Source note
Autonomous Incident Resolution at Hyperscale: An Agentic AI Architecture for Network Operations
Summary
The paper describes a production multi-agent AI system for resolving hyperscale cloud network incidents. It claims over 90% autonomous resolution for common incident types, with MTTR cut from hours to minutes.
Problem
- Human on-call response does not scale to cloud networks with millions of devices across hundreds of data centers.
- Network failures can cascade in seconds, while manual diagnosis and remediation often take minutes to hours.
- Operational knowledge often sits with senior engineers, which slows response and makes incident handling inconsistent.
Approach
- The system splits incident handling across four agents: intake, planning, execution, and verification.
- Agents use structured playbooks built from observed human resolutions, with explicit preconditions, steps, success checks, and abort rules.
- Operational actions are exposed as typed skills with declared permissions, schemas, idempotency behavior, and audit logs.
- Safety controls check authorization, blast radius, redundancy, rate limits, rollback paths, and post-action health before or after execution.
- Autonomy increases through levels 0 to 4, from advisory mode to self-improving behavior, with demotion and circuit breakers when failure rates rise.
Results
- In production at a major cloud provider, the system claims autonomous resolution rates above 90% for well-understood incident categories.
- For autonomously handled incidents, MTTR improved by two orders of magnitude versus human response, moving from hours to minutes.
- False positive remediation is reported below 5%, with no customer-visible impact attributed to those cases.
- The paper reports zero critical incidents caused by autonomous actions and says no action exceeded its predicted blast radius.
- Automatic rollbacks occurred in a small percentage of execution attempts, and all recovered within defined time bounds.
- The excerpt does not provide raw incident counts, dataset details, confidence intervals, or per-category evaluation tables.