Operations | Monitoring | ITSM | DevOps | Cloud

Everyone wants an AI agent factory in 2026. Autonomous agents fixing bugs and shipping features while you sleep. I’ve been building toward that myself. But the error rates don’t support the fantasy. The best AI coding agents in the world fix about 50% of real bugs on SWE-bench verified. Half the time they fail. And AI-generated code produces 1.7x more issues than human-written code.

For network administrators, managing DNS has traditionally meant juggling zones and records across separate server interfaces, manually tracking changes, and responding to resolution failures after they’ve already caused disruption. We’re excited to introduce Microsoft DNS management in ManageEngine OpUtils, bringing DNS zone and record administration directly into the same console you already use for IP address management (IPAM).

In May 2026, StatusGator detected 854 Early Warning Signals across SaaS, cloud, developer, and infrastructure services. Of those incidents, 695 were never acknowledged by providers, while 159 were eventually confirmed on official status pages. Throughout the month, StatusGator’s Early Warning Signals continued to surface emerging outages before many providers published updates, giving teams valuable time to investigate and respond.

Most IT teams already know when something breaks. The real problem is making sure the right person responds fast enough. A server goes down. A customer-facing application crashes. A security alert triggers after hours. The monitoring system sends the notification. But nobody responds. The alert gets buried in Slack. The on-call engineer misses the push notification. The wrong person is scheduled. Everyone assumes somebody else is handling it. That is how small incidents become expensive outages.

Software moves from code to production more quickly today, but it is still difficult to tell whether delivery is actually improving or just becoming more active. Most teams rely on dashboards filled with metrics like deployments, uptime, failures, and tickets. The numbers are available, but the meaning behind them is often unclear. DevOps metrics become useful only when grouped into clear categories: DORA metrics cover only delivery speed and stability, which is just part of the picture.

ServiceNow's internal IT operations now resolve more than 90% of employee requests through autonomous agents. The platform that demonstrated this in Las Vegas earlier this month is the same platform sitting in your environment right now. So why isn't your operation running the same way? This is the question every CIO should have walked out of Knowledge 2026 with.

Some support cases are straightforward. Others lead deep into legacy code, where a single logic bug can quietly turn a routine command into a major performance problem. This series looks at how Canonical Support and Sustaining Engineering work together to investigate, patch, and upstream difficult issues that standard troubleshooting alone cannot solve.

This blog helps IT leaders use the Forrester Consulting TEI study as a practical framework for evaluating Edwin AI in their own environments. A Total Economic Impact study is useful for one, critical reason: it takes a broad technology claim and turns it into a financial and operational framework. That matters in AI for IT operations because the market is crowded with claims. Every platform says it reduces noise. Every platform says it improves efficiency. Every platform says it helps teams move faster.