Operations | Monitoring | ITSM | DevOps | Cloud

If you have written any kind of end-to-end tests or UI tests you probably know that the greatest headache to deal with is test flakiness due to browser actions not behaving in the way that you expect them to behave. This flakiness can be a major bottleneck especially in CI/CD pipelines due to constant failures.

Traditional alerts are simple by design: if a metric crosses a threshold, fire an alert. While that simplicity makes alerts easy to configure, it also leads to alert noise, because single metrics rarely tell the full story and often trigger during non-actionable conditions. Hosted Graphite Composite Alerts solve this by allowing you to combine multiple alert conditions using logical expressions like AND (&&) and OR (||).

AI automation in ITOps fails because execution loses decision context, and context graphs turn incident history into durable execution memory that systems can actually reuse. AI automation for ITOps fails because it remembers what it did, but not why. Fixing an issue depends on what was tried last time, what failed, what worked, which exceptions were approved, and under what conditions. That information rarely lives in the system.

A VP of Engineering (from a company I’m not allowed to name) told me recently: "You helped us find and fix real user-facing issues. Now we need to convince our CTO why that matters more than the standard SLO’s and systems." Here's the thing: your CTO is not wrong in measuring the systems and basic uptime. That’s the baseline though. They’re all trying to watch everything, but they’re seeing nothing as it relates to users.

Most enterprises believe that once an asset is purchased and recorded, its value is automatically realized. In reality, the opposite is often true. Poor asset performance silently erodes budgets, reduces operational agility, and creates long term inefficiencies that remain hidden for years. Studies consistently show that nearly 30 percent of assets owned by large organizations remain unused or severely underutilized.

Your organization has invested heavily in monitoring tools for application performance, infrastructure monitoring tools for servers and databases, log monitoring tools, network monitoring tools, and third-party monitoring tools for specific services. But the actual problem is your IT team is drowning in that data. A single production issue generates 30+ alerts across applications, databases, servers, and monitoring tools, creating an alert flood that buries the actual problem.

In the previous post, we explored how AI accelerates delivery and compresses the time between change and user impact. As velocity increases, knowing that something has gone wrong before users do becomes a critical capability. But detection is only the beginning. Once alerts fire and dashboards light up, humans still have to interpret what’s happening, make decisions under pressure, and act.