The Control Plane for Autonomous AI
Enforce policy before execution, require human approvals where risk demands it, and keep a full audit trail — from first action to final result.
Runtime policy enforcement for AI agents. Evaluate every agent action against your organization's policies before execution, with observe and enforce modes.
Runtime governance for AI-agent fleets that continuously monitors agent health, confidence, and behavior through check-ins, and returns verdicts to enable self-correction before failures occur.
AgentPay is the authorization layer between an AI agent and real spending. You define the rules — spending caps, allowed merchants, time windows — and every purchase attempt the agent makes is checked against them in real time. Approved transactions go through. Anything outside the mandate is blocked and logged.
No more babysitting every agent action. No more runaway charges.
Pre-execution governance for AI agents. 45 MCP tools for hold queues, audit trails, risk scoring, and policy enforcement. Validates agent actions before they execute.
A local-first security system for autonomous AI agents that provides tools for security verification, goal anchoring, and action logging. It protects against prompt injection and goal drift by enforcing user-defined rules and offering performance insights through session grading.