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)
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)