Earlier quoted context omitted.
I have no idea what agents are for, could be my own ignorance. That said, I have been using LLMs for a while now with great benefit. I did not notice anything missing, and I am not sure what agents bring to the table. Do you know?
You are a manual agent to LLMs when you use things like ChatGPT. You go through a workflow loop when you try to investigate and consult with an LLM. Agents are just trying to automate your workflow against an LLM. It's basically just scripting. Scripting these LLMs is where we all want to go, but the context window length is a limiting factor, as well as inferencing on any notable sized window. I'll manage my whiney…
The current hype around autonomous agents, and what actually works in production
181–190 of 268 posts
Re: The current hype around autonomous agents, and what actually works in production
#182Earlier quoted context omitted.
It's about what you do with errors. If you let them compound they lead to destruction, if instead you inspect, maintain, reinspect, replace, etc. you can manage them. My point was that something extremely complex, like a plane, works, because the system tries hard to prevent compounding errors.
That works because each plane is (nearly) exactly the same as the one before it and we have exact specifications for the plane. You can do maintenance, inspections, and replacement because of those specifications. In software the equivalent of blueprints is code. The room for variation outside software “specifications” is infinite. Human reliability when comes to assembling planes is also much higher than 99%, and LL…
Re: The current hype around autonomous agents, and what actually works in production
#183These are all solvable problems. The issue is given the race to get to a certain ARR quickly, many startups end up not focusing on these. There is some truth to AI agents being not as useful as their promise, but the problems mentioned are engineering problems, and once we start seeing them with a different lens, they would start working. (This is not to say I believe orchestration or multi step agents are a way to g…
Re: The current hype around autonomous agents, and what actually works in production
#184Earlier quoted context omitted.
> 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…
100% and it seems like we need a whole new architecture to get there, because right now training a model takes so much time. At the risk of making a terrible analogy, right now we're able to "give birth" to these machines after months of training, but once they're born, they can't really learn. Whereas animals learn something new every day, got to sleep, clean up their memories a bit, deleting some, solidifying other…
Re: The current hype around autonomous agents, and what actually works in production
#185Earlier quoted context omitted.
100% and it seems like we need a whole new architecture to get there, because right now training a model takes so much time. At the risk of making a terrible analogy, right now we're able to "give birth" to these machines after months of training, but once they're born, they can't really learn. Whereas animals learn something new every day, got to sleep, clean up their memories a bit, deleting some, solidifying other…
Maybe you're on to something. We need AI lions which will eat the models which don't learn or adapt enough.
Right now we train AI babies, dump them in the wild... and expect them to have all the answers.
Re: The current hype around autonomous agents, and what actually works in production
#186I just want someone to give me one legit use case where an AI Agent now enables them to do something that couldn’t be done before, and actually makes an impact on overall profit.
Re: The current hype around autonomous agents, and what actually works in production
#187Earlier quoted context omitted.
Real question: what's the best way to short AI right now?
Just short any of the publicly traded companies with AI based valuations? Nvida, Meta? Seems like an awful idea but I'm often wrong.
Re: The current hype around autonomous agents, and what actually works in production
#188These are all solvable problems. The issue is given the race to get to a certain ARR quickly, many startups end up not focusing on these. There is some truth to AI agents being not as useful as their promise, but the problems mentioned are engineering problems, and once we start seeing them with a different lens, they would start working. (This is not to say I believe orchestration or multi step agents are a way to g…
In the software world (like the article is talking about) this is the logic that has ruthlessly cut software QA teams over the years. I think quality has declined as a result.
Verifiers are hard because the possible states of the internal system + of the external world multiply rapidly as you start going up the component chain towards external-facing interfaces.
That coordination is the sort of thing that really looks appealing for LLMs - do all the tedious stuff to mock a dependency, or pre-fill a database, etc - but they have an unfortunate tendency to need to be 100% correct in order for the verification test that depends on them to be worth anything. So you can go further down the rabbit hole, and build verifiers for each of those pre-conditions. This might recurse a few times. Now you end up with the math working against you - if you need 20 things to all be 100%, then even high chances of each individual one starts to degrade cumulatively.
A human generally wouldn't bother with perfect verification of every case, it's too expensive. A human would make some judgement calls of which specific things to test in which ways based on their intimate knowledge of the code. White box testing is far more common than black box testing. Test a bunch of specific internals instead of 100% permutations of every external interface + every possible state of the world.
But if you let enough of the code to solve the task be LLM-generated, you stop being in a position to do white-box testing unless you take the time to internalize all the code the machine wrote for you. Now your time savings have shrunk dramatically. And in the current state of the world, I find myself having to correct it more often then not, further reducing my confidence and taking up more time. In some places you can try to work around this by adjusting your interfaces to match what the LLM predicts, but this isn't universal.
---
In the non-software world the situation is even more dire. Often verification is impossible without doing the task. Consider "generate a report on the five most promising gaming startups" - there's no canonical source to reference. Yet these are things people are starting to blindly hand off to machines. If you're an investor doing that to pick companies, you won't even find out if you're wrong until it's too late.
Re: The current hype around autonomous agents, and what actually works in production
#189OP here. I posted this, this morning and then promptly forgot about it. How come the title has been changed from the blog posts own?
Re: The current hype around autonomous agents, and what actually works in production
#190> Error rates compound exponentially in multi-step workflows. 95% reliability per step = 36% success over 20 steps. Production needs 99.9%+. This misses a key feature of agents though. They get feedback from linters, build logs, test runs and even screenshots. And they collect this feedback themselves. This means they can error correct some mistakes along the way. The math works out differently, depending on how well…
For context, relevant information from steps can be cherrypicked to next stage.
The math works differently because AI (mostly) ignores irrelevant results. So steps actually increase reliability overall.