Same experience. I started in mid 2023 building agents. The agents that are still working on production - they do one specific thing and the only coordination/integration I allow is via automation framework based on deterministic api only.
The current hype around autonomous agents, and what actually works in production
231–240 of 268 posts
Re: The current hype around autonomous agents, and what actually works in production
#232Re: The current hype around autonomous agents, and what actually works in production
#233These 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…
That’s a common fallacy. I suggest you make a plot of failure rate vs amount of components that can fail, any one of them failing leading to a total failure. You’ll be shocked by how quickly you get terrible numbers.
I talk about it from experience. How else do you think people are training RL agents if not based on verifiers? You don't have to verify every output at every step, you just need enough to course correct the agent and catch early when it's going wrong. That is the exact fallacy I was trying to address. The optimization comes from realizing the critical checks and then what passes to the next step. Requires letting go of the previous thinking and changing paradigms.
The failure rate is high because you view it in series. At test time you need to know what is correct from the options (including nothing correct), you dont need to know why it failed. You can debug later. The challenge is how easily can you return to the right track.
Re: The current hype around autonomous agents, and what actually works in production
#234These 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…
> Many tasks have easier verifications than doing the task. 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 interfa…
This is not an NxM verifier hell. I explicitly talked about one way which is parallel generation + classifier. You can also use majority voting here. Both would give you the right answer at each step without having to write code or test cases, just a simple prompt. There are more ways to do the same, eg: verifier blocks, layering, backtracking search (end to end assertions and then see which step went wrong), simple generative verifiers with simpler prompts and so on.
For non software world, people use majority voting most of the time.
Re: The current hype around autonomous agents, and what actually works in production
#235One thing I'll add that isn't touched on here is about context windows. While not "infinite", humans have a very large context window for problems they're specialized in solving. Models can often overcome their context window limitations by having larger and more diverse training sets, but that still isn't really a solution to context windows. Yes, I get the context window increases over time and that for many purpos…
> humans have a very large context window for problems they're specialized in solving Do they? I certainly don't. I don't know if it's my memory deficiency, but I frequently hit my "context window" when solving problems of sufficient complexity. Can you provide some examples of problems where humans have such large context windows?
> Do they? I certainly don't. I don't know if it's my memory deficiency, but I frequently hit my "context window" when solving problems of sufficient complexity.
Human context windows are not linear. They have "holes" in them which are quickly filled with extrapolation that is frequently correct.
It's why you can give a human an entire novel, say "Christine" by Stephen King, then ask them questions about some other novel until their "context window" is filled, then switch to questions about "Christine" and they'll "remember" that they read the book (even if they get some of the details wrong).
> Can you provide some examples of problems where humans have such large context windows?
See above.
The reason is because humans don't just have a "context window", they have a working memory that is also their primary source of information.
IOW, if we change LLMs so that each query modifies the weights (i.e. each query is also another training data-point), then you wouldn't need a context window.
With humans, each new problem effectively retrains the weights to incorporate the new information. With current LLMs the architecture does not allow this.
Re: The current hype around autonomous agents, and what actually works in production
#236I spoke with an Amazon AI production engineer who’s talking with prospective clients about implementing AI in our business. When a colleague asked about using generative AI in customer facing chats the engineer said he knows of zero companies who don’t have a human in the loop. All the automatic replies are non-generative “old” tech. Gen AI is just not reliable enough for anyone to stake their reputation on it.
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Re: The current hype around autonomous agents, and what actually works in production
#237I spoke with an Amazon AI production engineer who’s talking with prospective clients about implementing AI in our business. When a colleague asked about using generative AI in customer facing chats the engineer said he knows of zero companies who don’t have a human in the loop. All the automatic replies are non-generative “old” tech. Gen AI is just not reliable enough for anyone to stake their reputation on it.
Plenty of tech companies have started using gen AI for live chat support. Off the top of head I know off sonder.com and wealthsimple.com. If the LLM can’t answer a query it usually forwards the chat to a human support agent.
"This tech works fine as long as you have a back up for when it frequently fails"
Re: The current hype around autonomous agents, and what actually works in production
#238Earlier quoted context omitted.
If you think human reliability when writing code is more than 99%, have I got news for you!
If you’re going by bugs per lines of code, I’m far higher than 99% reliable.
When measuring the reliability of my vacuum, I don't go by component, so I don't go by line when I measure the reliability of my code.
Re: The current hype around autonomous agents, and what actually works in production
#239> 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/
They've built 12+ products with a full time job for the last 3 years Something seems off about that...
His full time job is building AI systems for others (and the article is a well written promo piece).
If most of these are one-shot deterministic workflows (as opposed of input-llm-tool loop usually meant by the current use of the term "ai agent"), it's not hard to assume you can build, test and deploy one in a month on average.
Re: The current hype around autonomous agents, and what actually works in production
#240I spoke with an Amazon AI production engineer who’s talking with prospective clients about implementing AI in our business. When a colleague asked about using generative AI in customer facing chats the engineer said he knows of zero companies who don’t have a human in the loop. All the automatic replies are non-generative “old” tech. Gen AI is just not reliable enough for anyone to stake their reputation on it.
Gen AI can only support people. In our case it scans incoming mails for patterns of order or article numbers, if the customers is already know, etc.
That isn't reliable either, but it supports the person who gets the mail on his desk in the end.
We sometimes get handwritten service protocols and the model we are using is very proficient in reading handwritten notes which you would have difficulties to parse yourself.
It works most of the time, but not often enough that AI could give autogenerated answers. For service quality reasons we don't want to impose any chatbot or AI on a customer.
Also data protection issues arise if you use most AI services today, so parsing customer contact info is a problem as well. We also rely on service partners to tell the truth about not using any data...