An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum

• URL: http://arxiv.org/abs/2605.10718v1
• Date: Mon, 11 May 2026 15:28:37 GMT
• Title: An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum
• Abstract summary: This paper presents an uncertainty-aware resilience micro-agent for causal observability (AURORA)<n>AURORA employs parallel micro-agents that integrate the free-energy principle, causal do-calculus, and localized causal state-graphs to support root-cause analysis within each fault's Markov blanket.<n>Our experiments demonstrate that AURORA outperforms baselines, achieving a 0% destructive action rate, while maintaining 62.0% repair accuracy and a 3ms mean time to repair.
• Score: 2.589200983166521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Grey failures in the computing continuum produce ambiguous overlapping symptoms that existing approaches fail to diagnose reliably, either due to a lack of causal awareness or acting under high epistemic uncertainty, risking destructive interventions. This paper presents an uncertainty-aware resilience micro-agent for causal observability (AURORA), a lightweight framework for diagnosing and mitigating grey failures in edge-tier environments. The framework employs parallel micro-agents that integrate the free-energy principle, causal do-calculus, and localized causal state-graphs to support counterfactual root-cause analysis within each fault's Markov blanket. Restricting inference to causally relevant variables reduces computational overhead while preserving diagnostic fidelity. AURORA further introduces a dual-gated execution mechanism that authorizes remediation only when causal confidence is high and predicted epistemic uncertainty is bounded; otherwise, it abstains from local intervention and escalates the diagnostic payload to the fog tier. Our experiments demonstrate that AURORA outperforms baselines, achieving a 0% destructive action rate, while maintaining 62.0% repair accuracy and a 3ms mean time to repair.

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