How I built cryptographic audit trails for AI agents (and why it matters)

Every company is deploying AI agents. Nobody knows what those agents are actually doing. An autonomous agent can read your emails, call your Stripe API, export your database, and send messages — all without a human in the loop. When something goes wrong, there's no proof of what happened, no way to prove authorization, and no compliance trail. I built MandateZ to solve this. Here's how it works technically. The core problem Traditional software has clear audit trails. An AI agent doesn't. When a LangChain agent calls send_email(), nothing records: who authorized it, which policy allowed it, what the payload was, or whether a human approved it. That's fine for demos. It's a blocker for any enterprise deployment. The architecture Everything flows from one data structure — the AgentEvent: interface AgentEvent { event_id : string ; // uuid v4 agent_id : string ; // ag_ prefix + nanoid owner_id : string ; timestamp : string ; // ISO 8601 action_type : ' read ' | ' write ' | ' export ' | ' d