Comment by ilaksh

Obviously there are going to be narrow tasks where fine tuning makes sense. But using leading models for agents is a completely different mindset and approach.

Because I have been working on replacing multiple humans handling complex business processes mostly end-to-end (with human in the loop somehow in there).

I find that I need the very best models to be able to handle a lot of instructions and make the best decisions about tool selection. And overall I just need the most intelligence possible to make fewer weird errors or misinterpretations of the instructions or situations/data.

I can see how fine tuning would help for some issues like some report formatting. But that output comes at the end of the whole process. And I can address formatting issues almost instantly by either just using a smarter model that follows instructions better, or adding a reminder instruction, or creating a simpler subtask. Sometimes the subtask can run on a cheaper model.

So it's kind of like the difference between building a traditional manufacturing line with very specific robot arms, tooling and and conveyor belts, versus plugging in just a few different humanoid robots with assembly manuals and access to more general purposes tools on their belt. You used to always have to build the full traditional line. In many cases that doesn't necessarily make sense anymore.