6 ms·

The craziest for me is companies that sticking stochastic agents into automated business processes and expecting stable/reliable outcomes. Businesses want deter

The craziest for me is companies that sticking stochastic agents into automated business processes and expecting stable/reliable outcomes. Businesses want deterministic processes in the vast majority of cases.

barrkel 2mo ago
People are stochastic. You build reliable processes out of unreliable parts with feedback and self-correcting mechanisms. AI is not actually magically special in this regard. It has higher variance and we're still figuring out how to get all the tradeoffs right.
- gmerc 2mo ago
  [dead]
- Topfi 2mo ago
  Please. If you told a customer support rep that you are the former US president [0], they would not hand over the account straight away because you asked nicely. These models are great tools, but putting them and people on the same level does a disservice to our species and also is simply incorrect to what we know these models to be and their capabilities/limitations. [0] https://www.theguardian.com/technology/2026/jun/01/meta-ai-hack-obama-sephora-instagram https://www.theguardian.com/technology/2026/jun/01/meta-ai-h...
  - barrkel 2mo ago
    I didn't put them on the same level. At the same time, one should acknowledge that not all tasks are on the same level.
    - skydhash 2mo ago
      Most tasks we use computers for are deterministic and was coded for that specific quality. Introducing nom deterministic behavior is lowering the value of the app, especially for power users.
      - klibertp 2mo ago
        I don't think this is true. Computers are still computers, and code executes (mostly) deterministically. What computers struggle with is making sense of unclear inputs, precisely because of their determinism. OCR, speech-to-text, and computer vision all proved extremely limited when tackled purely deterministically. The whole "ML revolution" before LLMs was about recognizing that messy, noisy data can be interpreted with some accuracy using statistical methods. LLMs are the continuation of that: they are even harder to measure and completely impossible to prove, but they bring capabilities we have been unable to achieve any other way for decades. Should they be used to handle deterministic logic? No, obviously not - but they do enable computer systems to start working in contexts where they either couldn't at all before, or were just bad at them.
- rowanG077 2mo ago
  The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. That is to say I do agree that building reliable processes out of unreliable parts with feedback is the modus operandi. However AI cannot meaningfully handle feedback and learn. And that is a key unsolved problem.
  - rahidz 2mo ago
    "AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck." please take a look at the error(s) made in the prior run. what could've been done better? create or modify an existing skill to emphasize this, or suggest additional language in AGENTS.md.
    - drdexebtjl 2mo ago
      It will return a bunch of relevant-sounding insight, modify skills and context files… Then do the same error again. We’re not at the point where AI is capable of knowing what went wrong and self-aware enough to understand how it could reliably change its own behavior. For months I’ve been trying to have the agents stop manually writing our auto-generated SQL migrations and run the command that generates them instead. SOTA models insist on occasionally getting it wrong.
  - embedding-shape 2mo ago
    > The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. I feel like this line of thinking is kind of an unfair comparison. I'm not saying LLMs are magical beings that can suddenly learn by themselves after getting something wrong, but your "person making mistake then being corrected" assumes you do tell the person about the mistake and tell them to avoid doing the same mistake in the future, but for the "LLM making mistake" example you then intentionally avoid letting the prompt being changed in response to the mistake, which would be the "then being corrected" part on the LLM side of the comparison. Similarly, if you just let a person make a mistake and don't let them know about the mistake, they might keep making that same mistake over and over again. If you update how you use the LLM as you discover what mistakes it does, just like you'd correct a person, then you can use an LLM and also the LLM can "be taught to not make that mistake again".
- csomar 2mo ago
  People live in very stochastic and volatile environments and they manage that in ways no LLMs currently ever can. (ie: imagine sending an LLM all the data - sensory/auditory/etc… - that a human receive) People’s job is to partially reign in this volatile environment by creating processes with stable output.
bob1029 2mo ago
I'm struggling with the assertion that these models cannot provide reasonably deterministic guarantees. I am using gpt to populate JSON objects conforming to a list of natural language constraints for purposes of generating fake customers. I am finding that gpt5+ never fucks up. Not even a little bit. I've ran this test hundreds of times with 20+ constraints and it's been perfect every time. Stable information yields stable control flow. Humans are much more likely to forget one of the many constraints during testing. This happy mistake may incidentally cover an edge but it also means we lose coverage elsewhere. I think whether or not the LLM should be allowed to directly author deterministic control flow (code) is mostly the same thing. If you have a lot of constraints you want to satisfy all at the same time, this can give you a hit very close to the ideal target very quickly. Not knowing exactly what you want is when the LLM takes you for a ride.
- tmountain 2mo ago
  We could probably debate this ad nauseam, so I'll just give you my most compelling arguments. 1) Writing code the "old fashioned" way (i.e., a Python program that does X, Y, Z) allows you to arrive at a battle tested solution that will not change over time. From a risk assessment perspective, the behavior is essentially immutable, allowing a business to guarantee consistent behavior over long periods of time. 2) Just because something hasn't happened to you do, does not mean that it will not happen. LLM are opaque. If you stay on the "happy path", you may see consistent behavior for long periods of time, but there's always potential for an edge case where something goes catastrophically wrong. This is without even opening the can of worms regarding prompt injection and intentional sabotage of a working system. 3) There are plenty of real world examples of an LLM spontaneously deleting data from a DB (or the entire DB) or otherwise going completely off the rails. These might seem hyperbolic, but it happened at our company (to a test DB, not production). The severity of errors that occur can be existential to a business' survival without the proper guard rails. 4) There's no concrete way to truly confirm understanding between an LLM and a human. It can tell you that it completely understands what you want, and then it can do exactly the opposite. Followed by, "my bad" (Claude's new favorite catch phrase). Code can be audited and even proven to be correct given the appropriate level of time and energy. My best results have been gleaned in using LLM to produce deterministic systems. I recognize everyone has different use cases and needs, but this seems to be the best use of the technology in my experience.
desterothx 2mo ago
People really need to read Dijkstras Go to statement considered harmful letter [1]. If the obscurity of go to for static analysis of the code was too much, of course bringing in a literal ai black box is harmful for stable processes. [1] https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.pdf https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.p...
johnisgood 2mo ago
Yeah because "works many times in a row" = "deterministic" to many people.
whstl 2mo ago
"Business processes" can also mean "building power point decks" and other things. But your point stands: for critical business processes that need predictability, we indeed need determinism.
sscaryterry 2mo ago
I can argue that by applying multiple stochastic processes, with a human in the loop, that you will (may) converge on something that is deterministic. You use tests/test vectors to prove this. We're no different to AI. The code we write to solve a particular problem can (and probably does) change from day to day, depending on your "mood", what you had for breakfast, if you've been fighting with your significant other, other problems/human emotions.
- cyanydeez 2mo ago
  the humans only there to take the blame. you arn't not goingto be a cyborg, nor is your idea of determinism ever going to last the cycle of the agents, no matter how many memorrry layers, skills and other context guardrails you place. You're there for blame, not much else. The systems are still going to churn garbage, but because it's a business, that business will rather pay less for garbage they can sell than pay you a living wage, eventuially.
  - sscaryterry 2mo ago
    I agree that we as humans are there to be held accountable. I can foresee a future where the "tech" guys will be blamed, whether or not those people are the software developers/engineers/programmers or the tech bros in Silicon Valley. Converging on a solution via an LLM is possible. The code, well, it wouldn't necessarily be the way a human would write it, better or not...

