Semantic Observability for Autonomous Systems Diagnostics

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, bottlenecked by manual keyword filtering and disjointed temporal correlation across monitoring dashboards. This article introduces 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.

Unifying Heterogeneous Log Streams into an Attributed Property Graph

The framework captures physical infrastructure topologies, logical software dependencies, and chronological event sequences as attributed nodes and directed relations within a Property Graph Database. This representation transforms flat, unstructured log entries into a queryable substrate where each node carries metadata about its origin, type, and temporal position. Edges encode causal propagation patterns, dependency directions, and timing constraints. The graph structure enables traversals that would be impractical when scanning raw log files, such as back-tracing a failure across service boundaries or identifying all downstream effects of a single abnormal event.

Stateful Multi-Agent Orchestration via Directed Cyclic Graphs

Building on the unified substrate, a stateful multi-agent system is deployed, orchestrated through Directed Cyclic Graphs. These graphs define the control flow among agents, allowing cycles that represent repeated diagnostic loops or feedback between stages. Each agent operates with persistent state, retaining intermediate findings, hypothesis scores, and context about recently examined subgraphs. The orchestration engine routes messages between agents according to the DCG topology, ensuring that no agent processes the same traversal branch twice without new evidence. This design prevents redundant work and enables incremental refinement of the root cause hypothesis as additional log segments are examined.

Causal Graph Algorithms and Temporal Back-traversal

The system applies causal graph algorithms to prune the full graph into a minimal subgraph that still contains the anomaly's origin. Temporal back-traversal follows edges reverse‑direction, matching observed symptom timestamps against the direction of causality. By combining edge‑type filtering (e.g., "call", "error‑forward", "config‑change") with time-order constraints, the algorithm discards branches that cannot possibly lead to the observed failure. The result is a focused subgraph that isolates the blast radius—the smallest set of nodes and edges sufficient to explain the anomaly.

LLM‑Driven Root Cause Analysis from the Pruned Subgraph

The pruned subgraph is handed off to a Large Language Model reasoning engine. The LLM reads the attributed nodes and edges, leveraging its parametric knowledge to map observed graph patterns to known failure modes, code paths, and configuration pitfalls. It generates a contextualized, code‑level Root Cause Analysis that includes not just the likely failing component but also concrete remediation steps, such as config adjustments, circuit‑breaker tweaks, or cache invalidation sequences. Because the LLM operates on a already‑pruned graph, its token consumption is compressed relative to ingesting raw logs, and the risk of structural hallucinations is reduced by the graph's hard constraints.

Open‑Source Validation Framework on LogHub and Train-Ticket

The paper specifies an open‑source validation framework that runs on standard benchmarks LogHub and Train‑Ticket. These datasets provide curated collections of real‑world and synthetic log streams with ground‑truth root cause labels. The framework measures whether the multi-agent pipeline correctly identifies the annotated root cause, how much the graph pruning reduces edge count, and the token‑efficiency of the LLM stage. Results from these benchmarks lay the groundwork for a forthcoming companion paper that will present exhaustive quantitative evaluation and production‑grade benchmarking.

By uniting graph‑based substrate construction, DCG‑orchestrated multi-agent traversal, and LLM‑guided RCA generation, Semantic Observability offers a coherent pathway from noisy, distributed logs to actionable root cause answers. The approach eliminates ambient telemetry noise, reduces Mean Time to Resolution, compresses LLM token consumption, and prevents structural hallucinations—all while providing an extensible foundation for future quantitative studies.