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The current hype around autonomous agents, and what actually works in production

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Re: The current hype around autonomous agents, and what actually works in production

#151

Post by cosmic_cheese »

an0malous wrote: post #128
actinium226 wrote: post #123

Very nice article. The point about mathematical reliability is interesting. I generally agree with it, but humans aren't 100% reliable, or even 99% reliable, so how do we manage to create things like the Linux kernel or the Mars landers without AI? Clearly we have some sort of goal-based self-correction mechanism. I wonder if there's research into AI on that thread?

> Clearly we have some sort of goal-based self-correction mechanism. Humans can try things, learn, and iterate. LLMs still can't really do the second thing, you can feed back an error message into the prompt but the learning isn't being added to its weights so its knowledge doesn't compound with experience like it does for us. I think there are still a few theoretical breakthroughs needed for LLMs to achieve AGI and…

Additionally, LLMs still don’t truly understand anything, which is why they flounder so badly with e.g. writing code for a programming language or framework that it hasn’t seen a large enough set of training data for. Humans on the other hand do understand and generalize shared knowledge well, which is why we’re much better at handling that type of scenario.

More specific to agents, humans can also figure out how to use tools on the fly (even in the absence of documentation) where LLMs need human-built MCPs. This is also a significant limiting factor.

Re: The current hype around autonomous agents, and what actually works in production

#152

Post by jsemrau »

ivape wrote: post #126
jsemrau wrote: post #102

Earlier quoted context omitted.

Agents are more than that. Agents, besides tool use, also have memory, can plan work towards a goal, and can, through an iterative process (Reflect - Act), validate if they are on the right track.

If an agent takes a Topic A and goes down a rabbit hole all the way to Topic Z, you'll see that it won't be able to incorporate or backtrack back to Topic A without losing a lot of detail from the trek down to Topic Z. It's a serious limitation right now from the application development side of things, but I'm just reiterating what the article pointed out, which is that you need to work with fewer step workflows that…

Yes, that's commonly referred to as the Exploration-Exploitation Dilemma. Should the agent go deep or wide?

https://en.wikipedia.org/wiki/Exploration%E2%80%93exploitati...

Re: The current hype around autonomous agents, and what actually works in production

#154

Post by Rexxar »

mritchie712 wrote: post #76

> I've built 12+ production AI agent systems across development, DevOps, and data operations It's hard to make *one* good product (see startup failure rates). You couldn't make 12 (as seemingly a solo dev?) and you're surprised? we've been working on Definite[0] for 2 years with a small team and it only started getting really good in the past 6 months. 0 - data stack + AI agent: https://www.definite.app/

He didn't say he made 12 independent saleable products, he says he built 12 tools that fill a need at his job and are used in production. They are probably quite simple and do a very specific task as the whole article is telling us that we have to keep it simple to have something useable.

Re: The current hype around autonomous agents, and what actually works in production

#155

Post by oceanparkway »

I think the "math" on reliability-over-steps will end up differently than described here in the long term because getting new factual input from the real world should improve the reliability of the end state, and we have all observed agentic systems at this point producing that behavior at least sometimes (e.g., a test failure prompts claude code to refactor correctly).

Whether or not one term in this equation currently compounds faster is a good question, or under what circumstances, etc., but presenting agentic abilities as always flawed thinking resulting in impossible long term task execution isn't right. Humans are flawed and require long, drawn out multi task thinking to get correct answers, and interacting with and getting feedback from the world outside the mind during a task execution process typically raises the chance of the correct answer being spit out in the end.

I'd agree that the agentic math isn't great at the moment, but if it's possible to reduce hallucinations or raise the strength and frequency effect of real world feedback on the model, you could see this playing out differently perhaps quite soon. There's at least a couple of examples of "we're already there".

Re: The current hype around autonomous agents, and what actually works in production

#156

Post by 13zebras »

jvanderbot wrote: post #145
swader999 wrote: post #138

Earlier quoted context omitted.

Subscription?

I have one, and upgrades don't have unlimited access as far as I can tell. Correct me if I'm wrong. This cost scaling will be an issue for this whole AI employee thing, especially because I imagine these providers are heavily discounting.

