[locknitpicker]

> The secret sauce though is all the datasets, RL training, knowledge of what works from doing all kinds of ablation experiments, and a massive compute moat.

ReAct loops and tool-calling are the critical development feature. They turn a model from something that generates text into something that can independently influence the world around them.

Without agent features, you have just a chatbot.

[galaxyLogic]

The big breakthrough is we can interact with the agents using natural language - because of the LLM.

It is the combination of LLM and agent-harnesses that make it look really smart. Agent-harness is a programmatic device that lets us tap into the vast knowledge in the LLM.

It is probabaly true that many TV-commentators fail to appreciate this fact and therefore think LLMs are super-intelligent. No, it is the combination of LLM and the programmatic agent-haness that is the breakthrough.

An interesting thought is that the LLM could in theory code the agent-harrness, start it running every time we interact with it. Currently the agent-harrness I think is pretty static I think. In theory it could be dynamically created for every task. Would that make it better don't know.

[locknitpicker]

> The big breakthrough is we can interact with the agents using natural language - because of the LLM.

Without ReAct and tool calling, all you have is a chatbot. That's useful, but it's just a chatbot.

ReAct loops and tool calling is what unblocks high value usecases. It enables systems to actually address free-form problem statements, gather data that is not a part of their training set, inspect the current state of services,and trigger actions in external systems. This goes well beyond mere chatbots.

> It is the combination of LLM and agent-harnesses that make it look really smart.

It's really not about "smart". It's about autonomous systems, and being able to consume and analyze new data, and trigger actions in external systems.

[Chu4eeno]

It's not very novel, though, it's a fairly obvious step once you get something that can operate iteratively and largely independent, there were a ton of people trying to get LLMs to loop on their own even before deepseek r1.

And I remember talking about goal directed behavior (which what people are calling "agents" now don't seem to properly have) and autonomous operation decades ago in the intelligent agent course at uni, including react loops.

So no, the huge step with LLMs really was just that attention mechanism from that translation paper everyone forgot until Google brought its marketing to it, everything else is either just optimization/scaling, more money or old ideas suddenly relevant.

[locknitpicker]

> It's not very novel, though (...)

I completely disagree. The rollout of agentic tools, and even support for agent mode in IDEs, is the whole value proposition of AI code assistant services.

Otherwise you'd just have a glorified search engine in a chat window.

> (...) it's a fairly obvious step once you get something that can operate iteratively and largely independent,

There's some confusion in your reply. ReAct loops is exactly what this "operate iteratively and largely independently" represents.