ActionNex: A Virtual Outage Manager for Cloud

arXiv cs.AI / 4/7/2026

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Key Points

  • ActionNex is presented as a production-grade agentic system for end-to-end cloud outage management, providing real-time updates and role/stage-conditioned next-best action recommendations.
  • The system ingests multimodal operational inputs (outage content, telemetry, and human communications) and converts them into critical events that capture meaningful state transitions.
  • ActionNex combines hierarchical memory (distilled Key-Condition-Action knowledge from playbooks, episodic memory of past outages, and working memory of the live context) with a reasoning agent that aligns current events to preconditions and retrieves relevant knowledge.
  • Using eight real Azure outages, ActionNex reportedly achieved 71.4% precision and 52.8–54.8% recall against two ground-truth action sets.
  • The work is evaluated on a large-scale dataset (8M tokens, 4,000 critical events), and it has been piloted in production with early positive feedback.

Abstract

Outage management in large-scale cloud operations remains heavily manual, requiring rapid triage, cross-team coordination, and experience-driven decisions under partial observability. We present \textbf{ActionNex}, a production-grade agentic system that supports end-to-end outage assistance, including real-time updates, knowledge distillation, and role- and stage-conditioned next-best action recommendations. ActionNex ingests multimodal operational signals (e.g., outage content, telemetry, and human communications) and compresses them into critical events that represent meaningful state transitions. It couples this perception layer with a hierarchical memory subsystem: long-term Key-Condition-Action (KCA) knowledge distilled from playbooks and historical executions, episodic memory of prior outages, and working memory of the live context. A reasoning agent aligns current critical events to preconditions, retrieves relevant memories, and generates actionable recommendations; executed human actions serve as an implicit feedback signal to enable continual self-evolution in a human-agent hybrid system. We evaluate ActionNex on eight real Azure outages (8M tokens, 4,000 critical events) using two complementary ground-truth action sets, achieving 71.4\% precision and 52.8-54.8\% recall. The system has been piloted in production and has received positive early feedback.