Self-Governing Cloud Performance: an MCP-Orchestrated MultiAgent Blueprint for Autonomous SLA Assurance in Multi-Tenant Systems

Abstract

This paper presents a practitioner-oriented architectural blueprint for deploying self-governing performance management in multi-tenant cloud systems. Unlike prior theoretical treatments of AI-driven operations, this work provides DevOps Cloud Solutions Architects with concrete implementation patterns, deployment runbooks, infrastructure-as-code templates, and design decisions for constructing a multi-agent system orchestrated through the Model Context Protocol (MCP). The proposed architecture employs five coordinated agents, each bound by SLA-aware governance policies, that autonomously manage the full performance lifecycle from telemetry ingestion through remediation execution. We detail practical integration patterns for connecting agents to existing observability stacks (Prometheus, Grafana, Datadog), container orchestrators (Kubernetes, ECS), and CI/CD pipelines (ArgoCD, GitHub Actions). Based on published industry benchmarks and analytical modeling, the framework projects a 60-75% reduction in Mean Time to Resolution, SLA compliance exceeding 99.95%, and a 30-40% decrease in infrastructure spend. This paper provides the missing implementation bridge between academic agentic AI research and production-grade cloud operations.