Observability converts high-volume telemetry such as logs, traces, and metrics into human-readable narratives but often lacks structural system knowledge necessary for root cause isolation in distributed architectures. LLMs and agentic AI frequently hallucinate plausible yet incorrect explanations, confuse symptoms with causes, and ignore event ordering, producing misdiagnosis and incomplete remediation. Causal reasoning explicitly models service and resource dependencies, incorporates event temporality, and enables inference under partial or noisy observations to improve root cause identification. Causal graphs and Bayesian inference support counterfactual and probabilistic evaluation of remediation options and their likely impacts.
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. Causal reasoning models service and resource dependencies explicitly, accounts for event temporality, and supports inference under partial or noisy observations, enabling more accurate root cause identification. Causal graphs and Bayesian inference allow for counterfactual and probabilistic reasoning, which lets engineers evaluate remediation options and their likely impact before taking action.
The central goal of IT operations and site reliability engineering (SRE) is to maintain the availability, reliability, and performance of services while enabling safe and rapid delivery of changes. Achieving this requires a deep understanding of how systems behave during incidents and under operational stress. Observability platforms provide the foundation for this understanding by exposing telemetry data (logs, metrics, traces) that support anomaly detection, performance analysis, and root cause investigations.
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