Newest papers

Semantic Observability for Autonomous Systems Diagnostics

Jitendra Solanki, Anand Todkar, Ziran Min · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28611 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

现存的分布式重组式系统验证系统的正式部署在物理和逻辑上预分布的基础设施上生成大量异质多格式可观察记录的框架。在分散式失败中孤立根源的LLCA仍然是网站可靠性图表数据库(SREs)的艰难任务,通过手动关键词滤波和监控仪表板上的分离式时序列进行分解。我们引入了SemanticRadio Observability,一个自动化的多代理框架,将分布式日志流统一为一个动态的,可查询的属性图表数据库。通过模拟物理基础设施的分布式图表,逻辑软件依赖和时间事件序列作为分配的节点和导向关系,它将分离性在平板,无结构化

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

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Topics: Stateful firewall, Observability, Computer science, Security token, Event (particle physics), Root cause

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