LN: Chen et al. (2025) — AIOpsLab: A Holistic Framework to Evaluate AI Agents for Autonomous Clouds

Bibliographic Reference

Citation: Chen, Y., Shetty, M., Somashekar, G., et al. (2025). AIOpsLab: A holistic framework to evaluate AI agents for enabling autonomous clouds. arXiv:2501.06706. MLSys 2025. https://arxiv.org/abs/2501.06706

Important Note

Overview

The bibliography lists “Zhang, Y., & Cui, L.” as authors (incorrect). The verified first author is Yinfang Chen (Microsoft). The arXiv ID is 2501.06706, not the URL cited.


Pass 1 — Bird’s Eye View (5 Cs)

CAssessment
CategoryBenchmark framework + empirical evaluation
ContextMicrosoft Research’s framework for AIOps agent evaluation
CorrectnessComprehensive benchmark with 30+ tasks. Multi-agent evaluation. Real cloud scenarios.
Contributions(1) Holistic evaluation framework covering detection, diagnosis, and mitigation; (2) Orchestration layer for reproducible AIOps agent testing; (3) Baseline evaluation of frontier LLMs on cloud operations; (4) Open-source framework
ClarityExcellent. Clear evaluation protocols.

Relevance: ⭐⭐⭐⭐

AIOpsLab is the benchmark for AIOps agents that PUMA parallels for PM agents. The design principle (reproducible, standardised evaluation of LLM agents for operational tasks) is identical.


Pass 2 — Key Points

AIOpsLab’s design principle: “holistic” means covering the full incident lifecycle (detect → diagnose → mitigate). PUMA’s design is similarly holistic across PM lifecycle (triage → estimate → prioritise → plan).

Key design element for PUMA: AIOpsLab uses a “world model” to generate reproducible incident scenarios. PUMA could adopt a similar approach: generate reproducible PM scenarios (stratified Jira SR samples) for controlled comparison.


PUMA Integration

  • Section 2 (SLR): AIOpsLab as the AIOps counterpart to PUMA in the benchmark landscape
  • Architecture design: AIOpsLab’s orchestration layer is analogous to PUMA’s evaluation pipeline
  • Cite alongside LN-Gao-2024-AgentScope as benchmark design references

MOCs