"AI should elevate your thinking， not replace it."

I don't disagree， but the issue is that current LLMs are not really trained to support that out of the box.

I've solved this by building my own agent harness （retrieval， verification， memory， multi-agent architecture， skills， etc.）.

That's how important agent harnesses are today. Even with simple skills （.md files）， you can already get far， so even non-technical folks can improve the "human-centered augmenting" capabilities of LLMs/agents.

Continual learning promises to solve this， but we are so early on this. People need to understand that in-context learning works great for this. Today's LLMs are steerable if YOU spend time building and optimizing your workflows. Self-improving agents don't work as well because the incentives are not there. A good mindset is that every output you get from an LLM should be reused in some way， let it work for you， and make you and the agent better in the next session.