Identifying and Mitigating Risks Emerging from Uncertainty in AI Agents: A Stakeholder-Centered, Scenario-Based Approach

Today, AI agents are already capable of performing impressive tasks. However, their non-deterministic nature introduces novel risks that must be understood and mitigated carefully. This study adopts a scenario- based approach to identify and mitigate risks stemming from AI agents’ inherent uncertainties. In a workshop with 30 stakeholders from a medium-sized German software company, participants developed structured risk scenarios based on projections of system behavior, tasks, users, and environment. Qualitative analysis of 35 scenarios and 12 interviews enabled the identification of design principles that enhance stakeholders’ perception of risk manageability, while also assessing the approach’s strengths and limitations. Findings highlight financial risks as predominant and reveal stakeholder preferences for confidence estimators and human-in-the-loop strategies, alongside novel, context- specific mitigation approaches that remain underexplored in current academic literature. These results demonstrate the effectiveness of the scenario- based method in uncovering context-specific risks and fostering the development of mitigation strategies across diverse practitioner groups.