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Use Case · AI Agents

Approval and Audit Gateway for AI Agents

Put a policy and approval boundary between AI agents and real-world actions.

Build Your First Approval FlowOpen API Docs
The Problem

Once an agent can act, it needs a decision point

AI agents don’t just answer questions. They draft and send messages, update records, call APIs, and trigger downstream workflows. The moment an agent can take an action that touches the real world, you need something in front of every consequential action that says: allow it, deny it, or send it to a human first.

The Gateway Pattern

Propose → evaluate → approve → execute → record

An agent proposes an action. UAPK Gateway evaluates it against your policy. Low-risk actions are allowed and executed through a connector. Risky actions are escalated to a human approver. Once approved, the action runs. Every step — proposed, evaluated, approved, executed — is written to a signed audit record.

Agent proposes actionGateway evaluates policyLow-risk → allowedRisky → escalatedHuman approvesConnector executesSigned audit record
Use Cases

Actions worth putting a boundary in front of

  • Outbound client emails
  • CRM and record updates
  • Document generation
  • Research exports
  • Payment-related actions
  • Public publishing
  • API calls from AI tools
Integration

How it fits your stack

UAPK Gateway exposes a public OpenAPI, a Python SDK, a manifest-based policy model, and a builder UI for assembling governance manifests. Integration guides for Make.com, Zapier, n8n, and Langflow are in the docs.

FAQ

Common questions

Is this an agent framework?
No. It is an action governance boundary — it sits in front of whatever agent or automation you already run.
Does it execute every tool itself?
It evaluates and governs actions before they run, and can execute them through pluggable connectors. It is the control point, not the agent.

Put a boundary in front of your agents

Start with one action: propose it, let the Gateway evaluate it, approve it, and get a signed record.