If you're trying to get reliability and determinism out of the LLM, you've already lost

I agree with the sentiment, but I think the conclusion should be altered. When you hit the limit of prompting, you need to move from using LLMs at run time to accomplish a task to using LLMs to write software to accomplish the task. The role of LLMs at run time will generally shrink to helping users choose compliant inputs to a software system that embodies hard business rules.

I mean we have Langgraph, BAML etc

Generally agree with this stance case in point: the breakthrough in ai coding was not that AI intelligence increased as much as that a lot of the core process execution moved out of the LLM prompt and into the harness.

agreed - this is what we’ve been trying to build at scale.

https://github.com/salesforce/agentscript

Are you the guy who used to write MapleStory hacks?

It's the right direction, but control flow introduces limitations within a system that is quite adaptable to dynamic situations. The more control flow you try to do, the more buggy edge cases that pop up if done poorly.

Still have yet to see a universal treatment that tackles this well.

This is why I frequently refer to "next generation AIs" that aren't just LLMs. LLMs are pretty cool and I expect that even if we see no further foundational advancement in AIs that we're going to continue to see them exploited in more interesting ways and optimized better. Even if the models froze as they are today, there's a lot more value to be squeezed out of them as we figure out how to do that.

However, there are some things that I think need a foundational next-generation improvement of some sort. The way that LLMs sort of smudge away "NEVER DO X" and can even after a lot of work end up seeing that as a bit of a "PLEASE DO X" seems fundamental to how they work. It can be easy to lose track of as we are still in the initial flush of figuring out what they can do (despite all we've already found), but LLMs are not everything we're looking for out of AI.

There should be some sort of architecture that can take a "NEVER DO X" and treat it as a human would. There should be some sort of architecture that instead of having a "context window" has memory hierarchies something like we do, where if two people have sufficiently extended conversations with what was initially the same AI, the resulting two AIs are different not just in their context windows but have actually become two individuals.

I of course have no more idea what this looks like than anyone else. But I don't see any reason to think LLMs are the last word in AI.

I agree and I think a really wonderful way to encode agentic control flow would be with Polynomial Functors.

https://arxiv.org/abs/2312.00990

This was one of the key insights in Stripe's explanations about Minions[0], their autonomous agent system; in-between non-deterministic LLM work they had deterministic nodes that handled quality assurance and so on in order to not leave those types of things to the LLMs.

0 - https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-...

Slowly and surely we are replacing AI with programming languages.

I wrote something recently on how agent development differs from traditional software development

https://x.com/i/status/2051706304859881495

Agents are probabilistic systems. A common mechanism to get a reliable answer from systems that can have variable output is to run them several times (ideally in separate, isolated instances) and then have something vote on the best result or use the most common result. This happens in things like rockets and aviation where you have multiple systems giving an answer and an orchestrator picking the result.

I've tried doing something similar with AI by running a prompt several times and then have an agent pick the best response. It works fairly well but it burns a lot of tokens.

You can get a lot done with agentic programming without going "all in" on a gastown-like system, but I think there is a minimum viable setup:

1. an adversarial agent harness that uses one agent to create a plan and implement it, and another to review the plan and code-review each step.

2. an agentic validation suite -- a more flexible take on e2e testing.

3. some custom skills that explain how to use both of those flows.

With this in place you can formulate ideas in a chat session, produce planning artifacts, then use the adversarial system to implement the plans and the validation layer to get everything working e2e for human review.

There are a lot of tools you can use for these things but I chose to just build the tooling in the repo as I go.

I wonder if a part of the problem isn't just the misapplication of LLMs in the first place. As has been mentioned elsewhere, perhaps the agent's prompt should be to write code to accomplish as much of the task in as repeatable/verifiable/deterministic a way as possible. This would hopefully include validation of the agent's output as well. The overall goal would be to keep the LLM out of doing processing that could be more efficiently (and often correctly) handled programmatically.

Sometimes it feels like Agents are just reinventing microservices. Except they are are doing it in the most inefficient way possible. It is certainly a good way for the LLM companies to sell more tokens

this is just advocating for a harness, which has been the focus (along with evals) for at least the last three months by pretty much anyone working with agents professionally or seriously

This was basically my realization as well. We are trying to get LLMs to write software the way humans do it, but they have a different set of strength and weaknesses. Structuring tooling around what LLMs actually do well seems like an obvious thing to do. I wrote about this in some detail here:

https://yogthos.net/posts/2026-02-25-ai-at-scale.html

This is straight outta 2023:

Agents aren't reliable; use workflows instead.

This is exactly the problem I've been working on and I see others are too. When you implement quality control gates, everything works better. It solves so many of the basic problems llms create - saying code is finished when it isn't. Skipping tests, introducing code regressions, basic code validation etc

I am finding that the better the quality gates are the lower quality llm you can use for the same result (at a cost of time).