Agentic Copyright, Data Scraping & AI Governance: Toward a Coasean Bargain in the Era of Artificial Intelligence
Paulius Jurcys, Mark Fenwick

TL;DR
This paper proposes an agentic copyright model and a supervised multi-agent governance framework to address legal and market challenges posed by AI systems in creative industries.
Contribution
It introduces the concept of agentic copyright and develops a governance framework integrating legal, technical, and institutional mechanisms for AI-mediated copyright management.
Findings
Agentic copyright can facilitate scalable and fair copyright markets.
Supervised governance mechanisms can mitigate market failures among autonomous AI agents.
Embedding normative constraints into AI architectures aligns agent behavior with copyright law.
Abstract
This paper examines how the rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets. It argues that existing copyright frameworks are ill-equipped to govern AI agent-mediated interactions that occur at scale, speed, and with limited human oversight. The paper introduces the concept of agentic copyright, a model in which AI agents act on behalf of creators and users to negotiate access, attribution, and compensation for copyrighted works. While multi-agent ecosystems promise efficiency gains and reduced transaction costs, they also generate novel market failures, including miscoordination, conflict, and collusion among autonomous agents. To address these market failures, the paper develops a supervised multi-agent governance framework that integrates legal rules and principles, technical protocols, and institutional oversight. This…
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