[Paper] Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
Source: arXiv - 2606.09122v1
Overview
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.
Key Contributions
This paper presents research in the following areas:
- cs.SE
- cs.AI
- cs.ET
- cs.MA
- cs.NI
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.SE.
Authors
- Arun Malik
Paper Information
- arXiv ID: 2606.09122v1
- Categories: cs.SE, cs.AI, cs.ET, cs.MA, cs.NI
- Published: June 8, 2026
- PDF: Download PDF