Governance
AI governance with approvals, permissions, and audit logs
To use AI in production work, teams need to explain what data AI accessed, who approved the output, and where it was used. AGTO is built for AI operations that people can manage.
Control what AI can access
Useful AI must still respect data boundaries. AGTO designs knowledge access around users, tenants, and workflows.
Use HITL for risky work
Answers, skill changes, and routine outputs can be routed to human review when business risk is involved.
Keep work explainable with logs
Teams can trace who changed a skill, where it was used, and which approval allowed it to move into work.
Process
How to design governance
Policy
Define allowed data
Map data sources, teams, and user permissions.
Approval
Separate risky actions
Classify answers, skill edits, notifications, and reports by risk.
Audit
Use logs for improvement
Review exceptions, wrong answers, and pending approvals to update rules.
FAQ
AI governance FAQ
Does every answer need approval?
No. Approval can be limited to important or risky outputs.
What should audit logs include?
Usage, skill edits, approvals, and outcomes that help explain and improve operations.
Can permissions differ by department?
Yes. Data scope and users can be separated by team or workflow.
Next Step
Design governance for production AI
We can translate data scope, approval rules, and audit requirements into a pilot plan.