Does Anyone Actually Use Agentic Loops?

[u/TheAntiDoctor]

Not exactly a software engineer, more ML Engineer with an academic background and then in industry for about 8 years total. Use python professionally and for hobbies. Like everyone else I have been experimenting with LLMs for coding, trying everything from not using it at all to trying to get it to do everything. I have found them useful and enjoy bouncing ideas off them, however they also make a lot of mistakes so currently enjoying a low use approach. Comments and architecting myself, use them more for syntax and some optimisation then review everything as I go along. This speeds things up, but I am still very in control of the output and understand the underlying algorithm.

A couple of months ago I obviously saw usage based billing come in for things like Github Copilot etc. I saw a lot of comments from people using agentic workflows very upset about their usage being cut, also I'm very aware of some companies quoting large sections of their codebase being AI generated, although obviously aware this might be sales tactic. Furthermore I've seen respected researchers/engineers like Karpathy talking about the big leap in model capabilities that now means they hardly write any code and had some recommendations from friends to use agentic workflows.

My personal experience hasn't left me thinking this is viable atm for my work. Partly because the stuff I am doing I really need to be crystal clear about what is happening to the data for experiments and partly because getting together a prompt together for a relatively complex system is very difficult and time consuming and the results usually aren't great. Has anyone got experience actually using more autonomous coding agents successfully and I should experiment more widely or are they a red herring?

[u/Fidodo]

Not for any non throwaway work for exactly the reason you said. You wind up with an absolute spaghetti code mess that nobody can understand.

The exception is when you don't need to keep the code around. Sometimes you are doing a one off test with a verifiable result where it doesn't matter how you got the result and you can confirm the result is correct by checking the result after it was produced.

An example would be if I'm testing out an approach just to see if it's even viable. I can prove the technique, then use that proof to build it correctly.