Legal Governance of Agentic AI and Autonomous Agents

The rapid emergence of autonomous AI agents-systems capable of independent decision-making, task execution, and complex problem-solving with minimal human oversight-represents a paradigmatic shift in the nature of work itself. Unlike traditional AI tools that merely augment human capabilities, these sophisticated algorithmic entities possess agency; they plan, execute, and adapt in ways that fundamentally challenge existing legal frameworks, ethical paradigms, and regulatory structures.

This edited volume addresses the critical gap in scholarly literature by providing the first comprehensive, legally focused analysis of how autonomous AI agents are transforming the future of work, with particular emphasis on regulatory challenges, governance frameworks, and policy solutions. Drawing from interdisciplinary expertise across law, ethics, economics, and technology, the book addresses critical gaps in current literature by examining the unique legal and ethical questions that arise when AI systems possess degrees of agency and autonomy in workplace settings. The work is especially timely given recent breakthroughs in “Agentic AI” systems such as OpenAI’s Operator and Anthropic’s Claude, which represent a fundamental shift in AI capabilities.

Through interdisciplinary analysis, this book examines not merely the technological capabilities of autonomous agents, but their profound implications for liability, accountability, regulatory frameworks, and the fundamental nature of employment relationships. This approach ensures rigorous academic treatment while maintaining accessibility for practitioners, policymakers, and researchers navigating the complex intersection of AI governance and employment law in an increasingly automated world.

The book’s unique contribution lies in its specificity-moving beyond general AI regulation discourse to focus precisely on autonomous agents within workplace contexts. It addresses urgent questions: How do existing legal doctrines accommodate entities that make independent decisions affecting human livelihoods? What governance mechanisms ensure algorithmic fairness while preserving innovation? How should liability be distributed when autonomous systems cause harm or make discriminatory decisions? These inquiries demand immediate scholarly attention as autonomous agents transition from experimental technologies to workforce realities.