Minimal design explorations on execution boundaries and traceable AI actions.
Define execution boundaries first — let autonomy grow only where judgment remains explicit.
Design notes on execution boundaries and responsibility structures for AI systems interacting with the physical world.
As AI begins to participate in real-world decisions, the core challenge is no longer model capability — but how execution is allowed, constrained, and interpreted.
This repository connects a set of related design explorations:
- Intent–State–Effect (ISE) Model
- The 9-Question Protocol for Responsible AI Actions
- Button vs Switch: Action Semantics at Runtime
- Making the Physical World Callable for AI
The goal is not to define a standard, but to explore a minimal structure that makes execution decisions traceable and responsibility boundaries explicit.
These notes focus on separating intent, state, and effect, and on structuring execution conditions before autonomy expands beyond control.
[Making the Physical World Callable for AI]
→ why execution boundaries matter
[ISE (Intent–State–Effect) Model]
→ separating Intent, State, and Effect
[The 9-Question Protocol]
→ defining judgment completeness before execution
[Stop Turning Buttons into Switches]
→ preserving clear action semantics at runtime
↓
Execution Boundaries
(traceable decisions, explicit responsibility)
The goal is to make AI actions more interpretable by declaring limits first, and expanding autonomy only where judgment remains explicit.
These notes are not intended as a standard or a complete framework. They are a set of connected design explorations around execution boundaries and responsibility structures.
This repository acts as an anchor connecting the broader discussion across multiple design notes.