By deploying AI agent loops bounded to deterministic automation at the mission edge, the Air Force can match adversary cyber operations at machine speed with the small forward Mission Defense Teams that defend a weapon system in contested, degraded, and operationally limited (CDO-L) environments. A model loop selecting from a pre-validated play library, an idempotent configuration-as-code runtime executing the selected play, structured feedback confirming the system reached the intended state, and senior-NCO-encoded mission-system hardening policy can be combined to keep defense at the speed of attack. This approach dynamically routes containment decisions by alert class, policy, and adversary signature, multiplying operational tempo for 1D7X1 watch crews and turning Mission Defense Team Chief experience into durable automation that runs continuously. By extending bounded agent loops to mission systems under machine-speed adversary probing, warfighters gain a defensive posture as part of an AI-first force at the speed of mission, enabling faster, more resilient mission execution in CDO-L environments.