An exploratory analysis Explainable Risk Assessment and Independent Verification of AI-Agent Deployment Changes in Multi-Cloud Environments

Decisions about AI-agent deployment require evidence about confidence, repeatability and recovery, as well as task completion. This article analyses a structured collection of published benchmark results to identify requirements for explainable risk assessment and independent verification of deployment changes. The dataset contains 100 metric rows representing 16 agent systems and 14 experimental conditions across two benchmark studies. Analyses preserve the original evaluation settings and distinguish measured quantities from implications for cloud engineering. Across the 16 systems, mean autonomous accuracy was 70.74%, while mean success on the most confident half of cases was 79.51%, a descriptive difference of 8.77 percentage points. Reported perturbation results showed declines of 8.76 and 6.25 percentage points for two evaluated models. Recovery proportions calculated from published counts were 80.85% for ReAct and 67.31% for Reflexion. These observations indicate that selective handling, stress testing and recovery measurement provide information beyond a single success score. They do not establish calibrated deployment risk or prove that independent verification reduces unsafe cloud changes. An evidenceinformed architecture connects a change record, risk estimate, explanation and independently controlled checks to approval, rejection or deferral.