Variability-Aware Trust in Agentic Clinical AI: Modeling and Measuring Trajectory-Level Uncertainty

Abstract Large language models (LLMs) are increasingly deployed within multi-step agentic workflows in healthcare, where sequential decisions shape downstream clinical actions. However, existing evaluations primarily assess single-turn accuracy and fail to capture how stochastic variability propagates across multi-step trajectories. In this work, we introduce Variability-Aware Agentic AI Trust (VA-AAT), a framework for modeling and measuring trajectory-level uncertainty in agentic clinical systems. We formalize variability propagation through trajectory divergence and derive conservative bounds that characterize how per-step stochasticity may accumulate under simplifying assumptions. Building on this formulation, we define repeatability-based metrics that quantify consistency at both step and trajectory levels, and we propose a risk-informed trust framework linking repeatability to graded oversight strategies. We empirically evaluate VA-AAT on simulated and real-world clinical workflows, demonstrating that small per-step variations can lead to substantial divergence in multi-step trajectories. Variability was strongly influenced by model selection and decoding temperature, and we identified instruction-following failures (“unknown actions”) as an additional source of instability. Importantly, we show that repeatability provides a useful but incomplete indicator of system reliability: highly repeatable systems may still produce consistently incorrect outputs, while less repeatable systems may reflect underlying clinical ambiguity. Accordingly, repeatability should be interpreted as a diagnostic signal of variability-induced risk rather than a guarantee of safety. These findings highlight the need for trajectory-level evaluation and variability-aware governance in clinical AI. The proposed framework provides a foundation for assessing stochastic reliability in agentic systems and supports the design of risk-sensitive deployment strategies in healthcare settings.