Origins and history of AIOps
Domain 1 - Origins and history of AIOps
This domain tests whether you can trace how IT operations analytics evolved into AIOps, who named it, and why the shift happened. Expect questions on definitions, chronology, and the forces that made manual correlation untenable.
Three eras of operations analytics
- Siloed monitoring (1990s–2000s): separate tools for network, server, application, and database domains. Each used static thresholds; an operator correlated alerts by hand.
- ITOA (late 2000s): IT Operations Analytics aggregated logs, metrics, and events into a data store and applied search, dashboards, and statistical analysis. It improved visibility but was largely descriptive.
- AIOps (2016 onward): Gartner introduced the term in 2016, replacing its earlier label “Algorithmic IT Operations.” Gartner's definition combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.
The trigger was not a single invention. Cloud elasticity, microservices, containers, and API-driven infrastructure multiplied the volume, velocity, and variety of telemetry. Static thresholds generated more alerts than humans could triage. AIOps emerged as a way to learn normal behavior per service and correlate signals across domains.
Worked example
An enterprise in 2011 ran Nagios checks per host with CPU thresholds. A cascading failure produced 600 alerts; engineers found the root cause after 90 minutes. In 2019 the same estate emitted 40,000 events per hour. An AIOps platform clustered related events, suppressed duplicates, and flagged one anomalous latency pattern on a shared database. The on-call engineer received eight correlated alerts and identified the database as causal in 12 minutes. The reasoning: the platform did not “know” the database was at fault; it learned the baseline, grouped statistically similar events, and ranked the earliest deviation. Human judgment confirmed causality.
Traps that cost marks
- Treating AIOps as a synonym for “AI” or for generative AI; the term predates modern LLMs by years.
- Claiming AIOps began with cloud computing; its roots are in ITOA and APM.
- Assuming AIOps eliminates operators. It automates correlation and can trigger remediation, but accountability stays human.
- Confusing ITOA with AIOps: ITOA describes and visualizes; AIOps adds ML-driven prediction and automation.
- Forgetting Gartner's 2016 naming and the earlier “Algorithmic IT Operations” label.