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

arXiv: https://arxiv.org/abs/2606.09122 · HTML: https://mlsanity.com/paper/2606.09122/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 3 of 3)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)

## Built on by (most cited first, 3 of 3)

- [Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production](https://mlsanity.com/paper/2607.07052/) (2026)
- [Decentralized Granular Access Control for Agentic AI Systems in Critical Infrastructure](https://mlsanity.com/paper/2607.22611/) (2026)
- [From Reactive to Autonomous: Evolution of AI Operations in Cloud Network Infrastructure](https://mlsanity.com/paper/2608.14574/) (2026)

## Abstract (arXiv)

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.