Really interesting idea! Part of the ethos here is that models are already really good at writing Python, and we want to bet on that rather than mediate around it. Python has the nice property of failing loudly (e.g., unknown keywords, type errors, missing attributes) so models can autocorrect quickly. And marimo's reactivity adds another layer of guardrails on top when it comes to managing context/state.
Anecdotally working on pair, I've found it really hard to anticipate what a model might find useful to accomplish a task, and being too prescriptive can break them out of loops where they'd otherwise self-correct. We ran into this with our original MCP approach, which framed access to marimo state as discrete tools (list_cells, read_cell, etc.). But there was a long tail of more tools we kept needing, and behind the scenes they were all just Python functions exposing marimo's state. That was the insight: just let the model write Python directly.
So generally my hesitation with a proxy layer is that it risks boxing the agent in. A mediated interface that helps today might become a constraint tomorrow as models get more capable.
Yeah, I'm talking more about a wrapper over the python data model (pyobject) rather than an MCP-style API for kernel interaction. I'm not proposing you abstract interactions under a rigid proxy, but that you can use proxy objects to virtualise access to the runtime. You could still let the model believe it is calling normal python code, but in actuality, it goes via your control plane. Seeing the demo I'd imagine you already have parts of this nailed down tho.
Ah, I think I misread your earlier comment. That's a more interesting version of the idea than what I responded to. We don't do this today, but marimo's reactivity already gives us some control plane benefits without virtualizing object access. That said, I can imagine there are many more things a proxy layer could do. Need to think on it, thanks for the clarification :)