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

TL;DR

AURORA is a lightweight, uncertainty-aware micro-agent framework that improves diagnosis and mitigation of grey failures in the computing continuum by leveraging causal analysis and confidence-based decision making.

Contribution

It introduces a novel micro-agent architecture integrating causal inference, uncertainty estimation, and a dual-gated mechanism for safe, efficient fault diagnosis and repair.

Findings

AURORA achieves 0% destructive actions in experiments.

It maintains 62.0% repair accuracy.

It has a mean time to repair of 3ms.

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.