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

arXiv: https://arxiv.org/abs/2504.04058 · HTML: https://mlsanity.com/paper/2504.04058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Built on by (most cited first, 2 of 2)

- [LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://mlsanity.com/paper/2505.00753/) (2025)
- [Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era](https://mlsanity.com/paper/2506.09755/) (2025)

## Abstract (arXiv)

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.