Large language models (LLMs) in observability excel at turning high-volume telemetry such as logs, traces, and metrics into concise human-readable narratives, but they lack structural system knowledge and struggle to isolate root causes in complex distributed architectures. Current LLM and agentic AI approaches are prone to hallucinating plausible but incorrect explanations, mistaking symptoms for causes, and ignoring event ordering, which leads to misdiagnosis and incomplete remediation.
