Identity before autonomy
Every tool call has an identity, a permission boundary, and an accountable owner.
Trust / Controlled autonomy
Earn every permission.
AI becomes operational when people can see what it did, understand why, constrain what it may do next, and recover when it is wrong.
Control principles
Safety lives inside the workflow.
Governance is not a policy deck added at launch. It is implemented in identities, permissions, interfaces, evidence, and operating procedures.
Shadow before write
Historical replay and parallel runs establish behavior before production actions are considered.
Evidence before confidence
Outputs expose sources, limits, and escalation paths; important decisions are evaluated on real task examples.
Humans at the control point
Approval, rejection, correction, pause, and takeover are designed into consequential workflows.
Rollback is a feature
Versions, fallbacks, kill switches, and the original human process remain available in proportion to risk.
Data stays in context
Our intended default keeps customer data in its owning environment or required region; reusable methods may move under agreement, raw data does not by default.
The operating record
Consequential work should leave evidence.
What is recorded depends on risk and customer policy, but the design goal is a reviewable chain from task to action and recovery.
- 01Task and model version
- 02Authorized context and sources
- 03Policy and permission decision
- 04Human approval, rejection, or correction
- 05Action result, error, and recovery path
Data boundaries
Reusable methods move. Raw customer data does not by default.
Deployment architecture is chosen with the customer. Data stays in its owning environment or required region unless a documented, authorized path says otherwise. Cross-border capability is a design target—not a claim about current operations.