Stochastic, Dynamic, Fluid Autonomy in Agentic AI: Implications for Authorship, Inventorship, and Liability

TL;DR

The paper investigates how agentic AI with stochastic, dynamic, and fluid autonomy disrupts traditional attribution frameworks for authorship, inventorship, and liability. It argues that unmappability—where human and AI contributions are irreducibly entangled—renders conventional, origin-based standards impractical. To address this, it proposes functional equivalence: focusing on outcomes and human supervision rather than disentangling specific contributions, with practical reforms across copyright, patent, and tort regimes. The discussion highlights comparative legal tensions (US vs China, and broader EU considerations) and emphasizes the need for empirical validation and risk-based governance to enable workable, forward-looking accountability in human-AI co-creative processes.

Abstract

Agentic Artificial Intelligence (AI) systems, exemplified by OpenAI's DeepResearch, autonomously pursue goals, adapting strategies through implicit learning. Unlike traditional generative AI, which is reactive to user prompts, agentic AI proactively orchestrates complex workflows. It exhibits stochastic, dynamic, and fluid autonomy: its steps and outputs vary probabilistically (stochastic), it evolves based on prior interactions (dynamic), and it operates with significant independence within human-defined parameters, adapting to context (fluid). While this fosters complex, co-evolutionary human-machine interactions capable of generating uniquely synthesized creative outputs, it also irrevocably blurs boundaries--human and machine contributions become irreducibly entangled in intertwined creative processes. Consequently, agentic AI poses significant challenges to legal frameworks reliant on clear attribution: authorship doctrines struggle to disentangle ownership, intellectual property regimes strain to accommodate recursively blended novelty, and liability models falter as accountability diffuses across shifting loci of control. The central issue is not the legal treatment of human versus machine contributions, but the fundamental unmappability--the practical impossibility in many cases--of accurately attributing specific creative elements to either source. When retroactively parsing contributions becomes infeasible, applying distinct standards based on origin becomes impracticable. Therefore, we argue, legal and policy frameworks may need to treat human and machine contributions as functionally equivalent--not for moral or economic reasons, but as a pragmatic necessity.