Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations

• URL: http://arxiv.org/abs/2606.09122v1
• Date: Mon, 08 Jun 2026 07:15:53 GMT
• Title: Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
• Abstract summary: This paper presents an agentic AI architecture for autonomous incident resolution in large-scale network operations.<n>Our system employs a multi-agent orchestration framework where specialized AI agents collaborate to detect, diagnose, and remediate network incidents without human intervention.
• Score: 0.33842793760651557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cloud network infrastructure at hyperscale presents unique operational challenges where traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures. This paper presents an agentic AI architecture for autonomous incident resolution in large-scale network operations. Our system employs a multi-agent orchestration framework where specialized AI agents collaborate to detect, diagnose, and remediate network incidents without human intervention. We describe the architectural principles, including hierarchical agent decomposition, skills-based tool invocation via standardized protocols, structured knowledge encoding from operational runbooks, progressive autonomy with safety boundaries, and closed-loop verification. The architecture has been deployed in production at a major cloud provider, demonstrating that agentic AI systems can achieve autonomous resolution rates exceeding 90% for common incident categories while maintaining safety guarantees through layered authorization and rollback mechanisms. We discuss design tradeoffs, failure modes, and lessons learned from operating autonomous AI agents at scale.

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