Operations | Monitoring | ITSM | DevOps | Cloud

Back when I was a network administrator, one of the most frustrating phrases I would hear was: “The application is slow.” Today, after years of working with customers, not much has changed. The technologies are different, the networks are faster, and the applications are increasingly distributed across data centers, cloud platforms, and SaaS providers, but the troubleshooting challenge is often the same. Users know something is slow. Application teams believe the servers are healthy.

A healthcare technology company routes patient appointment notifications through ActiveMQ. A payment processing firm uses ActiveMQ to bridge its order management system to its payment gateway. A SaaS provider includes ActiveMQ in the architecture scope for its annual SOC 2 Type II audit.

Once you've published more than a handful of KPIs, you eventually need a way to summarize them. A total cost. An overall health status. An organization-wide SLA. Something that lets you answer the big questions without opening ten different dashboards. Summarizing those into a handful of KPIs usually feels straightforward. You add things together, average them, or collapse several statuses into one. The dashboard becomes easier to read, and nothing looks obviously wrong.

Most companies buy AI tools for developers and hope the impact shows up somewhere. A faster sprint. Fewer escaped bugs. Something. What they don’t have is a way to actually see it happening, which means adoption becomes a leap of faith instead of a measured bet. That’s the gap Kepler and GitKraken Insights close together, and it’s worth understanding as one story, not two separate product updates.

Someone on your team says "the Internet is slow." Twenty minutes later, IT finds out the Internet was never the problem. Maybe it was a laptop with a full RAM disk. Maybe it was an ISP outage two towns over that had nothing to do with your office. Misdiagnosing slow Internet wastes time. It sends you down the wrong fix path, like rebooting a router when the real issue is sitting on someone's desktop.

Effective incident response depends on having all of the context surrounding what’s happening. You have to understand your systems, services, architecture, and teams deeply enough to correctly interpret whatever alert just fired. Too often, that context doesn’t arrive packaged neatly in one place. Gathering and interpreting context correctly under time pressure is one of the most difficult parts of the job.

When our company pushed everyone to start using AI tools, I thought about what it would mean for my team. As a remote company, we are already challenged by the lack of organic human connection. Every connection is planned and takes effort, and now, AI adds another layer. People now spend part of their day collaborating with a tool rather than with a person, which can take away from the time we spend learning from each other.