Semantic observability framework unifies logs for faster system failure analysis
Semantic Observability for Autonomous Systems Diagnostics
Artificial Intelligence
Summary
Modern computer systems create huge amounts of messy logs that are hard to understand when things go wrong. The authors designed a system that turns all these confusing logs into a clear map showing how parts of the system connect and what happened over time. This system uses smart software agents and AI to find the exact cause of failures and suggest fixes automatically. Their approach helps engineers find problems faster by cutting through unnecessary data and explaining issues clearly.
What this means in practice
- •For site reliability engineers: Diagnose complex cascading failures faster by integrating diverse logs into a navigable semantic graph and automating root cause extraction.
- •For cloud infrastructure operators: Improve incident response by automatically correlating infrastructure topology with software events to isolate failure impacts accurately.
Tested on one dataset.
Authors
Jitendra Solanki, Anand Todkar, Ziran Min
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.