barrkel 2mo ago

People are stochastic. You build reliable processes out of unreliable parts with feedback and self-correcting mechanisms. AI is not actually magically special in this regard. It has higher variance and we're still figuring out how to get all the tradeoffs right.

gmerc 2mo ago
[dead]
Topfi 2mo ago
Please. If you told a customer support rep that you are the former US president [0], they would not hand over the account straight away because you asked nicely. These models are great tools, but putting them and people on the same level does a disservice to our species and also is simply incorrect to what we know these models to be and their capabilities/limitations. [0] https://www.theguardian.com/technology/2026/jun/01/meta-ai-hack-obama-sephora-instagram https://www.theguardian.com/technology/2026/jun/01/meta-ai-h...
- barrkel 2mo ago
  I didn't put them on the same level. At the same time, one should acknowledge that not all tasks are on the same level.
  - skydhash 2mo ago
    Most tasks we use computers for are deterministic and was coded for that specific quality. Introducing nom deterministic behavior is lowering the value of the app, especially for power users.
    - klibertp 2mo ago
      I don't think this is true. Computers are still computers, and code executes (mostly) deterministically. What computers struggle with is making sense of unclear inputs, precisely because of their determinism. OCR, speech-to-text, and computer vision all proved extremely limited when tackled purely deterministically. The whole "ML revolution" before LLMs was about recognizing that messy, noisy data can be interpreted with some accuracy using statistical methods. LLMs are the continuation of that: they are even harder to measure and completely impossible to prove, but they bring capabilities we have been unable to achieve any other way for decades. Should they be used to handle deterministic logic? No, obviously not - but they do enable computer systems to start working in contexts where they either couldn't at all before, or were just bad at them.
rowanG077 2mo ago
The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. That is to say I do agree that building reliable processes out of unreliable parts with feedback is the modus operandi. However AI cannot meaningfully handle feedback and learn. And that is a key unsolved problem.
- rahidz 2mo ago
  "AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck." please take a look at the error(s) made in the prior run. what could've been done better? create or modify an existing skill to emphasize this, or suggest additional language in AGENTS.md.
  - drdexebtjl 2mo ago
    It will return a bunch of relevant-sounding insight, modify skills and context files… Then do the same error again. We’re not at the point where AI is capable of knowing what went wrong and self-aware enough to understand how it could reliably change its own behavior. For months I’ve been trying to have the agents stop manually writing our auto-generated SQL migrations and run the command that generates them instead. SOTA models insist on occasionally getting it wrong.
- embedding-shape 2mo ago
  > The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. I feel like this line of thinking is kind of an unfair comparison. I'm not saying LLMs are magical beings that can suddenly learn by themselves after getting something wrong, but your "person making mistake then being corrected" assumes you do tell the person about the mistake and tell them to avoid doing the same mistake in the future, but for the "LLM making mistake" example you then intentionally avoid letting the prompt being changed in response to the mistake, which would be the "then being corrected" part on the LLM side of the comparison. Similarly, if you just let a person make a mistake and don't let them know about the mistake, they might keep making that same mistake over and over again. If you update how you use the LLM as you discover what mistakes it does, just like you'd correct a person, then you can use an LLM and also the LLM can "be taught to not make that mistake again".
csomar 2mo ago
People live in very stochastic and volatile environments and they manage that in ways no LLMs currently ever can. (ie: imagine sending an LLM all the data - sensory/auditory/etc… - that a human receive) People’s job is to partially reign in this volatile environment by creating processes with stable output.

