Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation

A recent preprint on arXiv (2605.19779) presents innovative methods for uncertainty quantification in the evaluation of continuous AI agents, utilizing split conformal prediction and adaptive conformal inference (ACI). This technique ensures coverage guarantees for predicted quality scores without relying on distributional assumptions. Conformal intervals maintain a calibration error of less than 0.02 across all nominal levels at a 24-hour horizon, while ACI effectively adjusts intervals by 35% after agent releases before reconverging. The research also establishes compositional uncertainty bounds for multi-agent systems, tested through simulations with inter-stage correlations ranging from -0.5 to 0.9, and introduces a conformal abstention rule for pairwise rankings. Analyzing 50 agents using 18 hourly real-time signals, the findings indicate that per-agent conditional coverage is closely aligned with the nominal level (mean 80.4%, with 90% of agents between [72%, 90%]), and that variations in cross-source sentiment can forecast ranking fluctuations.