The hardest part of scaling AI inside an enterprise isn't building the first agent. It's what happens after.
More agents, more teams, more use cases - and the growing questions of who has access to what, what those agents are actually doing, and whether anyone would know if something went wrong.Unframe's Governance and Observability layer is built for that problem.
Nothing is on by default
Every agent is off for every user until an administrator explicitly makes it available. Access is scoped by team, by business unit, or by individual role. The default state is closed; every opening is a deliberate decision.
One place for every access decision
Administrators manage all of it from a single surface: which agents are available to which users, which capabilities are exposed per team, and which versions are in production. Changes take effect immediately, and every change is logged.
Agents are versioned and controlled
When an agent is updated, the change doesn't automatically reach every user. Administrators control when a new version propagates, with rollback available if something needs to be corrected.
Every action, captured automatically
Every agent run is logged in full - the tools it called, the approvals it triggered, the data it touched. The log is write-once and tamper-proof, making it reliable evidence for internal reviews and external audits. Anomaly detection runs continuously across token usage, latency, and behavioral patterns, surfacing problems before they reach users.Governance is usually where AI programs stall. Controls that are too loose don't satisfy compliance. Controls that are too rigid stop teams from moving. Unframe's model is designed so administrators can set real boundaries - and teams can still build and deploy without constant friction.
