Everything you need to
govern AI agent actions

Flexible policy rules

Define exactly what your agents are allowed to do. Rules match on tool name, action type, and payload values — so a $25 refund auto-approves while a $180 one waits for a human.

• Match by tool name, action type, or payload field
• Set thresholds to auto-allow, hold for approval, or deny
• Chain conditions with AND/OR logic

{
  "name": "Refund Threshold",
  "tool": "payment_api",
  "action_type": "process_refund",
  "condition": {
    "field": "payload.amount",
    "operator": "gt",
    "value: 50
  },
  "decision": "pending_approval",
  "enabled: true
}

Human in the loop, exactly when it matters

Sensitive actions pause for human review instead of executing automatically. Reviewers see full context — tool name, arguments, risk score, matched policy — and act in one click.

• Live approval queue with risk scores and policy details
• One-click approve or deny with optional comment
• Keyboard shortcut (A) for fast triage at scale

Full accountability, every action

Every decision Contentio makes — allowed, denied, or escalated — is recorded with full context. When auditors ask what your agents did and why, you have the answer.

• Timestamped events with agent ID, action, and decision
• Filter by event type, request ID, or decision outcome
• Compliance-ready export for any audit workflow

Works with any agent, any tool

Contentio implements the Model Context Protocol, acting as a transparent proxy between your agents and the tools they call. Drop it into any agent stack with a single config change.

• Transparent proxy — no changes to your tool implementations
• Compatible with LangChain, CrewAI, Autogen, and custom agents
• Open standard — no lock-in, no proprietary wire format