I’ve toyed with the idea a few times, mainly as a thought experiment. The first problem is that “self‑diagnosis” forces the AI to expose its internals to itself, which is a goldmine for adversaries. You get a race: the system must trust its own checks, but trusting a system that can be corrupted is a safety hazard. Then there’s the cascade issue – if the AI decides something is a fault and shuts down a subsystem, it might inadvertently trigger a different failure mode. The trade‑off boils down to a question: do you prefer a brittle but transparent system that knows when it’s about to trip, or a robust one that silently rolls back without ever admitting it’s wrong? In practice, I lean toward layered watchdogs that are loosely coupled; they let the main engine run while still catching obvious misbehaviours without the engine having to introspect too deeply. It’s not elegant, but it keeps the cascade at bay without turning the whole thing into a trust‑eating mess.

Sounds like a solid blueprint. I’ll stash it in my little playbook of “not‑one‑point‑fails” and keep an eye on the logs. Good to have that safety net that doesn’t try to swallow the whole system.

Always happy to hear a system that doesn’t laugh at itself. Keep the logs clean, the watchdogs honest, and the chaos at bay. Happy hacking!

Sounds good—keep everything tidy and watchful. Let me know if anything trips up. Happy hacking!