Give teams powerful agents with clear boundaries.
QUI is built to govern agents across users, teams, tools, channels, hosted contexts, and model routes.
Qate
Qate is the multi-user workspace and governance layer for QUI. It helps teams manage shared workspaces, members, roles, permissions, access boundaries, approvals, and audit visibility.
RBAC
RBAC gives organizations defined roles such as Admin, Developer, Operator, Auditor, and Viewer, each with explicit permissions for the AI platform.
Audit Logs
Audit logs record important governance events so teams can see who logged in, changed configuration, accessed data, deployed an agent, changed permissions, and when.
Why Governance Must Be Built In
Enterprise agents need more than good prompts. They need operating rules.
Companies must be able to answer:
- Who owns this agent?
- Which tenant, team, or user does it serve?
- Which data can it reach?
- Which tools are enabled?
- Which users can create agents, change prompts, or access memory?
- Can it send messages or only receive them?
- Which model routes are allowed?
- Who can view conversations, use sensitive models, or alter infrastructure?
- Is this workflow approved for autonomous execution?
- Where is the audit record for this login, deployment, access, or change?
- What happens when policy changes?
QUI Governance Surfaces
Qate Workspaces
Qate gives teams a shared workspace model for multi-user QUI deployments. Instead of treating agent work as individual accounts and isolated local state, Qate helps organize members, roles, workspace boundaries, shared agents, approval responsibility, and audit visibility.
For enterprise teams, Qate is where workspace-level governance becomes visible: who belongs to the workspace, what role they have, which agents and memories they can reach, which actions require approval, and which changes need an audit record.
Role-Based Access Control
RBAC, or Role-Based Access Control, prevents every user from having the same permissions. Users receive roles such as Admin, Developer, Operator, Auditor, or Viewer, and each role has defined capabilities.
For an AI platform, RBAC controls who can create agents, change prompts, access memory, use certain models, view conversations, approve actions, manage tools, or alter infrastructure.
Audit Logs
Audit logs give enterprises a tamper-resistant record of important actions in the system: who logged in, who changed a configuration, who accessed data, who deployed an agent, who changed permissions, and when.
Security, operations, and compliance teams use these records for investigations, accountability, security monitoring, and audit review.
Character Governance
Anima centralizes agent configuration, including identity, tools, memory, prompts, model preferences, and capability settings.
Workflow Governance
ThinkThing workflows make decision points, review gates, branches, and execution rules visible.
Integration Governance
Tools and integrations are admin-configured and exposed through controlled capability paths.
Channel Governance
Strings and channel bindings support controlled participation across direct chat, M2M, Telegram, Slack, Discord, WhatsApp, and Email.
Tenant Boundaries
Hosted contexts can enforce tenant separation so one tenant cannot invoke, observe, acknowledge, or reply through another tenant's state.
Route Governance
QUI Core governs whether a request uses managed cloud, local inference, or private owner-controlled compute.
Business Benefit
Governance lets companies scale agent usage without turning every deployment into an unmanaged experiment.
It makes AI adoption repeatable across departments, workstations, and hosted contexts.
Applied Use Cases
- Multi-team agent rollout
- Multi-user Qate workspaces
- Hosted customer-facing agents
- Internal department agents with different permissions
- Admin, Developer, Operator, Auditor, and Viewer access patterns
- Audit review for logins, data access, deployments, and permission changes
- Channel-specific response ownership
- Sensitive workload routing by policy
- Admin-governed integrations