A Cloud-Native AI Framework for Predictive Failure Detection and Self-Healing Distributed Infrastructure

Abstract

Cloud-native platforms already heal many failures on their own. Container orchestrators restart crashed containers, reschedule
work away from failed machines, and continuously reconcile the running system with its declared state. What they cannot
do is foresee failures that have not yet happened, or reason about problems that no simple rule covers, such as a slow
dependency, a faulty configuration, or an unusual interaction between services. Two developments now address these gaps.
Predictive anomaly detection learns the normal behavior of infrastructure and flags deviations early. Large language model
(LLM) agents can investigate incidents by querying monitoring tools, reading logs and runbooks, forming hypotheses, and
proposing or taking corrective actions. This article proposes a cloud-native AI framework that layers predictive detection and
an AI operations agent on top of orchestrator self-healing. The agent acts through a tool gateway with tiered permissions;
routine actions run automatically, consequential ones pass through a risk-governed workflow, and every step is recorded under
audit-ready controls. Using a design-oriented approach grounded in eleven studies published between 2015 and 2025, the
article maps the evidence base, defines layers of cloud-native self-healing, specifies agent tools and permissions, and maps
agent risks to controls. It illustrates the design with a software-as-a-service payroll provider during its monthly payroll run. It
argues that AI agents extend cloud-native self-healing safely only when their actions are bounded, verified, and accountable.