Governing AI Agents: Bounded Autonomy and Human Oversight

Abstract

The sudden arrival of Artificial Intelligence (AI), is shifting both scholarly and governmental focus away from issues of technological feasibility to greater concern regarding its use and oversight. AI, which promises great advancements in operational efficiency, scalability and adaptive learning, however presents many challenges as it is used in large-scale applications, including those of ethics and compliance with law; accountability and the ability of humans to exercise meaningful control. Traditional decision-support systems differ from AI agents because they are capable of independently initiating action, coordinating workflow processes, and modifying behavior based upon past experience. These characteristics create new obstacles for existing governance and oversight mechanisms. Research continues to emphasize technical performance and model-based protections against unwanted behavior by AI agents. There remain substantial gaps in governance structures that can support autonomous AI while maintaining some level of institutional accountability. This paper proposes a Bounded Autonomy Governance Framework for AI Agents to be developed using autonomy as a design variable to be governed during all phases of the development process. The proposed framework includes several key components such as calibrated levels of autonomy for AI agents, human-in-the-loop and human-on-the-loop mechanisms to include ethical guardrails, and compliance-by-design provisions throughout the entire life cycle of an AI agent. Using a multi-disciplinary body of literature relating to AI governance, digital infrastructure, and organizational systems, the author views AI agents as social and technical actors embedded in institutions. The paper also builds from the bounded rationality and absorptive capacity literature to strengthen the theoretical case for studying bounded autonomy. Through both theoretical and practical design considerations —including an applied case walkthrough—this paper provides actionable recommendations for policymakers, platform designers, and industry practitioners wishing to responsibly develop and implement AI agents at scale.