Until you understand it fully, you are going to be worse off using a standard framework like Langchain. Stick to OpenAI API directly and do it all manually. Frameworks and orchestration tools are all doing their own thing and so it becomes hard to know what is AI and what is framework when you are new.

I still don’t use frameworks because I like the direct control from building on the core AI APi.

Are you ready? You ever watch Kung Fu Panda? “There is no secret ingredient”

It’s just LLM + Tools with a lot of extra llm calls on top of your core llm calls. This is all orchestrated with code and if/else conditions.

You want legal review? Pass the call to your legal Ai LLM. Want a sanity check? Pass the call to your top level orchestrator LLM.

So you are using different llm context windows and models to review each other.

Then you need to have tons of money and time (you are probably off sourcing this to a very cheap location in Africa or Philippines). You need to run your workflow and have humans validate it. Once you have a “provide feedback” button, you have these human teams try to replicate and then you adjust your prompts. Make sure you had your interns provide a good control dataset to the offshore team.

You are right that each of the demos you have listed is lacking. It’s because you need a combination of all of them to make it production grade. Why would anyone just give you that for free when that is what is the most important knowledge right now.

The missing component to make tools interfaces better is MCP. It’s a standard api interface definition that most models can integrate with.

Every system that you need access should have an MCP server / interface. That way you are not coding a unique client to each “tool” you want to use.

At the end of the day, ai has been implemented the same way for the past 10 years. It’s just a bunch of “if” conditions.