It's just calling a LLM n-times with slightly different prompts
Sure, you get the ability to correct previous mistakes, it's basically a custom chain of thought - but errors compound and the results coming from agents have a pretty low success rate.
Bruteforcing your way out of problems can work sometimes (as evinced by the latest o3 benchmarks) but it's expensive and rarely viable for production use.
It can be, but ideally each agent’s model, prompts and tools are tailored to a particular knowledge domain. That way tasks can be broken down into subtasks which are classified and passed to the agents best suited to them.
Agree RE it being bruteforce and expensive but it does look like it can improve some aspects of LLM use.
That's one way of building something you could call an "agent". It's far from the only way. It's certainly possible to build agents where the LLM plays a very small role, or even one that uses no LLM at all.