Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
arXiv:2606.09122v1 Announce Type: cross 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
The increasing complexity and scale of cloud network infrastructure are making traditional manual incident response unsustainable, pushing the need for autonomous solutions.
Autonomous AI agents for network operations promise to significantly reduce downtime, operational costs, and human error in critical infrastructure, leading to more resilient and efficient digital systems.
This architecture suggests a fundamental shift from human-driven to AI-driven incident resolution in hyperscale environments, enhancing reliability and potentially displacing some network operations roles.
- · Cloud service providers
- · Hyperscale network operators
- · AI software developers
- · Infrastructure-reliant businesses
- · Traditional network operations roles
- · Legacy incident management software vendors
- · Companies slow to adopt AI automation
Reduced mean time to resolution and increased network uptime across hyperscale cloud environments.
A significant re-skilling or displacement of human network operations staff as AI agents take over more tasks.
The potential for new vulnerabilities or failure modes if autonomous AI agents themselves become compromised or misbehave.
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Read at arXiv cs.AI