# Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systems

arXiv: https://arxiv.org/abs/2607.25405 · HTML: https://mlsanity.com/paper/2607.25405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 68 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 20 of 20)

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- [CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization](https://mlsanity.com/paper/2310.10134/) (2023)
- [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025)
- [Fully Autonomous AI Agents Should Not be Developed](https://mlsanity.com/paper/2502.02649/) (2025)
- [Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems](https://mlsanity.com/paper/2604.14228/) (2026)
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- [Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking](https://mlsanity.com/paper/2411.10184/) (2024)
- [Optimizing delegation between human and AI collaborative agents](https://mlsanity.com/paper/2309.14718/) (2023)
- [On Implementing Autonomous Supply Chains: a Multi-Agent System Approach](https://mlsanity.com/paper/2310.09435/) (2023)
- [Will bots take over the supply chain? Revisiting Agent-based supply chain automation](https://mlsanity.com/paper/2109.01703/) (2021)
- [AI Agent Behavioral Science](https://mlsanity.com/paper/2506.06366/) (2025)
- [AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing](https://mlsanity.com/paper/2507.01376/) (2025)
- [AURA: An Agent Autonomy Risk Assessment Framework](https://mlsanity.com/paper/2510.15739/) (2025)
- [Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness](https://mlsanity.com/paper/2601.06223/) (2026)
- [Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels](https://mlsanity.com/paper/2401.14183/) (2023)
- [Architectures for Building Agentic AI](https://mlsanity.com/paper/2512.09458/) (2025)

## Abstract (arXiv)

Supply chain decision-making is rapidly transforming with the rise of agentic AI - highly autonomous systems that can operate on complex, long-horizon tasks. Yet the adoption of agentic systems outpaces their governance: existing taxonomies of autonomy only offer discrete classifications, rely on subjective judgement, and cannot track autonomy across a system's life cycle, leaving enterprises unable to assess the risks of increasingly autonomous supply chain agents. This paper proposes the Agentic AI Autonomy Assessment (AAAA) framework, which defines and measures the degree of autonomy at a task level. The framework is based on the three dimensions of user delegation, consultation, and collaboration, enabling continuous monitoring from an agent's development through its runtime to end-of-life. The framework's construct validity was tested in a simulated beer distribution game, examining how the autonomy score relates to a company's performance. Results reveal a weak link between autonomy and tier costs with a positional effect: upstream tiers benefit from higher autonomy while downstream tiers are harmed, positioning autonomy as an inherent dimension of agentic systems, orthogonal to capability. The framework provides a foundation for risk assessment, governance, and transparent autonomy policies to support the governed enterprise adoption of agentic AI in supply chains.