enterprise ai agents with governance
technology
Agents with real work to do, and authority kept in your hands. Four layers of control for enterprise AI agents.
How do you let AI agents do real work without losing control of company data? Most teams get stuck choosing between two bad options: a cage where AI is safe but useless, or the wild where it's useful but risky. There's a third option: AI agents that use your company memory, inside boundaries you define and can review.
Four layers you control
Constella is built in four layers:
- Models: your choice, including a self-hosted option so data and weights stay inside your walls.
- Company memory: scoped by team, project, and role, and connected into a knowledge graph.
- Guardrails: roles, scopes, and approvals for what matters. Rules decide what every agent can see and do.
- People and agents: capture, chat, projects, and agents doing the actual work.
Controls enterprises ask for
- Data boundaries
- Model policy
- Memory and retention
- Access by role
Revoke a member and their access ends immediately. Approval rules and a full activity record are coming soon.
Agents that ask before they act
Coming soon: a procurement agent drafts renewal terms from the contract and the rate card. Sending anything outside the company waits for a manager to approve, by policy.
Learning stays governed too. When someone corrects an answer or rejects a draft, the correction is saved with who made it and why. An owner or admin reviews it before it changes how agents behave, and you can always roll it back.
No vendor lock-in
The model is rentable, but your memory is not. Swap models as better ones arrive while you keep your knowledge, your rules, and your agents. Constella follows industry-standard security practices, with a self-hosted option when data must stay on your own infrastructure. See trust and controls or Constella for enterprise.