Disclosure: I m building ChaseOS Studio, a local-first workspace where agents operate under explicit approval and evidence boundaries.
I do not think local model and local-first product are the same thing. An app can call a cloud model and still keep the operator in control. It can also run a model locally while trapping durable state in an opaque store that is difficult to inspect, export or recover.
The boundary I m testing keeps five control surfaces on the operator s machine:
1. Durable project truth in inspectable, exportable representations
Disclosure: I m building ChaseOS Studio, and this is the memory boundary I m testing.
Most agent-memory stacks make capture and trust almost the same event: ingest a page or attachment, embed it, and let future agents retrieve it.
I m taking a different approach. New material enters Intake first. Its source remains visible, a person decides whether it belongs in the workspace graph, and rejection happens before it becomes durable agent context.
That separates three decisions that are often collapsed:
Most agent tools force a choice between supervising every step and granting broad autonomy. I m testing a third approach: let work continue locally, but require approval before publishing, external sends, protected writes or other consequential actions. The run history then records what the agent read, produced and was refused.
I m curious where other builders draw that boundary. Which actions would you allow unattended, and which should always pause for a human decision?
If you want to explore the current Windows workspace and try the free Community edition (the complete local product, not a trial): https://chaseos.ai/?utm_source=p...
