Thanks!
So this means that every node in the graph has to be a fully fledged agent runtime with the smarts to handle an arbitrary filesystem.
I guess there is some sort of tool call that the agent has to tell the runtime 'I am done, and I succeeded go to the success node in the parent state machine'
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I think I have been trying to approach a slightly different problem where you have a mostly deterministic control flow of steps/ nodes in the graph, but individual nodes can be pretty stupid/ small models, so having a more constrained universe should be more reliable. (My intuition)
My example is a citation checker for an essay -
1. generate a list of claims in a paragraph, For each claim: Read the attached citation, validate that it backs up that claim.
The output is a nested json structure with a list of claims with a explanation+binary classification of whether each one is supported.
This is not really agentic, and the nodes would be claim identification > validate claims (in parallel) > deterministically merge the validated claims into a unified module.
It should be very token efficient, and since it is so constrained I think I can get away with tiny models, and small context.
At least that is my hope... But I think I need typed IO/state so that the llm that writes the graph can deterministically say that the individual nodes complete the problem wholly.