Artificial Intelligence for IT Operations, the use of ML and statistical models to detect anomalies, correlate alerts, and surface root cause across observability data.
AIOps (Artificial Intelligence for IT Operations) is a category of software that applies machine learning and statistical analysis to IT operations data, logs, metrics, traces, events, to reduce alert noise, detect anomalies that static thresholds miss, and correlate symptoms across services into a likely root cause. AIOps platforms surface findings to a human operator who decides what to do about them. Modern Agentic SRE extends AIOps by also closing the action loop autonomously within a policy envelope.
A team running 200 services on Kubernetes sees thousands of alerts per day from static-threshold tools. 95% are noise. AIOps cuts that to a manageable signal an on-call engineer can act on in minutes instead of hours, and surfaces correlations across services that humans simply cannot inspect at speed.
See the part of the platform that handles aiops in production.