Discussion summary
Discussions focus on automating AI workflows, deterministic model workflows, and the challenges of integrating LLMs into existing systems. Participants mention efforts to improve reliability and tool-building practices.
What the discussion says
- Some emphasize the importance of deterministic workflows for reliability.
- Others highlight the proliferation of AI tools and the difficulty in choosing effective ones.
- Several discuss the need for better scaffolding and app layers for deployment.
“Model's input is in natural language which isn't formally defined.”
“LLMs are the new CPU.”
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- Hacker News
- I think scaffolds and the app layer are really the two big things needed for the deployment of AI in most use cases. In general, my company says for a given problem, we prefer deterministic software as the solution first, followed by LLMs, followed by humans. That's how we approach pretty much every problem. Yes, there are many things that we do with LLMs that we can eventually get to be done with software, and many things that are done by humans that we can get to be done by LLMs.by mchusma
- I was recently doing some work - reasonably repetitive and tedious.
I asked Claude to spin up a bunch of agents to do it and after a bit of discussion we ended up writing a bunch of deterministic scripts that ran off the data collated by some “research” agents.
It took a few pilot loops of the process to nail it down, but separating the process into “data collection” and “process the data” has pretty much eliminated the AI step. Once the data has been collected from the random sources and normalised into something sensible we rarely have to do it again.
Even that process has been largely automated, scripts that deterministically scrape data, the AI is only needed for the very difficult parts that need some decisions or interpretation.
Getting the AI to write tools for itself is a great way to work.
by iamflimflam1 - Second this, following Cloudflare's post on how they do agentic PR review, I'm working on a script that renders the conext and diff to disk before passing it off to the agent, which generates a jsonl file of comment add/update, which another script will process. Way better than handing it bash and clis so it can fumble about non deterministicallyby verdverm
- We used to (and still do) have things that could run commands and interpret them. These things would sometimes forget key parts to run or even forget to run them at all. So we invented a system where you could give instructions (code) and schedule when they would be run (cron etc). Those things were called humans.
There is a great article called "Manual Work is a Bug" [0]. The idea is that you have humans doing a lot of random things so you should:
- first make a list of the things they are doing
- then update the list with the commands they have to run for each step
- some of the steps won't have commands b/c it's things like "ask Bob what the limit should be"
- over time, the commands become scripts
- then the "ask Bob" becomes an API call
- one day, the whole thing is an automated system that runs code
People like to think that LLMs can do all of the above. I don't get this b/c code is deterministic and can be run repeatedly basically "for free" (at least compared to token spend).
I do think that LLMs can greatly accelerate the creation of the code/system etc and can also help with maintaining it but the whole "we will just version control the prompt" was clearly hogwash.
by alexpotato - Basically what I’ve been saying since OldJob forced LLMs down our throats and pegging performance to usage metrics: why the fuck are we handing deterministic processes to probabilistic systems when it should be the other way around (using probabilistic systems to design deterministic ones)?
LLMS should be abstracted out of a process as soon as practicable, replaced with deterministic processes or procedures. Otherwise you’ve built the world’s most fragile process at the mercy of token cost, vendor hostility, geopolitics, and model deprecation.
by stego-tech - This is exactly right. Abstracted out of the process, or to a point of most optimal application.
- I love the way you put this. Are there any sites or forums or places where people discuss/hash this out?
I've genuinely never considered it from this angle before.
by hadi121 - I think a big part is the misperception that it’s “easier” and less effort to run stuff through LLM than to design an effective deterministic process.
Would love to know how you’ve managed to counter this as the drive to throw everything at LLMs is driving me insane.
by sensecall - > … deterministic processes…
Just to be clear, software development itself is not deterministic, though? The software developer pushes a given business process from less-deterministic toward more deterministic? When we say we’ve “abstracted LLMs out of a process” we’d also say that we’ve abstracted software developers out that process as well?
by jt2190 - Thats the best description I have heard of the problem so far. I ran into this recently where I automated a ton of stuff and got essentially threatened by leadership for not using AI. My system produces the same output 100% of the time, is free, and scales plus is reliable. Doing what they wanted with an LLM was fragile, didn't always produce the same output and was subject to costs. I don't think they could wrap their brains around it.by datakan
- Actually... yes. I was bracing to be very annoyed with your comment starting with "why is everyone using AI so stupid?!" (I know those weren't your words, but it felt like that kind of post)
And then... yeah. You got it exactly right. Once a problem or process is deterministic, that's the wrong application of an LLM.
But I had never quite thought of it in these exact terms. The way I've been thinking about it up until now is that the very best way to use LLMs is to have them produce tools. The tools get to stay reliable and predictable. They boost your performance. But I think you found the more general abstraction of the same idea. Tool-making is not deterministic. But the tools themselves can be. That's why it fits. Trying to stuff LLMs into what's otherwise a deterministic process is an absurd waste and error-prone.
