[entrustai]

Interesting layer to enforce policy at. You're governing what the agent can do — filesystem, shell, execution. There's a complementary problem one layer up: governing what the agent can say before output reaches a user or downstream system.

The failure modes are different. An agent that deletes the wrong file causes immediate visible damage. An agent that outputs a guaranteed return, a clinical claim it can't support, or a sycophantic opener in a regulated context causes liability that surfaces weeks later in a compliance review.

The audit trail approach you've taken with HMAC on approvals is the right instinct for the action layer. The same logic applies to the output layer — you need to prove not just what was blocked, but that the check happened at all, against a specific versioned policy, at a specific time.

Good work on the blast radius simulation — that's the kind of deterministic pre-flight check that makes governance defensible.

[JimmyRacheta]

Thank you. It was a very interesting development and testing, it's amazing to see the models learn in realtime that there's a protection layer and sometimes they say they expect a command to fail because of it even before it runs. Amazing but scary at the same time. While the tool still has a lot of room for improvement, I'm looking forward to add additional features as quickly as I can.

[entrustai]

The models anticipating the protection layer before execution is a genuinely interesting signal — it suggests the governance constraint is becoming part of the agent's operational context rather than an external surprise. Whether that makes the system more robust or creates new circumvention vectors worth red-teaming is an open question worth exploring as you add features. Good foundation. Looking forward to seeing where it goes.