AI is moving from generating answers to operating systems. JoyMux exists to make that execution visible, attributable, and controllable.
JoyMux began with durable execution sessions: preserving work when terminals and clients disconnect. That foundation also supports concurrent work, compact context, and deterministic workspace coordination. Runtime security adds identity, detection, containment, and mediated authority to the same execution layer.
Model safeguards matter. So do the permissions, processes, resources, and recovery paths underneath the model. Our focus is the point where intent becomes execution.
We prefer workload-specific qualification over universal promises. A pilot should establish which controls work, where the boundary ends, and how the system behaves under failure.