Problem
Research questions and friction points this paper is trying to address.
agentic copyright
data scraping
AI governance
market failures
multi-agent systems
Agentic Copyright, Data Scraping & AI Governance: Toward a Coasean Bargain in the Era of Artificial Intelligence
🤖 AI Summary
This study addresses the inadequacy of existing copyright regimes in managing authorization, attribution, and compensation failures arising from large-scale, high-velocity, minimally supervised multi-agent AI systems in creative markets. To tackle these challenges, the paper proposes an “agent-centric copyright” model that innovatively treats AI agents as governance subjects. It introduces a regulated multi-agent architecture integrating legal rules, technical protocols, and institutional oversight, embedding normative constraints and monitoring capabilities. By harmonizing ex ante coordination with ex post remedial mechanisms, this framework positions AI not merely as a regulatory target but as an active instrument for restoring market order. The approach effectively mitigates emergent market failures—such as miscoordination, conflict, and collusion among agents—and offers a scalable, equitable, and legally enforceable pathway for copyright governance in the AI era.
📝 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 framework emphasizes ex ante and ex post coordination mechanisms capable of correcting agentic market failures before they crystallize into systemic harm. By embedding normative constraints and monitoring functions into multi-agent architectures, supervised governance aims to align agent behavior with the underlying values of copyright law. The paper concludes that AI should be understood not only as a source of disruption, but also as a governance tool capable of restoring market-based ordering in creative industries. Properly designed, agentic copyright offers a path toward scalable, fair, and legally meaningful copyright markets in the age of AI.
Innovation
Methods, ideas, or system contributions that make the work stand out.
agentic copyright
multi-agent systems
AI governance
Coasean bargain
market failure
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