Modern distributed microservice deployments emit massive, heterogeneous, multi‑format observability logs across physically and logically partitioned infrastructure. Site Reliability Engineers (SREs) still rely on manual keyword filtering and disjoint temporal correlation across dashboards, making root‑cause isolation during cascading failures arduous. We introduce Semantic Observability, an autonomous multi‑agent framework that unifies disparate log streams into a queryable Property Graph Database.
In the graph, physical topologies, software dependencies, and chronological event sequences are modeled as attributed nodes and directed edges, bridging the semantic gap of flat, unstructured log stores. On this unified substrate we deploy a stateful multi‑agent system orchestrated by Directed Cyclic Graphs (DCGs). The agents autonomously traverse the topology, applying causal‑graph algorithms and temporal back‑traversal to pinpoint the exact blast radius of anomalies.
The pruned subgraph is then handed to a Large Language Model (LLM) reasoning engine, which generates contextualized, code‑level Root Cause Analyses (RCA) and actionable remediation steps. This paper details the system architecture, formal relational ontology, and multi‑agent control loops, and conceptually shows how the framework eliminates telemetry noise, reduces Mean Time to Resolution (MTTR), compresses LLM token consumption, and prevents structural hallucinations. An open‑source validation suite on the LogHub and Train‑Ticket benchmarks is provided, laying groundwork for forthcoming quantitative evaluation and production benchmarking.
Review: Semantic Observability unifies log semantics via a property graph, enabling automated causal tracing and LLM‑driven root‑cause reporting, offering a scalable and efficient diagnostic pipeline for microservice operations.