Paper proposes Semantic Observability for automated root-cause analysis
Original title: Semantic Observability for Autonomous Systems Diagnostics
The paper is architectural and defers measured results on LogHub and Train-Ticket to a companion study.
Researchers describe Semantic Observability, a multi-agent framework that turns heterogeneous microservice logs into a queryable property graph of infrastructure, dependencies, and events. Agents use causal algorithms and temporal back-traversal to bound an anomaly’s blast radius. An LLM then produces code-level root-cause analysis and remediations. Claimed gains in noise, MTTR, tokens, and hallucinations are conceptual, not measured here.
Unconfirmed: Performance claims are conceptual; the authors defer quantitative evaluation to a forthcoming paper.
- Logs become attributed nodes and directed relations in a property graph.
- Agent control is orchestrated through directed cyclic graphs.
- Benefits are argued conceptually rather than measured in this paper.
- Planned validation uses the LogHub and Train-Ticket benchmarks.
From the original article
1 min read at arxiv.orgModern 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.
By Jitendra Solanki · arxiv.org
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