bob1029 2mo ago

I'm struggling with the assertion that these models cannot provide reasonably deterministic guarantees. I am using gpt to populate JSON objects conforming to a list of natural language constraints for purposes of generating fake customers. I am finding that gpt5+ never fucks up. Not even a little bit. I've ran this test hundreds of times with 20+ constraints and it's been perfect every time. Stable information yields stable control flow. Humans are much more likely to forget one of the many constraints during testing. This happy mistake may incidentally cover an edge but it also means we lose coverage elsewhere. I think whether or not the LLM should be allowed to directly author deterministic control flow (code) is mostly the same thing. If you have a lot of constraints you want to satisfy all at the same time, this can give you a hit very close to the ideal target very quickly. Not knowing exactly what you want is when the LLM takes you for a ride.

tmountain 2mo ago
We could probably debate this ad nauseam, so I'll just give you my most compelling arguments. 1) Writing code the "old fashioned" way (i.e., a Python program that does X, Y, Z) allows you to arrive at a battle tested solution that will not change over time. From a risk assessment perspective, the behavior is essentially immutable, allowing a business to guarantee consistent behavior over long periods of time. 2) Just because something hasn't happened to you do, does not mean that it will not happen. LLM are opaque. If you stay on the "happy path", you may see consistent behavior for long periods of time, but there's always potential for an edge case where something goes catastrophically wrong. This is without even opening the can of worms regarding prompt injection and intentional sabotage of a working system. 3) There are plenty of real world examples of an LLM spontaneously deleting data from a DB (or the entire DB) or otherwise going completely off the rails. These might seem hyperbolic, but it happened at our company (to a test DB, not production). The severity of errors that occur can be existential to a business' survival without the proper guard rails. 4) There's no concrete way to truly confirm understanding between an LLM and a human. It can tell you that it completely understands what you want, and then it can do exactly the opposite. Followed by, "my bad" (Claude's new favorite catch phrase). Code can be audited and even proven to be correct given the appropriate level of time and energy. My best results have been gleaned in using LLM to produce deterministic systems. I recognize everyone has different use cases and needs, but this seems to be the best use of the technology in my experience.

desterothx 2mo ago

People really need to read Dijkstras Go to statement considered harmful letter [1]. If the obscurity of go to for static analysis of the code was too much, of course bringing in a literal ai black box is harmful for stable processes. [1] https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.pdf https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.p...

johnisgood 2mo ago

Yeah because "works many times in a row" = "deterministic" to many people.

whstl 2mo ago

"Business processes" can also mean "building power point decks" and other things. But your point stands: for critical business processes that need predictability, we indeed need determinism.

sscaryterry 2mo ago

I can argue that by applying multiple stochastic processes, with a human in the loop, that you will (may) converge on something that is deterministic. You use tests/test vectors to prove this. We're no different to AI. The code we write to solve a particular problem can (and probably does) change from day to day, depending on your "mood", what you had for breakfast, if you've been fighting with your significant other, other problems/human emotions.

cyanydeez 2mo ago
the humans only there to take the blame. you arn't not goingto be a cyborg, nor is your idea of determinism ever going to last the cycle of the agents, no matter how many memorrry layers, skills and other context guardrails you place. You're there for blame, not much else. The systems are still going to churn garbage, but because it's a business, that business will rather pay less for garbage they can sell than pay you a living wage, eventuially.
- sscaryterry 2mo ago
  I agree that we as humans are there to be held accountable. I can foresee a future where the "tech" guys will be blamed, whether or not those people are the software developers/engineers/programmers or the tech bros in Silicon Valley. Converging on a solution via an LLM is possible. The code, well, it wouldn't necessarily be the way a human would write it, better or not...