From Detection to Verified Action: Operational Readiness for AI-Enabled Cloud Failure Management

Abstract

Artificial intelligence for information technology operations has progressed from alert correlation and anomaly detection to root-cause analysis, mitigation recommendation, and agents that can invoke infrastructure tools. This progression creates an assurance problem: analytical performance does not establish authority to change a live cloud system. This critical narrative review examines the evidence required before AI output may influence or execute failure-management action in mission-critical cloud infrastructure. Searches through 24 July 2026 covered scholarly databases, major systems venues, standards sources, and authoritative production reports. Forty-four sources were coded by operational task, evidence setting, authority, safety control, rollback, and outcome verification. Production evidence is substantial for detection, triage, diagnosis, and several narrowly bounded mitigation systems, but remains weak for general-purpose autonomous action. Recent agentic AIOps surveys emphasize contracts, bounded tools, canary deployment, and rollback; the unresolved need is an assessment method that separates what a system can infer, what it may do, and what evidence shows that the action remained safe. The proposed Operational Decision-Readiness and Verification framework represents a deployment claim through analytical capability, operational authority, and assurance maturity. Twelve domains use explicit 0-3 evidence anchors, while endpoint validity, evidence integrity, intervention risk, authorization, reversibility, and recovery verification operate as non-compensatory gates. Worked assessments of six systems illustrate distinctions among analytical support, testbed execution, and narrow production autonomy. The framework is conceptual and requires prospective and inter-rater validation; it structures an actionspecific readiness case rather than certifying a model or product