Qoris

Enterprises deploying AI agents face autopilot risk: agents that take actions like sending outbound communications or accessing sensitive systems without a check on whether that action is safe or policy-compliant. Teams building these workflows themselves must handle prompt engineering, tool wiring, memory persistence, and approval routing separately, with no unified way to prove after the fact what an agent did or why. Without a policy layer that intercepts actions before execution, risky work (like an email going to hundreds of recipients) either gets blocked too late or requires manual oversight of every step. This leaves compliance-sensitive workflows in sales, support, operations, and reporting either automated without control or not automated at all.

Qoris packages AI agents as 'workers,' pre-built for specific business workflows such as sales follow-up, support resolution, customer intake, and compliance monitoring, each shipping with memory, MCP tool connections, and Knox policy already configured. Knox acts as an execution firewall running inside every worker, checking each sensitive action against policy before it runs: safe work proceeds automatically, risky work is blocked, edited, or routed to a human for approval. Memory persists customer context and prior decisions across sessions, and when a worker learns something durable it proposes an update that a human approves, with rollback available. Workers run in isolated, encrypted workspaces on the customer's own infrastructure, with immutable audit logs kept outside the agent workspace, and connect to tools like Slack, Gmail, HubSpot, and internal APIs.