Safe Remediation as Risk-Constrained Intervention Decision in Microservice Systems

• URL: http://arxiv.org/abs/2607.20005v1
• Date: Wed, 22 Jul 2026 10:43:14 GMT
• Title: Safe Remediation as Risk-Constrained Intervention Decision in Microservice Systems
• Abstract summary: In modern IT operations (IT-Ops), the cost of an incorrect repair often exceeds the cost of no action at all.<n>This paper makes three contributions to close this gap: (i) we reformulate safe remediation as a risk-constrained intervention decision problem; (ii) we introduce a three-dimensional risk decomposition; and (iii) we design a context-adaptive human-in-the-loop (HITL) gate that turns from a binary failsafe into a bandwidth-aware control layer.
• Score: 2.1686718805804976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In modern IT operations (IT-Ops), the cost of an incorrect repair often exceeds the cost of no action at all. Yet existing automated remediation systems are designed to generate actions rather than to decide whether intervention is warranted, leaving safety as an afterthought enforced by manual approval. This paper makes three contributions to close this gap: (i) we reformulate safe remediation as a risk-constrained intervention decision problem and cast it as a Constrained Markov Decision Process (CMDP), in which the agent maximizes repair success subject to a bounded false remediation rate (FRR); (ii) we introduce a three-dimensional risk decomposition comprising blast radius, reversibility, and epistemic uncertainty, providing operators with an interpretable per-action safety interface; and (iii) we design a context-adaptive human-in-the-loop (HITL) gate that turns escalation from a binary failsafe into a bandwidth-aware control layer responsive to on-call load and business criticality. The full policy is learned offline from historical incident logs, enabling explicit control of the expected FRR. Experiments on the Train Ticket microservice benchmark with Chaos Mesh fault injection and an RCAEval-aligned fault taxonomy show that our framework reduces FRR by 39% while improving repair success by 2.5 points over a strong runbook baseline, and reduces on-call escalation load by 17% relative to a fixed-threshold variant.

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