Statistical Frameworks for Uncertainty in Agentic Systems

Long-horizon agentic workloads mark a shift into a regime where declarative systems adaptively build larger declarative systems. In this setting, uncertainty quantification must address not only output-level error but also the risks of adaptive resource allocation: budgeted routing to tools and subagents, safeguards against unnecessary spend, and principled stopping under continuous monitoring. As systems become more modular, we also need compositional guarantees that aggregate local uncertainties into end-to-end risk bounds. The aim of this workshop is to bring distribution-free statistical tools to bear on these agentic systems. We focus on three themes: (1) distribution-free validity layers for coverage, risk, and abstention under heterogeneity and distribution shift; (2) anytime-valid sequential inference for continuous monitoring, evidence aggregation, and principled stopping; and (3) uncertainty reporting for interactive components and inter-agent interaction. We invite contributions advancing statistical foundations for uncertainty in agentic systems, including theory, methodology, benchmarks, and case studies.