Control what your
AI agents can do.
Contentio is a policy-and-approval layer that sits between your AI agents and the tools they use. Set rules, require human approval for risky actions, and maintain a complete audit trail.
How it works
From request to decision in milliseconds
Agent requests an action
Your AI agent calls a tool — a refund, a delete, a write to production. Contentio intercepts every request before execution.
Policy engine evaluates risk
Rules match against tool name, action type, and argument values. A risk score is calculated. The matching policy decides the outcome.
Auto-allow or human review
Low-risk actions proceed automatically. High-risk actions are queued for human approval with full context — arguments, cost, matched policy.
Why Contentio
Everything you need for agent governance
Policy-based control
Define rules for which actions need approval and which can auto-execute. Match on tool name, action type, or argument values like refund amount.
Human-in-the-loop
Require approval for high-risk actions — large refunds, record deletions, production changes. Approvers see full context before deciding.
Complete audit trail
Every request, decision, and approval is logged with timestamps, arguments, matched policy, and risk score. Immutable and queryable.
Works with MCP
Built on the Model Context Protocol standard for connecting LLMs to external tools. Drop Contentio in front of any MCP-compatible tool server.
See it in action
The live demo runs a fully-functional policy engine with a real approval queue. No signup required.
Try the Demo