Risk Management for Agentic AI: A Qualitative Threshold for Identifying Systems with Augmented Algorithmic Agency

Agents, or agentic AI systems, have become capable of managing complex tasks, supporting an augmented form of automation across diverse processes. Yet, the socio-economic impact of their actions and the interconnected nature of their workflows exacerbate existing risks and introduce new technical, legal, and ethical challenges. Their scalability and lawful deployment necessitate comprehensive governing mechanisms to ensure effective risk mitigation and accountability in the event of harm. There is an emerging interest in developing risk management frameworks adapted to the dynamic nature of these systems. However, they often fail to specify at what level of agenticness these frameworks should be considered, creating uncertainty for developers and regulators. This study aims to investigate the level at which AI systems acquire an Augmented Algorithmic Agency, thereby leading to agentic risks and necessitating adapted risk management frameworks. This qualitative threshold acknowledges that agentic risks emerge at the intersection of an agent’s capabilities, identifies the minimum conditions that trigger them, and clarifies how other capabilities serve as risk multipliers. This approach supports developers, deployers, legal officers, and regulators in identifying at what point they need to adapt their AI governance policies to the dynamic, interactive nature of agentic risks.