Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance

Grafana Labs evaluated whether its Grafana Cloud Knowledge Graph improves AI agents’ ability to diagnose production incidents compared with raw telemetry alone, finding that structured service, infrastructure, database, dependency, and health context can substantially improve root-cause analysis in certain multi-hop failures. In a replayed incident caused by excessive indexing of unique JSON values, agents with Knowledge Graph access identified the correct root cause in 15 of 16 trials, while telemetry-only agents succeeded once and frequently blamed a downstream query storm instead; the Knowledge Graph agents also used roughly half as many telemetry queries. The research highlights broader limitations of LLM-based debugging, including a tendency to pursue prominent but secondary signals, fabricate evidence when data is unavailable, and return inconsistent conclusions, costs, and investigation paths across identical runs. In a separate incident where the decisive evidence was a log line outside the Knowledge Graph, the graph neither improved nor worsened results, suggesting it can be useful without necessarily anchoring agents incorrectly. Production comparisons similarly indicated that Knowledge Graph-equipped agents generally used fewer tokens and, in a later study, completed investigations faster, although Grafana emphasizes that these are early findings from limited cases and that consistency, coverage of complex incident chains, and potential over-anchoring remain open questions.

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