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AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds

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arxiv 2501.06706 v1 pith:ORFEDFSX submitted 2025-01-12 cs.AI cs.DCcs.MAcs.SE

classification cs.AIcs.DCcs.MAcs.SE
keywords agentsaiopsaiopslabframeworkoperationaltaskscloudcomplex
verification ladder T0 review T1 audit T2 compute T3 formal
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AI for IT Operations (AIOps) aims to automate complex operational tasks, such as fault localization and root cause analysis, to reduce human workload and minimize customer impact. While traditional DevOps tools and AIOps algorithms often focus on addressing isolated operational tasks, recent advances in Large Language Models (LLMs) and AI agents are revolutionizing AIOps by enabling end-to-end and multitask automation. This paper envisions a future where AI agents autonomously manage operational tasks throughout the entire incident lifecycle, leading to self-healing cloud systems, a paradigm we term AgentOps. Realizing this vision requires a comprehensive framework to guide the design, development, and evaluation of these agents. To this end, we present AIOPSLAB, a framework that not only deploys microservice cloud environments, injects faults, generates workloads, and exports telemetry data but also orchestrates these components and provides interfaces for interacting with and evaluating agents. We discuss the key requirements for such a holistic framework and demonstrate how AIOPSLAB can facilitate the evaluation of next-generation AIOps agents. Through evaluations of state-of-the-art LLM agents within the benchmark created by AIOPSLAB, we provide insights into their capabilities and limitations in handling complex operational tasks in cloud environments.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents

    cs.DB 2026-07 conditional novelty 6.0 of 10

    A production-fidelity benchmark finds that LLM database-operation agents achieve at most 17.9% safe recovery versus 93.4% for human DBAs.

  2. OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning

    cs.LG 2026-04 unverdicted novelty 6.0 of 10

    OpsLLM's pipeline (HITL data curation, SFT, GRPO RL with a domain process reward model) improves LLM accuracy on software-operations QA and RCA, especially on in-distribution root-cause-analysis tasks.

  3. ARBITER: Guarded Agentic Control for SLO-Oriented Kubernetes Remediation

    cs.DC 2026-07 conditional novelty 5.0 of 10

    ARBITER separates SLO remediation planning (deterministic rules or an LLM) from deterministic guarded execution over a finite typed Kubernetes-action set, and rolls back bad images and targets downstream critical-path...

  4. KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management

    cs.DC 2025-09 conditional novelty 5.0 of 10

    A modular LLM-orchestrated agent framework translates natural language requests into end-to-end Kubernetes operations, with dynamically generated and validated tools.

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