Jitendra Solanki, Anand Todkar, Ziran Min · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28611 · open-access PDF
현대 분산 마이크로 서비스 배포는 물리적으로나 논리적으로 분할된 인프라에 걸쳐 다양한 형식의 관찰성 로그를 엄청난 양으로 생성합니다.캐스케딩 실패 중 뿌리 원인을 분리하는 것은 사이트 안정성 엔지니어 (SRE)에게는 까다로운 작업이지만, 매뉴얼 키워드 필터링과 모니터링 대시보드 간의 분리된 시간적 상관 관계에 의해 방해됩니다.We introduce Semantic Observability, an autonomous multi-agent framework that unifies disparate log streams into a dynamic, queryable Property Graph Database.
Machine translation (Meta M2M100); the English original follows.
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 topologi
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