o1 (and likely sonnet 3.5) made chain of through and other complex prompt engineering irrelevant.

Realtime API (and others that will soon follow) will made the best VTT > LLM > TTV irrelevant.

VLMs will likely make LLMs irrelevant. Who knows what Google has planned for Gemini 2.

The point is building these complex agents has been proven a waste of time over and over again until, at least until we see a plateau in models. It's much easier to swap in a single API call and modify one or two prompts than to rework a convoluted agentic approach. Especially when it's very clear that the same prompts can't be reused reliably between different models.

I suppose my comment is reserved more for the documentation than the actual models in the wild?

I do worry that LLM service providers won't do any better than rest API providers in versioning their backend. Even if we specify the model in the call to the API, it feels like it will silently be upgraded behind the scenes. There are so many parameters that could be adjusted to "improve" the experience for users even if the weights don't change.

I prefer to use open weight models when possible. But so many agentic frameworks, like this one (to be fair, I would not expect OpenAI to offer a framework that work local first), treat the local LLM experience as second class, at best.