Smart. I like it.
by x3haloed - I’ve got a test that checks to see if “Logger” has been imported anywhere in my Elixir project, and if it finds one it prints out an explanation of why this project shouldn’t use Logger and what it should do instead. (Which is— emit OpenTelemetry events.)by natbennett
- This makes sense, although it's not well described here.
Formal methods, as in proof of correctness, have been around for decades (I was doing that stuff in the 1980s) but pushing the proofs through was too laborious. The seL4 verification effort reportedly used over a decade of people time.
The idea is that if you have a formal specification of what you want to happen, you can get a LLM to do the struggling with the proof system to get it right. It's a good task for an LLM, because there's feedback from the prover.
I'd like to see more non-trivial examples of this. People keep republishing verifications of greatest common divisor or stack algorithms, which was done decades ago.
by Animats - might be relevant: https://martin.kleppmann.com/2025/12/08/ai-formal-verificati...by gritzko
- Problem is, usually describing the problem you want to solve *correctly* using formal tool is a task as hard (and often, equivalent to) the implementation. That said, having a formal description is usefulby oulipo2
- This is a very interesting introduction to a blog post, but... I'm somehow missing the actual blog post. How does this stuff work in practice? What are some concrete examples? How does one get from JavaScript tokenizing things in a commit hook to validating that the LLM didn't disable tests it didn't agree with, or any other helpful property?by derdi
- You need to always be looking for what can be done deterministically and what can't. If it can, write a script or whatever is needed to make that happen. Your agent can help you figure this out. The agent becomes a glue layer for all your scripts. Use LLM judgement as an extra layer on top of a mechanical baseline.
> validating that the LLM didn't disable tests it didn't agree with
Provide a test runner and force the agent to call it. Have it emit something if you want evidence.
by chickensong - I am the author. I am trying to limit one post to one page. Most people here are reading reasoning all day, I am afraid. Might get tired.
I also aspire to make one post a day. To be continued.
by gritzko - Makes sense, I have had the biggest wins with AI by attacking nondeterminism whenever possible.
BTW, you should probably fix the Beagle link on your homepage: https://replicated.live/beagle/
by lubujackson - Thanks, fixed. The runtime[1] and the scripts[2] are the practical ones. I am separating the old repo[3] into submodules since submodule recursion became smooth in Beagle.
[1]: https://github.com/gritzko/jab
by gritzko - I find some of the most interesting, and catastrophic failures in my agent fine-tuning come from the clamping down of non-determinism. It is totally the correct approach, but must be handled delicately. The non-deterministic core remains, but now under bimodal pressure.
- A dumber but related habit I've gotten into is that if I want to use AI to do some sort of refactoring on a C# codebase, instead of asking it to edit the code directly I ask it to write a code transformation using the Roslyn compiler API, then run that on the code. The result is less likely to have subtle bugs if it appears to work and gets through a light code review on the transformation (i.e., attempts to cheat with weird special-casing are more likely to stand out amongst the Roslyn API code, and if there isn't such weird special-casing but the code is wrong, the result is more likely to be completely broken rather than subtly broken)by contextfree
- Is it open source, can you share? Have been pondering using source generators to achieve a similar effect and have the LLM build the source generator ‘framework’ that code would derive from rather than just generating the code itself, you create the code generation framework.by rubenvanwyk
- This sounds interesting, I am really naive. I don't code in C#, is there an analogy for other programming languages, like GO, or Python or Typescript?
Like are you prompting like:
--- I need code that does X,Y, and Z. Write it so that the Roslyn compiler on this machine can compile and the code passes the repo's styling and formatting requirements. ---
Or something else.
by twosdai - I think semi-automation with contextual and domain-specific tooling is the key to the best quality outcomes.
For example, with browser automation, giving the LLM raw access to the literal DOM generally results in disaster for tasks that need to be stable across more than 5-10 interactions. The better approach is to write an intermediate layer that understands each view and can provide a list of tools that are precisely tailored for each case. E.g.:
vshttps://myapp/Login - <raw dom - hundreds of kb> - Available Tools: <arbitrary javascript>
The later case takes a lot more effort, but it also reduces a Turing complete problem space into a binary decision at this particular step.https://myapp/login - We detected that this is the application's login page. - It has the following visible elements: + Username + Password + Login Button - Available Tools: + PerformLogin + Quitby bob1029 - Yea I’ve been warming up to the web mcp after doing more ai browser work and hitting so many pain points: https://developer.chrome.com/docs/ai/webmcpby braebo