A new GitHub repository introduces design explorations for execution boundaries in AI systems interacting with the physical world. The project focuses on creating traceable AI actions through structured frameworks like the ISE (Intent–State–Effect) Model, which separates decision-making components to enhance transparency. By defining execution limits first, the framework aims to expand autonomy only where human judgment remains explicit, addressing ethical and operational accountability in AI-driven physical systems.

The repository outlines three core components: the 9-Question Protocol to evaluate action completeness before execution, the Button vs Switch concept to maintain clear action semantics at runtime, and methods to make the physical world programmatically accessible to AI. These elements collectively prioritize explicit responsibility structures over emergent autonomy, ensuring decisions can be audited and consequences traced back to specific design choices.

Technical significance lies in its approach to preemptively constraining AI behavior rather than retrofitting controls. For example, the execution boundaries framework separates intent (goal), state (current conditions), and effect (outcomes) to prevent unintended interactions. This structured separation enables developers to map responsibility hierarchies before deploying AI in environments like robotics or autonomous systems.

While not a formal standard, the repository serves as a collaborative anchor for broader discussions on AI safety. It references related projects, including Anna Soft's Nemo-Anna initiative, and emphasizes practical applications in fields requiring high-stakes decision-making. The work positions execution boundaries as a foundational concept for building interpretable, accountable AI systems capable of real-world engagement.