The framework of policies, processes, and controls that govern how AI agents are deployed, monitored, and managed in an organization.
AI governance answers: who can deploy agents, what can agents do, how are they monitored, and who is accountable when things go wrong.
It covers the full lifecycle: approval to deploy, configuration standards, monitoring requirements, incident response, and decommissioning. Good governance prevents shadow AI (unauthorized agent deployments) and ensures consistent security across all agents.
Without governance, AI agent deployments become the Wild West. Teams deploy agents without security review. Credentials are stored insecurely. Nobody monitors what agents do. Governance brings order.
Clawctl centralizes AI governance: all agents in one dashboard, consistent security policies, centralized audit trails, and RBAC for team access. No shadow deployments — everything is visible and controlled.
Try Clawctl — 60 Second DeployStart simple: centralize agent deployments (use Clawctl), enable audit trails, and require approval for production deploys.
No. Even small teams benefit from consistent policies. It prevents the "who deployed that agent?" problem.
Unauthorized AI agent deployments — team members running agents without IT/security review. Governance prevents this.
AI Compliance
Meeting regulatory and organizational requirements for deploying AI agents in production — including audit trails, data handling, and accountability.
Policy Engine
A rule system that defines what an AI agent can and cannot do, with versioning, rollback, and enforcement.
RBAC for AI Agents
Role-Based Access Control applied to AI agent management — different team members get different permissions for viewing, configuring, and approving agent actions.
Audit Trail
A chronological record of every action an AI agent takes, providing accountability, compliance evidence, and forensic capability.