Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation

• URL: http://arxiv.org/abs/2605.19779v1
• Date: Tue, 19 May 2026 12:47:21 GMT
• Title: Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation
• Abstract summary: We adapt split conformal prediction and adaptive conformal inference to continuous AI agent evaluation.<n>We show that per-agent conditional coverage is well-concentrated around the nominal level.<n>A circularity-controlled validation confirms the framework captures signal beyond benchmarks.
• Score: 1.2299000423193074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We adapt split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guarantees for forecasted quality scores. Conformal intervals achieve calibration error below 0.02 across all nominal levels at the 24h horizon, while ACI correctly widens intervals by 35% following agent releases then reconverges. We further develop compositional uncertainty bounds for multi-agent pipelines (validated via simulation across inter-stage correlations rho in [-0.5, 0.9]), a conformal abstention rule for pairwise rankings with controlled false-ranking rate, and FDR-corrected abstention for leaderboard-scale multiple testing. Evaluating 50 agents via 18 real-time signals collected hourly, we show that per-agent conditional coverage is well-concentrated around the nominal level (mean 80.4%, 90% of agents within [72%, 90%]), and that cross-source sentiment divergence predicts ranking instability (r=0.64, p<0.01). A circularity-controlled validation confirms the framework captures signal beyond benchmarks (rho_s=0.52, p<0.01, n=35). Code and data are released under CC BY 4.0.

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