Building an internal agent: Code-driven vs. LLM-driven workflows

galaxyLogic an hour ago | parent | next [-]

What I'm struggling with is, when you ask AI to do something, its answer is always undeterministically different, more or less.

If I start out with a "spec" that tells AI what I want, it can create working software for me. Seems great. But let's say some weeks, or months or even years later I realize I need to change my spec a bit. I would like to give the new spec to the AI and have it produce an improved version of "my" software. But there seems to be no way to then evaluate how (much, where, how) the solution has changed/improved because of the changed/improved spec. Becauze AI's outputs are undeterministic, the new solution might be totally different from the previous one. So AI would not seem to support "iterative development" in this sense does it?

My question then really is, why can't there be an LLM that would always give the exact same output for the exact same input? I could then still explore multiple answers by changing my input incrementally. It just seems to me that a small change in inputs/specs should only produce a small change in outputs. Does any current LLM support this way of working?

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mchonedev an hour ago | parent | next [-]

This is absolutely possible but likely not desirable for a large enough population of customers such that current LLM inference providers don't offer it. You can get closer by lowering a variable, temperature. This is typically a floating point number 0-1 or 0-2. The lower this number, the less noise in responses, but a 0 still does not result in identical responses due to other variability.

In response to the idea of iterative development, it is still possible, actually! You run something more akin to integration tests and measure the output against either deterministic processes or have an LLM judge it's own output. These are called evals and in my experience are a pretty hard requirement to trusting deployed AI.

	▲	galaxyLogic an hour ago \| parent [-]
		So, you would perhaps ask AI to write a set of unit-tests, and then to create the implementation, then ask the AI to evaluate that implementation against the unit-tests it wrote. Right? But then again the unit-tests now, might be completetly different from the previous unit-tests? Right? Or would it help if a different LLM wrote the unit-tests than the one writing the implementation? Or, should the unit-tests perhaps be in an .md file? I also have a question about using .md files with AI: Why .md, why not .txt?

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weslleyskah an hour ago | parent | prev | next [-]

Also, if a new method to solve the problem is discovered, the data will need to be modified to give the most effective way to implement the solution?

So, regardless of the architecture of your software, the AI will output the most updated version of the solution if the problem is common enough among large codebases?

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bitwize an hour ago | parent | prev [-]

Other concerns:

1) How many bits and bobs of like, GPLed or proprietary code are finding their way into the LLM's output? Without careful training, this is impossible to eliminate, just like you can't prevent insect parts from finding their way into grain processing.

2) Proompt injection is a doddle to implement—malicious HTML, PDF, and JPEG with "ignore all previous instructions" type input can pop many current models. It's also very difficult to defend against. With agents running higgledy-piggledy on people's dev stations (container discipline is NOT being practiced at many shops), who knows what kind of IDs and credentials are being lifted?

	▲	galaxyLogic 44 minutes ago \| parent [-]
		Nice analogue, insect-parts. I thhink that is the elephant in the room. I read Microsoft said something like 30% of their code-output has AI generated code. Do they know what was the training set for the AI they use? Should they be transparent about that? Or, if/since it is legal to do your AI training "in the dark" does that solve the problem for them, they can not be responsible for the outputs of the AI they use?

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David 3 hours ago | parent | prev | next [-]

> We still start all workflows using the LLM, which works for many cases. When we do rewrite, Claude Code can almost always rewrite the prompt into the code workflow in one-shot.

Why always start with an LLM to solve problems? Using an LLM adds a judgment call, and (at least for now) those judgment calls are not reliable. For something like the motivating example in this article of "is this PR approved" it seems straightforward to get the deterministic right answer using the github API without muddying the waters with an LLM.

	▲	soccernee 2 hours ago \| parent [-]
		Likely because it's just easier to see if the LLM solution works. When it doesn't, then it makes more sense to move into deterministic workflows (which isn't all the hard to build to be honest with Claude Code). It's the old principle of avoiding premature optimization.

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jaynate 3 hours ago | parent | prev | next [-]

It’s sort of difficult to understand why this is even a question - LLM-based / judgment dependent workflows vs script-based / deterministic workflows.

In mapping out the problems that need to be solved with internal workflows, it’s wise to clarify where probabilistic judgments are helpful / required vs. not upfront. If the process is fixed and requires determinism why not just write scripts (code-gen’ed, of course).

	▲	David 2 hours ago \| parent [-]
		This bothered me at first but I think it's about ease of implementation. If you've built a good harness with access to lots of tools, it's very easy to plug in a request like "if the linked PR is approved, please react to the slack message with :checkmark:". For a lot of things I can see how it'd actually be harder to generate a script that uses the APIs correctly than to rely on the LLM to figure it out, and maybe that lets you figure out if it's worth spending an hour automating properly. Of course the specific example in the post seems like it could be one-shotted pretty easily, so it's a strange motivating example.

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Edmond 4 hours ago | parent | prev | next [-]

There is a third option, letting AI write workflow code:

https://youtu.be/zzkSC26fPPE

You get the benefit of AI CodeGen along with the determinism of conventional logic.

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dmarwicke 3 hours ago | parent | prev | next [-]

hit this with support ticket filtering. llm kept missing weird edge cases. wrote some janky regex instead, works fine

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mayop100 3 hours ago | parent | prev | next [-]

This is the basic idea we built Tasklet.ai on. LLMs are great at problem solving but less great at cost and reliability — but they are great at writing code that is!

So we gave the Tasklet agent a filesystem, shell, code runtime, general purpose triggering system, etc so that it could build the automation system it needed.

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retinaros 2 hours ago | parent | prev [-]

its just a form of structured output. you still need an env to run the code. secure it. maintain it. upgrade it. its some work. easier to build a rule based workflow for simple stuff like this.