Operations | Monitoring | ITSM | DevOps | Cloud

When an incident occurs, an L2/3 engineer or SRE can spend 20–30 minutes investigating across alert consoles, combing through change records, and pinging teams on Slack or Microsoft Teams. When you multiply that time spent across thousands of incidents per year by the cost of an IT outage at $14,056 per minute, the cost is staggering. Enterprises can’t afford to waste time searching across disparate tools.

When you launch a new service in production, you’re working with a lot of unknowns. You don’t yet know how it behaves under real traffic or which incidents are worth waking someone up for. That makes alerting for a new service a little different from what you’re used to with an established one. The goal in the early days isn’t to get everything perfectly configured. It’s to learn enough about the service to get your alerting right.

April saw widespread disruptions across SaaS platforms, developer tools, and cloud services, with login failures, pipeline issues, and general service outages among the most common problems. StatusGator’s Early Warning Signals consistently identified these incidents ahead of official provider updates. In several cases, the lead time was significant. Bitbucket pipeline failures were detected 1 hour 17 minutes before acknowledgment, while Claude performance issues surfaced 59 minutes early.

Recurring incidents are a symptom of a broken process. Your teams are working hard to get services back online, but constantly battling the same problems is frustrating and not a sustainable approach. What’s reflected here is not a failure in engineering abilities, but a deficiency in the learning that should follow an incident. When incident analysis focuses on finding a single person or team to blame, it creates a culture of fear.

Not every incident alert needs the same kind of response. One incident may need to wake someone up right away. Another may simply need to be picked up when the team starts work in the morning. Without a clear way to tell them apart, every incident feels equally urgent. That usually adds noise and makes incident response decisions harder than they need to be. This is where two questions help: In this guide, we’ll discuss what those questions mean and the four combinations that follow.

An alert in the middle of the night warns of a potential business failure. Manual incident response becomes more complex due to the overwhelming data from distributed and dynamic digital services. With an SRE agent, your engineering team can cut through alert clutter. They can sort through various signals quicker, decreasing burnout and achieving faster, more affordable resolutions. Operational resilience will see its next evolution with Agentic AI.