StateBundle: Cause-Centered Evidence Selection for Agentic Data-Center Operations

Autonomous data-center operations require agents to interpret heterogeneous telemetry and manage faults that propagate across physical facilities and the IT stack. In modern operational environments, redundant and fragmented telemetry complicates reasoning while increasing context and computational costs. We propose Statebundle, a hierarchical, cause-centered evidence-selection method that constructs compact observation bundles for operational agents. Statebundle selects nonredundant evidence anchors and retrieves corroborating observations through contrastive cross-modal alignment, hierarchical root-cause and propagation supervision, and task-oriented evidence-selection objectives. To enable controlled cross-layer evaluation, we further introduce DC-Bench, a deterministic simulator-backed benchmark that couples physical facility with compute, storage, network, control-plane, and application dynamics. DC-Bench contains 72 task instances spanning 18 fault mechanisms and four operational objectives: detection, localization, root-cause analysis, and mitigation. Across ten evaluated agent systems, Statebundle reduces total model-token usage per episode by 37.4–80.9% relative to full-canonical observations, while improving task success for evaluated agents.