AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds
This addresses the need for comprehensive evaluation tools in AIOps to advance autonomous cloud systems, though it is incremental as it builds on existing AIOps and LLM advancements.
The paper tackles the challenge of evaluating AI agents for autonomous cloud management by introducing AIOPSLAB, a holistic framework that deploys cloud environments, injects faults, and orchestrates components to assess agents, demonstrating insights into their capabilities and limitations in handling operational tasks.
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.