saying multi-agents is all you need for SWE-bench haha. I touch on this at the end of the blog post, where I mention jumps in progress require better base models or tooling.