Absolutely! My thoughts exactly, you are 100% hitting the nail in the head! 

I have used the "raw" chat-based LLMs, they are impressive and smarter than me for sure, within a limited context. The key to good results is to explain the context, which is really hard to be honest. 

MCP, tool calling, RAG - they are ... primitive I mean impressive, yes, but so primitive it's useless.

My hypothesis is the same as yours: LLMs are waaaaaay smarter than we think, already. They are engines on a stand like you said and nobody has figured out the mechanics how to connect it to wheels, bas pedal and steering etc. They are trying to get the engine itself to twist the wheels instead of having mechanics and gears do the work.

For example, a simple example: context memory. Take humans, my context window (working memory) is really small. If I'm multitasking, f.ex. household chores, I sometimes switch tasks to do something else and completely forget about the thing I left halfway done until my wife (an outsider) reminds me. 

What you need is an operating system for LLMs. Instead of a limited chat system, you'd have the incoming message represent the state of the OS. For example, you could have widget-based text OS: 2025-06-03T13:21:58   Investigate latest AI papers   ...   Imagine many goals set by you and the LLM   Read and train on the AlphaEvolve paper   ... imagine many tasks created by you and the LLM based on the goals or just individual, one-off tasks

The idea here is to create a real-time OS for LLMs. It would be text based, XML-like as shown in the dummy example. Every token the model outputs is actually fed into the OS, which then updates the state. For example, basic tokens would be fed as thought widgets. So the LLM does not have its own "chat box" where it writes and posts an answer. Instead, it appears "on the screen". You can then add special tokens like so LLM can select, expand, collapse etc interact with widgets with single tokens. 

It requires some training for sure and you should understand that the example I gave is very simplistic. You could even have type of widget that contains the image base64 bytes. As long as the whole thing fits into 128k or 32k tokens, the LLM can basically operate in real-time. You can add type widget where you can post messages, LLM can respond and take action based on your input. For example, you can ask it to create a new task.

The idea is that the text-based OS can be created in any programming language like Python by almost anyone, you could create any number of widgets. It would create "guardrails" and a "systematic way of doing things" like LLM playing a single player game with strong narrative. 

Also, a widget for raw conversation logs/files, some vector database thst include the summarized/compressed info with references to raw data, some kind of general "post it notes" kind of widget etc. Python console for quick calculations etc.

Thinking long-term, neural updates are the key here to make it really understand and evolve on its own. LoRA updates whenever something new is to be learned. The system must direct the LLM to update its weights on a schedule (like every night) or even during operation (if single pass update takes only a handful of seconds).

I think if this idea would be implemented (and I will implement it later this year, probably as open source python library), you'd start to move towards LLMs being able to really operate in the world. And again, I suspect they are already wya smarter than we think. I can't solve many math problems in my head but given a piece of paper and a calculator it's a different story.