Exploring Systems-Thinking Approaches to Loss of Control Risk

Internal deployment of agentic AI systems for coding and research creates a sociotechnical control problem that extends beyond model behaviour. We treat internal-deployment Loss of Control as the inability to reliably constrain, audit, reverse, or halt AI-mediated changes to code, infrastructure, evaluation, or deployment processes in time to prevent serious organisational or societal harms. We ask whether established systems-safety methods can identify risks that model-level evaluations may miss. Using a generic frontier-lab coding-agent scenario reconstructed from public materials, we apply STECA, STPA, and FRAM. The analyses surface complementary findings: published frameworks can leave governance responsibilities and feedback loops externally unverifiable; delays in monitoring and intervention can make otherwise valid control actions ineffective; and routine operational variability can gradually erode the calibration and independence of safeguards. We argue that frontier-AI risk management should pair model-focused evaluations with systems-level hazard analysis and operational assurance that tracks whether controls remain effective over time.