Semantic Observability：用多智能体图谱定位微服务故障根因
Semantic Observability for Autonomous Systems Diagnostics
SRE 排障神器来了：论文把散落的日志拼成一张图，多智能体自动跑因果分析，LLM 直接给你代码级根因报告。
arXiv 论文提出 Semantic Observability，一个面向分布式微服务的自主多智能体诊断框架。它把异构日志统一建模为 Property Graph Database，将物理拓扑、软件依赖和事件时序表示为带属性的节点与有向边。系统通过有向循环图（DCG）编排多智能体，结合因果图算法与时间回溯来圈定异常的爆炸半径，再由 LLM 推理引擎生成代码级根因分析（RCA）。论文还给出基于 LogHub 和 Train-Ticket 两个基准的开源验证框架，声称可降低 MTTR、压缩 LLM token 消耗并抑制结构性幻觉。
Semantic Observability for Autonomous Systems Diagnostics
Modern distributed microservice deployments generate massive volumes of heterogeneous, multi-format observability logs across physically and logically partitioned infrastructure. Isolating root causes during cascading failures remains an arduous task for Site Reliability Engineers (SREs), bottlenecked by manual keyword filtering and disjointed temporal correlation across monitoring dashboards. We introduce Semantic Observability, an autonomous multi-agent framework that unifies disparate log streams into a dynamic, queryable Property Graph Database. By modeling physical infrastructure topologies, logical software dependencies, and chronological event sequences as attributed nodes and directed relations, it bridges the semantic gap inherent in flat, unstructured log repositories. Building on this unified substrate, we deploy a stateful multi-agent system orchestrated through Directed Cyclic Graphs (DCGs). The system autonomously navigates the topology, applying causal graph algorithms and temporal back-traversal to isolate the precise blast radius of anomalies. The pruned subgraph is then interpreted by a Large Language Model (LLM) reasoning engine to generate contextualized, code-level Root Cause Analyses (RCA) and actionable remediations. This paper establishes the foundational system architecture, formal relational ontology, and multi-agent control loops of Semantic Observability. We show conceptually how the framework eliminates ambient telemetry noise, reduces Mean Time to Resolution (MTTR), compresses LLM token consumption, and prevents structural hallucinations. We also specify an open-source validation framework on standard benchmarks (LogHub and Train-Ticket), laying the groundwork for a forthcoming companion paper on exhaustive quantitative evaluation and production benchmarking.
