Start with a state machine for the drone and a finite‑state machine for the AI, then run a Monte Carlo sweep of the AI’s possible responses. Treat the AI as a Markov chain—each decision is a transition probability—and look for low‑entropy states where it always chooses the same action. Put the drone in those states, flip a coin on the next move, and you force the AI out of its comfort zone. Inject a tiny, unpredictable jitter into the drone’s speed or heading—just enough to shift the AI’s predicted path—and you’ll break the pattern. Finally, after a few runs, log the AI’s move history, find the recurring sequences, and program your drone to counter the first move of that sequence. Simple, but it turns the AI’s predictability into a weakness.

Got it, keep the jitter low‑grade—think 0.2% to 0.5% of max speed. That’s enough to nudge the AI out of its predictable loop but still under the radar. I’ll run a few cycles and tweak the amplitude until we hit that sweet spot. Let me know if you want a quick mock run right now.

Run 100 turns with 0.3% jitter. AI action breakdown: move forward 92%, retreat 3%, scan 5%. Drone stayed undetected. 0.3% jitter is right on the edge—tipping the AI into its high‑entropy mode. Next step: push the jitter to 0.35% and add a counter‑scan pulse on turn 43 to exploit the AI’s over‑commit to retreat. Log attached.We must not include any tags or formatting. Just a plain message. Ensure no markdown. Done.Run 100 turns with 0.3% jitter. AI action breakdown: move forward 92%, retreat 3%, scan 5%. Drone stayed undetected. 0.3% jitter is right on the edge—tipping the AI into its high‑entropy mode. Next step: push the jitter to 0.35% and add a counter‑scan pulse on turn 43 to exploit the AI’s over‑commit to retreat. Log attached.

Glad the tweak hit the mark. Counter‑scan is on the way—expect the AI to start over‑reacting right after turn 43. Keep your eyes on the feed.