arXiv:2606.29193v1 Announce Type: cross Abstract: LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observability data. However, existing benchmarks remain largely outcome-oriented: they score only the final answer and fail to assess the systematic reasoning process in failure diagnosis. We address this gap by introducing two large-scale datasets (AIOps2025 and RCA100) under a reasoning-process evaluation paradigm that assesses agentic diagnostic capability along three dimensions: Localization (where t
The rapid advancement and deployment of LLMs in enterprise operations necessitate robust evaluation benchmarks for practical applications like microservice failure diagnosis.
Improving the diagnostic capabilities of AI agents in complex microservice environments directly impacts system reliability, operational efficiency, and the broader adoption of autonomous operations.
This development moves beyond simple outcome-based AI evaluation to a more nuanced assessment of the reasoning process, fostering more reliable and systematically sound AI agent development.
- · AI Agent developers
- · Cloud service providers
- · Large enterprises with microservice architectures
- · AIOps platform vendors
- · Inefficient manual IT operations
- · AI agent developers producing opaque, outcome-only diagnostic tools
Enterprise IT operations become more automated and resilient due to improved AI agent diagnostic capabilities.
This improved reliability accelerates the 'AgentOps' paradigm, expanding AI's role in critical infrastructure management.
The success of these benchmarks could inspire similar process-oriented evaluation standards across other high-stakes AI applications, leading to a new era of verifiable AI reasoning.
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Read at arXiv cs.AI