让LLM自动帮你分析oncall事故根因，一条命令找到池大小调整引发的5xx错误。
The biggest gap today in SRE agents is missing domain knowledge. So they cannot give accurate results for oncall incident handling. I just didn't find…
不用盲目接受AI的修复，Osloq帮你定位bug根源并解释原因，让debug更透明可靠。
Osloq — ใช้ AI หาสาเหตุ bug แทน เวลา AI coding tools เสนอจะ "fix bug ให้" — เราได้แต่กด Accept หรือไม่ก็ Reject สองปุ่ม สองทางเลือก แต่เราไม่เคยรู้ว่า…
开源的WAN延迟监控神器，自动抓取多维度网络信号并用LLM诊断根因，适合网络运维和性能调优。
I love smokeping for finding jitter and latency but always wanted something that went deeper and grabbed all the related signals, so I built JustReboo…
用图引导LLM工具，精准定位K8s事故根因，告别场景捷径。
arXiv:2606.08590v1 Announce Type: cross Abstract: Kubernetes incidents are diagnosed reliably only when a root-cause system's reported gains come from…
聚焦AI Agent调试核心，强调追踪决策与工具调用，从失败到根源分析。
When an AI agent fails in production, the first instinct is usually to tweak the prompt and rerun the workflow. That can make the incident harder to u…
arXiv最新论文提出ORCA，一个端到端交互式AI协作者，实现自动化根因分析优化，智能定位故障。
arXiv:2605.27022v1 Announce Type: new Abstract: Causal analysis is a crucial task in many domains, including manufacturing, social science, and medici…
拓扑感知多智能体框架自进化解决微服务根因分析，应对噪声、级联故障和拓扑漂移三大挑战
arXiv:2605.15611v1 Announce Type: new Abstract: Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimoda…
用元学习+贝叶斯推断搞定复杂系统根因分析，无需完整因果图即可高效定位故障。
arXiv:2605.08786v2 Announce Type: replace Abstract: Root cause analysis (RCA) in complex systems is challenging due to error propagation across multip…
LLM-based RCA代理易出错？STAR框架通过阶段分诊与修复提升微服务故障诊断可靠性。
arXiv:2605.15581v1 Announce Type: new Abstract: LLM-based root cause analysis (RCA) agents have recently emerged as a promising paradigm for incident …
用超图本体打破LLM在企业系统中的幻觉与审计困境，HEAR在供应链根因分析上实现94.7%准确率，为多跳、n元推理提供了无需重训的可扩展范式。
arXiv:2605.14259v1 Announce Type: new Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucination…