Measuring AI Agent Autonomy: Towards a Scalable Approach with Code Inspection
Peter Cihon, Merlin Stein, Gagan Bansal, Sam Manning, Kevin Shuai Xu · SuperIntelligence - Robotics - Safety & Alignment · 2025
AI agents are AI systems that can achieve complex goals autonomously. Assess-ing the level of agent autonomy is crucial for understanding both their potential benefits and risks. Current assessments of autonomy often focus on specific risks and rely on run-time evaluations – observations of agent actions during operation. We introduce a code-based assessment of autonomy that eliminates the need to run an AI agent to perform specific tasks, thereby reducing the costs and risks associated with run-time evaluations. Using this code-based framework, the or-chestration code used to run an AI agent can be scored according to a taxonomy that assesses attributes of autonomy: impact and oversight. We demonstrate this approach with the AutoGen framework and select applications.