Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

If you want to get started with Icinga but don’t have a data center lying around, no worries. Icinga is a lightweight monitoring tool that works for both large infrastructures and small home labs. When I first explored Icinga during my first year as an apprentice, it was also my first real contact with monitoring tools. After completing the Icinga Fundamentals training, I wanted to experiment with hosts and services, but what should I monitor?

The financial services industry is continuing its acceleration. AI is rolling out across the enterprise, and compliance expectations continue to diverge based on jurisdiction. It’s an unprecedented technology shift to say the least, and the pressure is being felt throughout the IT industry to catch up and remain resilient. More important now than ever before, learn how Auvik provides financial institutions with full network visibility and monitoring that catches problems before they become outages.

This report presents incident data from major cloud providers for December 2025, covering AWS, Azure DevOps, DigitalOcean, Fly.io, Heroku, Linode, Netlify, Railway, Render, and Vercel. The data includes both officially reported incidents from provider status pages and unconfirmed incidents detected by IsDown's monitoring system.

We’ve added a new enhancement to the StatusGator API that makes it faster and easier to identify service issues in real time: status-based filtering for monitors and components.

Managing 50 client networks means 50 separate monitoring instances, 50 sets of credentials, and 50 different dashboards to check daily. Every morning starts with logging into multiple platforms, context switching between interfaces, and hoping you didn't miss a critical alert buried somewhere. Traditional network monitoring tools weren't exactly built for MSPs. They're designed for single organizations monitoring their own infrastructure, which means every client you onboard adds exponential complexity.