[nphard85]

Very interesting. How does this approach work for complex agentic workflows where the LLM is expected to orchestrate across multiple tools (such as when using MCP)? Or is this mainly for simple cases like the ones presented in the blog post?

[viksit]

+1 thanks for mentioning MCP!

re: different tools (apis vs mcps). in my mind, there should be no real difference at what kind of tools is called at this moment since I model this as a softmax over a label set of tools.

that said, an idea I want to investigate is whether tools can live in a learned embedding space, where selection isn’t a softmax over discrete labels but a nearest-neighbor or attention mechanism over continuous vectors.

this is the intuition I'm developing as we speak and in some of my other comments on this thread (see differentiable state machine comment).

[lgas]

The work described appears as if it would handle a complex set of multiple tools just fine, but you do train the controller on a specific tool set, so you would presumably need to train (or at least something like "fine tune") a controller for each toolset you wanted to use.

[viksit]

for sure, there's a way here where I think we ought to be able to learn multiple tool calls and prompts together with real world data. investigating that next.