Hey, sorry for the confusion. This tutorial is focusing on the low-level internals of how agents are implemented—much like how intelligent large language models still boil down to matrix multiplications at their core.
> This tutorial is focusing on the low-level internals of how agents are implemented
I don't think this tutorial is "near the metal" of LLMs nor do I think it should be considering it is aimed at "Dummies". Low-level would really need to get into the inner workings of the processing, probing agents, and getting into the weeds.
The original purpose is to help people understand how the inner agent framework is internally implemented, like those:
OpenAI Agents: https://github.com/openai/openai-agents-python/blob/48ff99bb... Pydantic Agents: https://github.com/pydantic/pydantic-ai/blob/4c0f384a0626299... Langchain: https://github.com/langchain-ai/langchain/blob/4d1d726e61ed5... LangGraph: https://github.com/langchain-ai/langgraph/blob/24f7d7c4399e2...
Although I personally don't think the graph implementation for agents is necessarily as established or widely standardized, it's helpful to know about why such an implementation was chosen and how it works.
> the inner workings of the processing, probing agents, and getting into the weeds
These feel to me like empty words... "inner workings of the processing"? You can say that about anything.
> You can say that about anything.
> How they interpret one another and respond.
That sounds like it just falls back to "how LLMs work". It's the wrong level of abstraction in this case, because it's one level down from the topic being discussed here.
> because it's one level down
Aren't we after the "low-level"? That's this whole conversation... yes, it is a level down, that's my whole point. Just as my original analogy with assembly being a level down from C. Working at the metal, as they say. In the weeds.
I honestly don't know how to respond because I'm saying "this is too high-level" and you're arguing "you're too low-level". I'm sorry, but when you do stuff at the low-level you in fact have to crouch down and put your face to the ground. The lower the better. You're trying to see something very small, we're not trying to observe mountains here
Does it really matter if you can understand them? waiting for strongly-opinionated engineers to finish their pedantic spiels (...even when they're wrong or there is no obvious standard of correctness) when everyone already understands each other is one of the most miserable part of being in this industry.
I—and I emphatically don't include the above poster in this view as it takes continual & repeated behavior to accrue such judgement—see this as a small tantrum, essentially, for people who never learned to regulate their emotions in professional spaces. I don't understand why this sort of bickering is considered acceptable behavior in the workplace or adjacent spaces. It's rude, arrogant, trivially avoidable with slight change in tone and rhetoric, and it makes you look like an asshole if you're not 100% right and approach it in good humor.
Why do you see this among engineers frequently? Well because it's the job of an expert to be concerned with nuance and details. The low-level in fact. This requires a high precision in communication too. The back and forth you see as bickering also ends up getting those details communicated. The reason being is that much of what's being intended is implicit. So the other approach is to use a lot of words. Unfortunately when you do that you are often ignored.
In the case of LLMs knowing it does boil down to matrix multiplication is insightful and useful because now you know what kind of hardware is best suited to executing a model.
What is actually not insightful or useful is believing LLMs are AGI or conscious.
Then again, I don't think anyone who can follow this article believed that LLMs were conscious to begin with, so I'm not sure what your point is. You're preaching on behalf of a demographic that won't read this article to begin with, and presumably the people who are can see how useless, distracting, and unproductive this reductionism is.
Pursue the hypothesis? Sure. But belief is a different beast entirely. It's not even clear AGI is a meaningful concept yet, and I'd bet my life savings everyone reading this comment in 2025 will die before it's answered. Skepticism is the barometer.
Our approach is so unrelated to any of the other hyped up stuff. We have not written a single line of ML, it has been all math & physics until now.
For example, for software projects, the algorithmic level is where most people focus because that’s typically where the biggest optimizations happen. But in some critical scenarios, you have to peel back those layers—down to how the hardware or compiler works—to make the best choices (like picking the right CPU/GPU).
Likewise, with agents, you can work with high-level abstractions for most applications. But if you need to optimize or compare different approaches (tool use vs. MCP vs. prompt-based, for instance), you have to dig deeper into how they’re actually implemented.
> this reductivism is not exactly insightful.
We wouldn't call least squares, even a bunch of them, ML nor would we call rasterization or ray tracing. Fundamentally all these things are "just GMMs". It also does not make apparent any differentiation from important distinctions like Linear Networks, CNNs, or Transformers. It brushes off a key element, the activation function, which is necessary for neural nets to do non-linear transformations! And what about the residual units? These are one of the most important factors in enabling Deep Learning. They're "just" addition. So we say it's all just matrix addition since we can convert multiplication to addition?
There is such a thing as oversimplification and I worry that we have hyper-optimized (over-optimized) for this. So I agree, saying they just "boil down to matrix multiplications" is fundamentally misleading. It provides no insight and only serves to mislead people.