This is cool! Seems like this is what AutoGen Studio wanted to be. And what a lot of "agentic" libs fell short of - a way to chain together stuff by using natural language.

Quick questions (I only looked at the demo video and briefly skimmed the docs, sorry if the qs are explained somewhere):

- it looks to me that a lot of the heavy weight "logic" is handled via prompts (when a new agent is created your copilot edits the "prompts"). Have you tested this w/ various models (and especially any open weights ones) to make sure the flows still work? This reminds me of the very early agent libraries that worked w/ oAI GPTs but not much else.

- if the above assumption is correct, are there plans of using newer libs where a lot of the logic / lifting is done by code instead of simply chaining prompts and hope the model can handle it? (A2A, pydantic, griptape, etc)