Who approved this agent's access?
Most teams can't point to a specific authorization. No name, timestamp, or policy behind the access.
Design Partner Program
Design Partner Program
Shape the enforcement layer for AI agents.
Before shipping AI agents, every team needs answers to three questions:
Security scanners find vulnerabilities. But finding a problem and preventing it are different things.
What happens when it goes wrong?
No kill switch. No escalation path. Agent keeps running.
Can you prove it to your board?
Most teams don't have a live audit trail.
One enforcement layer for every agent action.
Python + TypeScript SDKs for gating consequential agent actions.
from permission_protocol import require_approval@require_approval(resource="production", action="deploy")def deploy_service(): deploy("billing-api")Step 1
Wrap
Add a guard around consequential actions in the agent workflow.
Step 2
Gate
Enforce approval and policy checks before the action executes.
Step 3
Receipt
Issue a verifiable record of who approved what and when.
What ships with it
Built to enforce actions, not just observe them.
- Human approval gates
- Policy engine
- Kill switch
- Audit trail
- Any framework
What we exchange.
Start with one consequential action and direct support from the founding team.
What you get
- Free access for 30 days
- Direct access to the founding team
- A chance to shape the roadmap
- Priority support during integration
- A compliance-ready audit trail
What we need
- A production AI agent with tool access
- 30 minutes of feedback each week
- Optional permission to use your team as a reference
Why now
The assessment layer is getting crowded. The enforcement layer is wide open.
OWASP Agentic Top 10 published
EU AI Act enforcement 2026
Apply to the program
If your team is deploying AI agents with real tool access, we want to work with you.
[email protected]