Problem Statement
AI agents are being deployed into production. Most of them have no identity, no enforcement, and no audit trail. The infrastructure to control them safely does not exist in most frameworks.
No verifiable agent identity
When an agent sends a message, books a meeting, or initiates a payment, there is typically no way to prove which agent performed it, who authorized it, or whether it was tampered with. Every action is a black box.
Without a verifiable identity tied to an agent, accountability is impossible. You can log outputs, but you can't prove provenance.
No guaranteed policy enforcement
Most agent frameworks support policy configuration - spend limits, domain restrictions, quiet-hour controls. The problem is enforcement. Policies are checked in application code, which means they can be bypassed by a prompt injection, a logic bug, or a misconfiguration. There is no enforcement boundary that sits outside the agent itself.
Unfiltered data in model inference
When an agent processes documents, emails, or records, that data typically flows straight to the model. Names, addresses, account numbers, health information - none of it is stripped before inference. Compliance teams flag this. Users rarely know it's happening.
No payment privacy
Agent-initiated payments expose amounts, parties, and patterns. For any agent operating with financial authority, this is a real problem - counterparties shouldn't be able to infer your balance or spending behaviour from a payment authorization.
No tamper-evident audit trail
When something goes wrong - an agent makes an unexpected purchase, deletes the wrong file, sends the wrong message - there's usually no signed record of what happened, when, or why. Logs exist, but logs are mutable. You need something tamper-evident.
Non-portable agent reputation
If you've verified that an agent behaves correctly in one context, that trust doesn't carry anywhere. Every new system, every new counterparty has to start from scratch. There's no portable reputation.