Operations | Monitoring | ITSM | DevOps | Cloud

For any company to achieve a successful with automation project, it is more than just identifying the right automation tooling. This article discusses key areas that should be considered before embarking on an automation journey.

Artificial Intelligence (AI) tools have the potential to revolutionize IT infrastructure and operations (ITOps) by automating routine tasks, enhancing system reliability, and improving efficiency. However, the term “Artificial Intelligence” can sometimes be misleading in this context. A more accurate description might be “Automated Intelligence” because these tools often automate predefined tasks and processes rather than exhibit true cognitive intelligence.

Computing environments are more dynamic, distributed, and complex than ever. Observability tools help collect, monitor, and interpret the data they generate — like logs, metrics, and traces — to give IT teams and leaders real-time insights that can help detect issues, troubleshoot solutions, and improve the reliability of their IT systems. But observability tools can’t do their job without a strong infrastructure foundation and the right tools to manage it.

Did you know you can find variables by name and tag? Like the ability to find currently defined classes (as described in Feature Friday #13: classesmatching()) that match a name or tag, you can find variables by name and tag. It’s a nifty capability. variablesmatching() returns a list of variable names that match the name and tag criteria.1 variablesmatching_as_data() returns a data container of the matching variables along with their values2.

IT service outages, while inconvenient, also carry widespread ramifications that affect productivity, revenue streams, business reputation, and customer satisfaction. These outages can also drive burnout and increased human error for the IT operations (ITOps) teams tasked with managing the stress that comes with urgent issues and escalations.