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

Yinfang Chen, Manish Shetty, Gagan Somashekar, Minghua Ma, Yogesh Simmhan, Jonathan Mace, Chetan Bansal, Rujia Wang, S R (Microsoft Research / academic collaboration)

• Why: This is the "AgentOps" position paper — LLM agents autonomously handling fault localization, root-cause analysis, and incident response through the full incident lifecycle. For a networking PM whose world is increasingly AI-assisted operations, this frames the evaluation problem: how would you even know if an ops agent is any good?
• What: A framework that deploys diverse cloud environments, injects faults, generates workloads, and exports telemetry, then orchestrates it all behind interfaces for driving and evaluating agents end-to-end. Includes a benchmark built on it, and empirical findings on where state-of-the-art LLM agents succeed and fail at complex operational tasks. (from abstract only)
• How: Reproducible environment + fault injection + workload generation + telemetry export + agent interaction/evaluation interfaces — i.e., the missing harness that isolated single-task AIOps benchmarks never had.
• Where applicable: General-purpose and open-ended; the fault taxonomy and environments are cloud/DevOps-flavored rather than network-device-specific, so network ops teams would need to add their own scenarios. The capability/limitation findings for current agents are the immediately useful part.