An Explainable Agentic AI Framework for Intelligent Multi-Cloud Resource Allocation

Abstract

Enterprises increasingly distribute computing workloads across multiple public and private cloud providers to reduce cost, avoid vendor lock-in, and improve resilience, but this multiplies the complexity of deciding, for every incoming task, which provider to use. Static or single-objective heuristics — always choosing the cheapest or always the fastest provider — routinely fail because cost, latency, and reliability trade off against one another in ways that shift with demand and provider conditions. This paper proposes and evaluates an explainable agentic AI framework for multi-cloud task allocation built on a contextual-bandit agent (LinUCB) that observes each provider's current price, estimated latency, and load before autonomously selecting a placement, then updates its policy online from the resulting cost, latency, and service-level-agreement (SLA) outcome. Because no public multi-cloud trace exposes simultaneous, ground-truth price/latency/capacity data across providers, the framework is evaluated on a controlled, fully documented discrete-time simulation of four heterogeneous providers under realistic load dynamics — a standard and disclosed methodology in this research area. Across 30 independent simulation runs of 3,000 tasks each, the agent achieved a statistically significant improvement over the strongest single fixedweight heuristic baseline (Static-Weighted) on every safety- and balance-related metric: 86.2% fewer SLA violations, 20.9% lower average latency, and 19.6% higher load-balancing fairness (Jain's index = 0.955 vs. 0.799, paired t-test, all p < 0.001), at a 20.6% higher cost. Under a simulated transient provider degradation (a 5× latency spike on one provider for 20% of a run), the agent held SLA violations to 0.4%, versus 9.9% for naive round-robin routing and 40.2% for cost-only routing, while remaining markedly cheaper than a purely latencyreactive baseline. To support the "explainable" requirement of agentic systems intended for production use, the framework exposes two complementary explanation layers: the bandit's own per-provider linear coefficients, and a surrogate Random Forest trained to imitate the agent's decisions (99.6% fidelity), whose permutation importance identifies observed latency and price as the dominant drivers of every allocation decision. These results indicate that a lightweight, interpretable contextual-bandit agent can deliver a favourable, auditable balance of cost, latency, SLA compliance, and fairness in multi-cloud environments, including under operational stress, without the opacity of deeper reinforcement learning or black-box agentic architectures.