InfraAgent: A Multi-Agent AI Framework for Predictive Deployment Failure Detection and Autonomous Remediation in Multi-Cloud Environments

Abstract

Managing modern cloud-native infrastructure is, frankly, a problem that has grown faster than our tools for handling it. Continuous delivery pipelines, ephemeral containers, and sprawling multi-cloud Infrastructure-as-Code (IaC) ecosystems create an operational surface that expands combinatorially with organisational scale and static rule-based monitoring simply cannot keep up. This paper presents InfraAgent, a five-agent agentic AI framework designed for predictive infrastructure management and autonomous deployment operations in heterogeneous multi-cloud settings. The framework brings together: a Temporal Graph Attention Network (T-GAN) agent that jointly models infrastructure dependency topology and multi-variate telemetry for deployment failure prediction across 1-hour, 6-hour, and 24-hour horizons; a Remediation Planning Agent (RPA) built on a Large Language Model (LLM) that generates context-aware IaC patches, rollout prescriptions, and rollback directives directly from risk scores; a Deployment Safety Agent (DSA) that enforces configurable pre-flight autonomy gates; a Capacity Forecasting Agent (CFA) built on a Temporal Fusion Transformer (TFT) for probabilistic 24-hour to 72-hour demand forecasting; and a Continuous Operational Intelligence Orchestrator (COIO) that provides closed-loop governance, cryptographic auditability, and online model adaptation. We evaluate InfraAgent on 47,800 deployment events and 310,000 telemetry snapshots drawn from AWS, Azure, and GCP production environments. The results show a Mean Time to Detection of 4.3 minutes (an 85.2% reduction over rule-based baselines), a deployment failure prediction F1 of 0.931, a 76.8% reduction in production incidents per 100 Continuous Integration/Continuous Delivery (CI/CD) runs, and 91.4% capacity forecast accuracy at the 72-hour horizon, suggesting that coordinated agentic reasoning grounded in graph-temporal learning can meaningfully shift cloud operations from reactive firefighting toward continuous, self-correcting infrastructure assurance.