Is there a half-life for the success rates of AI agents?

Post by civilian » Wed, Jun 18, 2025, 5:20 PM UTC

Wowfunhappy wrote: post #69

mikeocool wrote: post #2

This very much aligns with my experience — I had a case yesterday where opus was trying to do something with a library, and it encountered a build error. Rather than fix the error, it decided to switch to another library. It then encountered another error and decided to switch back to the first library. I don’t think I’ve encountered a case where I’ve just let the LLM churn for more than a few minutes and gotten a go…

> I don’t think I’ve encountered a case where I’ve just let the LLM churn for more than a few minutes and gotten a good result. I absolutely have, for what it's worth. Particularly when the LLM has some sort of test to validate against, such as a test suite or simply fixing compilation errors until a project builds successfully. It will just keep chugging away until it gets it, often with good overall results in the…

Post by peacebeard » Wed, Jun 18, 2025, 5:47 PM UTC

alganet wrote: post #38

peacebeard wrote: post #18

Earlier quoted context omitted.

Very common to see in comments some people saying “it can’t do that” and others saying “here is how I make it work.” Maybe there is a knack to it, sure, but I’m inclined to say the difference between the problems people are trying to use it on may explain a lot of the difference as well. People are not usually being too specific about what they were trying to do. The same goes for a lot of programming discussion of c…

> People are not usually being too specific about what they were trying to do. The same goes for a lot of programming discussion of course. In programming, I already have a very good tool to follow specific steps: _the programming language_. It is designed to run algorithms. If I need to be specific, that's the tool to use. It does exactly what I ask it to do. When it fails, it's my fault. Some humans require algorit…

Post by autobodie » Wed, Jun 18, 2025, 5:59 PM UTC

Workaccount2 wrote: post #16

mikeocool wrote: post #2

This very much aligns with my experience — I had a case yesterday where opus was trying to do something with a library, and it encountered a build error. Rather than fix the error, it decided to switch to another library. It then encountered another error and decided to switch back to the first library. I don’t think I’ve encountered a case where I’ve just let the LLM churn for more than a few minutes and gotten a go…

They poison their own context. Maybe you can call it context rot, where as context grows and especially if it grows with lots of distractions and dead ends, the output quality falls off rapidly. Even with good context the rot will start to become apparent around 100k tokens (with Gemini 2.5). They really need to figure out a way to delete or "forget" prior context, so the user or even the model can go back and prune…

Post by dingnuts » Wed, Jun 18, 2025, 6:08 PM UTC

darepublic wrote: post #76

Workaccount2 wrote: post #16

Earlier quoted context omitted.

They poison their own context. Maybe you can call it context rot, where as context grows and especially if it grows with lots of distractions and dead ends, the output quality falls off rapidly. Even with good context the rot will start to become apparent around 100k tokens (with Gemini 2.5). They really need to figure out a way to delete or "forget" prior context, so the user or even the model can go back and prune…

There are just certain problems that they cannot solve. Usually when there is no clear example in its pretraining or discoverable on the net. I would say the reasoning capabilities of these models are pretty shallow, at least it seems that way to me

Post by vunderba » Wed, Jun 18, 2025, 6:23 PM UTC

matsemann wrote: post #74

vunderba wrote: post #43

Earlier quoted context omitted.

I've definitely seen this happen before too. Test-driven development isn't all that effective if the LLM's only stated goal is to pass the tests without thinking about the problem in a more holistic/contextual manner.

Reminds me of trying to train a small neural net to play Robocode ~10+ years ago. Tried to "punish" it for hitting walls, so next morning I had evolved a tanks that just stood still... Then punished it for standing still, ended up with a tanks just vibrating, alternating moving back and forth quickly, etc.