LLM that can call multiple tool APIs with one request

I have a saying: "any sufficiently advanced agent is indistinguishable from a DSL"

If I'm really leaning into multi-tool use for anything resembling a mutation, then I'd like to see an execution plan first. In my experience, asking an AI to code up a script that calls some functions with the same signature as tools and then executing that script actually ends up being more accurate than asking it to internalize its algorithm. Plus, I can audit it before I run it. This is effectively the same as asking it to "think step by step."

I like the idea of Command R+ but multitool feels like barking up the wrong tree. Maybe my use cases are too myopic.

You mean manually pre-baking a DAG from the user query, then “spawning” other LLMs to resolve each node and pass their input up the graph? This is the approach we take too. It seems to be a sufficiently performant approach that is - intuitively - generically useful regardless of ontology / domain, but would love to hear others’ experiences.

It would be nice to know if this is sort of how OpenAI’s native “file_search” retriever works - that’s certainly the suggestion in some of the documentation but it hasn’t, to my knowledge, been confirmed.

No. The DAG should be "manually pre-baked" ( defined at compile/design time).

In runtime you only parse the "user question" (user prompt) into a starting and end node, which is equivalent to a function call.

So the question

"What league does Messi play in?"

Is parsed by the llm as

League("Messi")

So if your dag only contains the functions team(player) and league(team), you can still solve the question.

But the llm isn't tasked with resolving the dag, that's code, let the llm chill and do what it's good at, don't make it code a for loop for you