Operations | Monitoring | ITSM | DevOps | Cloud

Edwin AI and Red Hat Ansible Automation Platform help ITOps teams move from correlated alerts and root cause analysis to governed, auditable remediation. When an outage starts, the first alert is only the first artifact. The harder work follows: grouping related signals, separating symptoms from cause, identifying the affected service, and deciding whether the next action is safe to run.

This is the eighth blog in our Azure Monitoring series, where we look at a challenge many organizations face as Azure and multi-cloud environments expand: monitoring tool sprawl. What starts as a few monitoring solutions for different needs can turn into disconnected dashboards, duplicate alerts, and fragmented visibility.

Self-healing ITOps helps restore services faster by combining AI-driven analysis, automation, and recovery validation. Organizations have invested heavily in monitoring, observability, and AIOps. These platforms are effective at identifying issues, but incident resolution is often still a manual process. Engineers still need to investigate alerts, determine the appropriate remediation, and verify that services have recovered.

Network performance metrics are real-time measurements of how data moves across your network, from speed and capacity to delay, loss, and reliability. Network performance metrics are the diagnostic layer between your infrastructure and your users. They explain why things are slow, dropped, or unreachable by capturing everything from how fast packets travel and how much bandwidth is in use to how often data gets dropped or delayed.

Most network monitoring tools alert you that a device is down. The best ones help you determine whether the problem is your WAN circuit, your ISP, or your SaaS provider before your users file a ticket. Traditional network monitoring tools were built for static networks. You poll devices, check interface counters, and still can’t explain why users are complaining about latency.