Right Reasons

Right Reasons is an experimental infrastructure layer for encoding and applying institutional reasoning in AI systems. It explores how human intent can be translated into a structured form that agents can use directly. Instead of relying on narrative interpretation, the aim is to make reasoning explicit and accessible at the point of action.

At the center is the Right Reasons Kernel — a system for structuring goals, constraints, and context into a form that can be referenced and applied across different situations.

As the use of agents expands, the challenge shifts. Running agents is becoming straightforward. Maintaining coherence across their actions is not. Control and monitoring help observe systems. They do not define how decisions should be made when multiple valid options exist.

Right Reasons explores that missing layer. You can explore the public kernel on GitHub.