agent/papers/items/2026-2607-01788-krca-an-efficient-root-cause-analysis-system-in-hyper-scale-microservice-systems.md

Paper: KRCA: An Efficient Root Cause Analysis System in Hyper-Scale Microservice Systems via Agentic AI

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type: paper title: "KRCA: An Efficient Root Cause Analysis System in Hyper-Scale Microservice Systems via Agentic AI" authors: Jiamin Jiang, Jingfei Feng, Yu Luo, Qingliang Zhang, Yongqian Su, Wenwei Gu, Shenglin Zhang, Tianyu Cui, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.01788 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-02 updated_at: 2026-07-02 status: queued relevance: high topics:

• agent-evaluation
• memory
• multi-agent
• rag
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.SE related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: agentic-ai

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agentic-ai
• inferred topics: agent-evaluation, memory, multi-agent, rag, reasoning, tool-use
• arXiv categories: cs.SE
• collection score: 16

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.01788