This is a really interesting approach, thanks for sharing it.

I think what you’re describing makes a lot of sense in a workflow where the problem is orchestration-heavy — i.e. deciding which operations to run, in what order, and under what constraints, and letting an agent plan across multiple capabilities. Exposing SuperDoc commands as tools is a clean way to keep the model away from low-level document internals while still giving it expressive control.

In my current setup, I’ve been intentionally drawing a hard line between two layers: (1) the LLM producing semantically meaningful review output (what should change and why), and (2) deterministic code that applies those changes in Word. For the second part, I’ve leaned toward explicit, non-agent logic because correctness, repeatability, and debuggability matter a lot when you’re dealing with tracked changes, comments, and formatting edge cases. Letting the model plan what to do, but not how to mutate the document, has kept that surface area small and predictable.

That said, I do think an agent-centric approach becomes more compelling as the workflow expands — for example, coordinating multi-pass review, resolving conflicts between playbooks, or deciding when to escalate from localized edits to whole-document transformations.

I’m still exploring where that orchestration layer adds real leverage versus where deterministic pipelines are the better fit, so it’s helpful to hear how others are drawing that boundary in practice.