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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

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, code). It's just slob and will bite you in the ass in the long run anyhow.

la_fayette 1y ago
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?
- barbazoo 1y ago
  Extracting structured data from unstructured text comes to mind. We’ve built workflows that we couldn’t before by bridging a non deterministic gap. It’s a business SaaS but the folks using our software seem to be really happy with the result.
- anon191928 1y ago
  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?
  - dlisboa 1y ago
    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.
    - RamblingCTO 1y ago
      I think classical ML should still be compared when you use an LLM as a classifier. some problems are so well defined you can just drop an SVM on it and be done with it. the biggest benefit of LLMs I think is you can get results that are ok quite fast. no need to split or clean data, compare metrics etc. etc.
- RamblingCTO 1y ago
  you are 100% right. LLMs are perfect for anything that required heuristics. "is that project a good fit for client A given the following specifications ... rate it from 1-10". stuff like that. I use it as a solution for an undefined search space/problem essentially.
lxgr 1y ago
Human validation is certainly the most reliable way of introducing checkpoints, but there's others: Running unit tests, doing ad-hoc validations of the entire system etc.
- RamblingCTO 1y ago
  that goes without saying. but I'd argue: HITL is more of a workflow pattern, the rest are engineering patterns
murukesh_s 1y ago
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.
- RamblingCTO 1y ago
  I just left my CTO job for personal reasons. we tried coding agents, agentic coding, LLM-driven coding whatever. the code any of these generate is subpar (a junior would get the PR rejected for what it produces) and you just waste so much time prompting and not thinking. people don't know the code anymore, don't check the code and it's all just gotten very sloppy. so not hiring coders because of AI is a dangerous thing to do and I'd advise heavily against it. your risk just got way higher because of hype. maintainability is out of the window, people don't know the code and there are so many hidden deviations to the spec that it's just not worth it. the truth is that we stop thinking when we code like that.
  - murukesh_s 1y ago
    You are misunderstanding coding vs logic. Coding is making our logic (which is creativity) fit into someone else's syntax. If a machine is able to translate your logic expressed in your language into running code, whats wrong? Common thats our dream isn't it? Its like you not using calculator because you are worried kids won't learn how to divide. Is assembly language better than C++? Yes - did that prevented high level languages from taking over the world? No. If done right we all code through spec written in English, not code.
stillsut 1y ago
Yes I agree: highly-focused-scope + low-stakes + high-chorelike-task is the sweet spot for agents currently. I wrote a little about one such task, getting agents to supplement my markdown dev-log here: https://github.com/sutt/agro/blob/master/docs/case-studies/aba-5.md https://github.com/sutt/agro/blob/master/docs/case-studies/a...

la_fayette 1y ago

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?

barbazoo 1y ago
Extracting structured data from unstructured text comes to mind. We’ve built workflows that we couldn’t before by bridging a non deterministic gap. It’s a business SaaS but the folks using our software seem to be really happy with the result.
anon191928 1y ago
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?
- dlisboa 1y ago
  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.
  - RamblingCTO 1y ago
    I think classical ML should still be compared when you use an LLM as a classifier. some problems are so well defined you can just drop an SVM on it and be done with it. the biggest benefit of LLMs I think is you can get results that are ok quite fast. no need to split or clean data, compare metrics etc. etc.
RamblingCTO 1y ago
you are 100% right. LLMs are perfect for anything that required heuristics. "is that project a good fit for client A given the following specifications ... rate it from 1-10". stuff like that. I use it as a solution for an undefined search space/problem essentially.

lxgr 1y ago

Human validation is certainly the most reliable way of introducing checkpoints, but there's others: Running unit tests, doing ad-hoc validations of the entire system etc.

RamblingCTO 1y ago
that goes without saying. but I'd argue: HITL is more of a workflow pattern, the rest are engineering patterns

murukesh_s 1y ago

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.

RamblingCTO 1y ago
I just left my CTO job for personal reasons. we tried coding agents, agentic coding, LLM-driven coding whatever. the code any of these generate is subpar (a junior would get the PR rejected for what it produces) and you just waste so much time prompting and not thinking. people don't know the code anymore, don't check the code and it's all just gotten very sloppy. so not hiring coders because of AI is a dangerous thing to do and I'd advise heavily against it. your risk just got way higher because of hype. maintainability is out of the window, people don't know the code and there are so many hidden deviations to the spec that it's just not worth it. the truth is that we stop thinking when we code like that.
- murukesh_s 1y ago
  You are misunderstanding coding vs logic. Coding is making our logic (which is creativity) fit into someone else's syntax. If a machine is able to translate your logic expressed in your language into running code, whats wrong? Common thats our dream isn't it? Its like you not using calculator because you are worried kids won't learn how to divide. Is assembly language better than C++? Yes - did that prevented high level languages from taking over the world? No. If done right we all code through spec written in English, not code.

stillsut 1y ago

Yes I agree: highly-focused-scope + low-stakes + high-chorelike-task is the sweet spot for agents currently. I wrote a little about one such task, getting agents to supplement my markdown dev-log here: https://github.com/sutt/agro/blob/master/docs/case-studies/aba-5.md https://github.com/sutt/agro/blob/master/docs/case-studies/a...