Build an agent workforce your organization can actually control.
QUI lets companies create role-based agents with memory, tools, workflow authority, model routing, and human oversight.
Platform users can also be governed with RBAC roles such as Admin, Developer, Operator, Auditor, and Viewer.
The Problem
Many companies are experimenting with agents, but few have an operating model for governing them.
Without that model, agents become inconsistent:
- Different teams create different prompt standards
- Tool access is unclear
- Memory is unmanaged
- Everyone can see or change too much
- Approvals happen manually or not at all
- Costs are hard to predict
- Agent work is hard to inspect
What QUI Enables
QUI gives each agent a defined operating context:
- Role
- Identity
- Knowledge
- Memory
- Tool permissions
- Workflow permissions
- Communication rights
- Model route
- Budget limits
- Review gates
RBAC defines which people can create agents, change prompts, access memory, view conversations, use certain models, operate workflows, audit activity, or change infrastructure settings.
Example Agent Roles
- Research analyst
- Engineering assistant
- Customer response drafter
- Executive memory agent
- Operations coordinator
- Documentation maintainer
- Strategy critic
- Incident response assistant
Example Workflow
- Admin defines approved tools and model routes.
- A team creates agents for specific roles.
- Each agent receives only the knowledge and permissions it needs.
- Workflows define when agents act alone and when humans approve.
- Evidence trails make work reviewable.
- Memory and prompts improve under controlled conditions.
Outcome
The company can move from random AI usage to an intentional agent workforce.
Agents become manageable participants in business workflows rather than untracked automations.