Comment by NitpickLawyer
2 days ago
> This is why we're seeing the bulk of gains from things like MCP and, now, "agents".
This is objectively not true. The models have improved a ton (with data from "tools" and "agentic loops", but it's still the models that become more capable).
Check out [1] a 100 LoC "LLM in a loop with just terminal access", it is now above last year's heavily harnessed SotA.
> Gemini 3 Pro reaches 74% on SWE-bench verified with mini-swe-agent!
I don't understand. You're highlighting a project that implements an "agent" as a counterargument to my claim that the bulk of improvements are from "agents"?
Sure, the models themselves have improved, but not by the same margins from a couple of years ago. E.g. the jump from GPT-3 to GPT-4 was far greater than the jump from GPT-4 to GPT-5. Currently we're seeing moderate improvements between each release, with "agents" taking up center stage. Only corporations like Google are still able to squeeze value out of hyperscale, while everyone else is more focused on engineering.
They're pointing out that the "agent" is just 100 lines of code with a single tool. That means the model itself has improved, since such a bare bones agent is little more than invoking the model in a loop.
That doesn't make sense, considering that the idea of an "agentic workflow" is essentially to invoke the model in a loop. It could probably be done in much less than 100 lines.
This doesn't refute the fact that this simple idea can be very useful. Especially since the utility doesn't come from invoking the model in a loop, but from integrating it with external tools and APIs, all of which requires much more code.
We've known for a long time that feeding the model with high quality contextual data can improve its performance. This is essentially what "reasoning" is. So it's no surprise that doing that repeatedly from external and accurate sources would do the same thing.
In order to back up GP's claim, they should compare models from a few years ago with modern non-reasoning models in a non-agentic workflow. Which, again, I'm not saying they haven't improved, but that the improvements have been much more marginal than before. It's surprising how many discussions derail because the person chose to argue against a point that wasn't being made.
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I think the point here is that it’s not adding agents on top but the improvements in the models allow the agentic flow.
But that’s not true, and the linked agentic design is not a counterargument to the poster above. The LLM is a small part of the agentic system.
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