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

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

#21

Post by accrual »

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've had some similar experiences. While I find agents very useful and able to complete many tasks on its own, it does hit roadblocks sometimes and its chosen solution can be unusual/silly.

For example, the other day I was converting models but was running out of disk space. The agent decided to change the quantization to save space when I'd prefer it ask "hey, I need some more disk space". I just paused it, cleared some space, then asked the agent to try the original command again.

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

#22

Post by heyitsguay »

peacebeard wrote: post #18
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…

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…

I've noticed this a lot, too, in HN LLM discourse.

(Context: Working in applied AI R&D for 10 years, daily user of Claude for boilerplate coding stuff and as an HTML coding assistant)

Lots of "with some tweaks i got it to work" or "we're using an agent at my company", rarely details about what's working or why, or what these production-grade agents are doing.

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

#23

Post by vadansky »

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 had a particularly hard parsing problem so I setup a bunch of tests and let the LLM churn for a while and did something else.

When I came back all the tests were passing!

But as I ran it live a lot of cases were still failing.

Turns out the LLM hardcoded the test values as “if (‘test value’) return ‘correct value’;”!

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

#24

Post by didgeoridoo »

onlyrealcuzzo wrote: post #10
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…

> If it doesn’t solve an issue on the first or second pass, it seems to rapidly start making things up, make totally unrelated changes claiming they’ll fix the issue, or trying the same thing over and over. Sounds like a lot of employees I know. Changing out the entire library is quite amusing, though. Just imagine: I couldn't fix this build error, so I migrated our entire database from Postgres to MongoDB...

Probably had “MongoDB is web scale” in the training set.

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

#25

Post by bwfan123 »

PaulHoule wrote: post #13

This was always my mental model. If you have a process with N steps where your probability of getting a step right is p, your chance of success is pᶰ, or 0 as N → ∞. It affects people too. Something I learned halfway through a theoretical physics PhD in the 1990s was that a 50-page paper with a complex calculation almost certainly had a serious mistake in it that you'd find if you went over it line-by-line. I thought…

> It affects people too. Something I learned halfway through a theoretical physics PhD in the 1990s was that a 50-page paper with a complex calculation almost certainly had a serious mistake in it that you'd find if you went over it line-by-line.

Interesting, and I used to think that math and sciences were invented by humans to model the world in a manner to avoid errors due to chains of fuzzy thinking. Also, formal languages allowed large buildings to be constructued on strong foundations.

From your anecdote it appears that the calculations in the paper were numerical ? but I suppose a similar argument applies to symbolic calculations.

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

#27

Post by einrealist »

So as the space for possible decisions increases, it increases the likelihood of models to end up with bad "decisions". And what is the correlation between the increase in "survival rate" and the increase in model parameters, compute power and memory (context)?

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

#28

Post by codeflo »

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…

I wonder to what extent this might be a case where the base model (the pure token prediction model without RLHF) is "taking over". This is a bit tongue-in-cheek, but if you see a chat protocol where an assistant makes 15 random wrong suggestions, the most likely continuation has to be yet another wrong suggestion.

People have also been reporting that ChatGPT's new "memory" feature is poisoning their context. But context is also useful. I think AI companies will have to put a lot of engineering effort into keeping those LLMs on the happy path even with larger and larger contexts.

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

#29

Post by kossae »

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…

This is my experience as well, and for now comes down to a workflow optimization. As I feel the LLM getting off track, I start a brand new session with useful previous context pasted in from my previous session. This seems to help steer it back to a decent solution, but agreed it would be nice if this was more automated based off of user/automated feedback (broken unit test, "this doesn't work", etc.)

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

#30

Post by nico »

skerit wrote: post #9
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. Is this with something like Aider or CLine? I've been using Claude-Code (with a Max plan, so I don't have to worry about it wasting tokens), and I've had it successfully handle tasks that take over an hour. But getting there isn't super easy, that's true. The instructions/CLAUDE.md file need…

> I've had it successfully handle tasks that take over an hour

What kind of tasks take over an hour?

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