Re: discounting… Given that OpenAI is burning billions and making trivial revenue in comparison, the cost per token is probably going to skyrocket when Sam runs out of BS to con the next investor. I’m guessing the only way that token cost doesn’t explode is if Claude ends up in Amazon’s hands and OpenAI is Microsoft’s. Then Amazon, Google, and MS can subsidize if they want. But as standalone businesses, they can’t make it at current token prices. IMHO

Re: The current hype around autonomous agents, and what actually works in production

#157

Post by murukesh_s »

RamblingCTO wrote: post #78

I also build agents/ai automation for a living. Coding agents or anything open-ended is just a stupid idea. It's best to have human validated checkpoints, small search spaces and very specific questions/prompts (does this email contain an invoice? YES/NO). Just because we'd love to have fully intelligent, automatic agents, doesn't mean the tech is here. I don't work on anything that generates content (text, images, c…

I am also building an agent framework and also used chat coding (not vibe coding) to generate work - I was easily able to save 50% of my time just by asking GPT.

But it generates mistakes like say 1 in 10 times and I do not see it getting fixed unless we drastically change the LLM architecture. In future I am sure we will have much more robust systems if the current hype cycle doesn't ruin its trust with devs.

But the hit is real, I mean I would hire a lot less If i were to hire now as I can clearly see the dev productivity boost.. Learning curve for most of the topics are also drastically reduced as the loss in Google search result quality is now supplemented by LLMs.

But thing I can vouch for is automation and more streamlined workflows. I mean having normal human tasks being augmented by an LLM in a workflow orchestration framework. The LLM can return its confidence % along with the task results and for anything less than ideal confidence % the workflow framework can fall back on a human. But if done correctly with proper testing, guardrails and all, I can see LLM is going to replace human agents in several non-critical tasks within such workflows.

The point is not replacing humans but automating most of the work so the team size would reduce. For e.g. large e-commerce firms have 100s of employees manually verifying product description, images etc, scanning for anything from typos to image mismatch to name a few. I can see LLMs going to do their job in future.

Re: The current hype around autonomous agents, and what actually works in production

#158

Post by iwontberude »

Filligree wrote: post #94
queenkjuul wrote: post #89

Earlier quoted context omitted.

My work has a corporate subscription and on the one hand it's very impressive and on the other i don't actually find it useful.

It’s best at small to medium projects written in a consistent style. So. It’s a potential superpower for personal projects, yet I don’t see it being very useful in a corporate setting. I used Claude Code to make this little thing: https://github.com/Baughn/ScriptView …took me thirty minutes. It wouldn’t have existed otherwise.

Exactly, if the program has less than 100 units or so of logic then it’s going to be a pretty good time so long as you aren’t working with very obscure/private dependencies.

The problems begin when integrating hundreds of units prompted by different people or when writing for work which is both prolific and secret. The context is too limited even with RAG, one would need to train a model filled with secret information.

So basically non-commercial use is the killer app thus far for Claude code. I am sure there are some business people who are not happy about this.

Re: The current hype around autonomous agents, and what actually works in production

#159

Post by corimaith »

actinium226 wrote: post #123

Very nice article. The point about mathematical reliability is interesting. I generally agree with it, but humans aren't 100% reliable, or even 99% reliable, so how do we manage to create things like the Linux kernel or the Mars landers without AI? Clearly we have some sort of goal-based self-correction mechanism. I wonder if there's research into AI on that thread?

Humans aren't 100% reliable but we can build tools that are 100% reliable to verify our predictions.

Re: The current hype around autonomous agents, and what actually works in production

#160

Post by dlisboa »

anon191928 wrote: post #150
la_fayette wrote: post #98

Earlier quoted context omitted.

In general I would agree, however the resulting systems of such an approach tend to be "just" expensive workflow systems, which could be done with old tech as well... Where is the real need for anything LLM here?

it would take months with old tech to create a bot that can check multiple websites for specific data or information? so LLM reduces the time a lot? am I wrong?

Months? Scraping wasn’t a hard problem then. Classifying information is a different and more complex thing, which is what these models are very good at. Then again we had other means of classification before LLMs without having to go through chat bots.
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