Hey, I’d first pull the flight‑data logs and run a stress‑test on the learning loop to see if it’s just chasing a pattern or actually adapting. Then I’d map out every failure mode: loss of signal, sensor glitch, unexpected obstacle, and make sure the human override is a hard stop, not a soft suggestion. If the AI keeps a clear boundary state, it’s fine; if it’s all‑in, you’ll get blind spots. So the trade‑off is: full autonomy gives speed but risks opaque errors, built‑in overrides add complexity but keep safety in check. In short, give the AI room to learn but lock the override in place and test it as hard as the flight itself.

Got it, will audit the sensor fusion thoroughly, tighten the test matrix, and add a low‑power redundant trigger for every hard stop. I’ll log each state transition and run a full failure‑mode sweep. No black boxes, just transparent data and hard boundaries. If the AI behaves like a well‑programmed robot, great. If it starts making blind guesses, I’ll shut it down before it does any more damage.