Enterprise AI Agents are live.
The governance isn’t.

An agent control plane is the governance layer that shows which AI agents are running, what they can access, how they perform, and whether they stay inside approved boundaries. It centralizes policy enforcement, audit trails, and approval workflows across frameworks.

AI agent governance is the set of policies, controls, and review workflows that determine how agents are deployed, what identities and permissions they receive, how their behavior is monitored, and what happens when risk thresholds are crossed.

Prefactor is built for enterprises managing AI governance at scale — organisations dealing with agentic sprawl across departments, frameworks, and compliance boundaries. That said, governance challenges aren’t unique to large organisations. Prefactor works for teams of all sizes, from startups shipping their first production agents to government agencies.

Runtime governance means applying policies directly at the agent execution layer - blocking risky actions, detecting PII in outputs, and routing high-risk operations for human approval. Unlike static rules, runtime enforcement adapts as agents operate.

AI security focuses on threats such as prompt injection, data leakage, model misuse, and compromised tooling. Agent governance focuses on whether agents are approved, operating within scope, producing acceptable outcomes, and following the right human or policy controls in production.

AI agents need their own identity and scoped access so each action can be tied to a specific agent, task, and user context. That enables least privilege, traceability, token revocation, and safer delegation than static shared credentials.

Prefactor assesses outcome quality, cost efficiency, and scope adherence across AI agents, then can block actions, route them for approval, or record them for audit when policy thresholds are crossed.