Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents

Abstract

To anticipate the socio-technical risks posed by AI agents, organizations first need taxonomies to classify them. However, existing AI risk taxonomies focus on broad risks and do not capture the job-specific risks introduced by agents. To address this gap, we make three main contributions. First, we developed a multi-layer framework based on a review of the literature on AI agents. The framework models three core components and their interactions: the agents, their goals, and their environment. Second, we embedded this framework in a structured prompt and applied it to descriptions of 2,078 job tasks from the O*NET occupational database, producing 8,356 risk scenarios labeled by severity and deployment mode (automation or augmentation). We validated these scenarios with 45 workers across 10 job roles and an independent LLM judge, confirming their high plausibility and alignment with the corresponding job tasks. Finally, we extended an existing taxonomy to create a 15-category taxonomy of workplace AI agent risks that covers all our risk scenarios. Our analysis highlights four findings. First, augmentation is not inherently safe because overreliance on agents can gradually erode workers' skills and oversight. Second, erroneous actions of agents account for the largest share of risk scenarios and has the highest number of severe risks. Many of these risks arise at the human-agent boundary. Third, automation is associated mainly with organizational risks (operational failures, financial losses), whereas augmentation is associated mainly with risks to workers (capability erosion, psychological and social risks). Fourth, workers found our taxonomy easier to use for a risk classification task than two other risk taxonomies and preferred it in 64% of non-tied comparisons with a recent generative AI risk taxonomy. Taken together, these findings show that workplace AI agent risks do not arise from agents alone; they also depend on how people work with agents and how agents are deployed. Safer workplaces therefore require not only safer agents but also carefully designed human-AI agent collaboration.