Operations | Monitoring | ITSM | DevOps | Cloud

Supporting every hospital chart, scan, and bedside alert is a web of digital systems—EHRs, lab interfaces, clinical apps, networks, and connected devices—all working in sync or struggling to. When something slips, say, an Epic interface queue backs up and lab results don’t reach the attending physician on time, the consequences aren’t theoretical. That delay might mean a sepsis alert gets missed. A treatment window closes. A patient’s outcome changes.

An operations team at one of the Asia-Pacific’s largest managed service providers (MSPs) was drowning in their own success. Years of investment in monitoring tools and automation had created comprehensive visibility—and comprehensive chaos. Engineers opened dashboards each morning to find thousands of alerts waiting, with critical incidents buried somewhere inside. The scale of the problem was overwhelming their capacity to respond effectively.

Twelve months ago, we shipped Edwin AI with a specific hypothesis that AI agents could handle the operational drudgery slowing down ITOps teams. It was a deliberate bet against the cautious consensus that AI should act only as a copilot, limited to offering suggestions. Most AIOps tools still follow that script. They’re stuck surfacing insights and stop short of action. Edwin was built differently. It was designed to make decisions, correlate events, and execute fixes.

There’s a common misconception in IT operations that mastering DevOps, AIOps, or MLOps means you’re “fully modern.” But these aren’t checkpoints on a single journey to automation. DevOps, MLOps, and AIOps solve different problems for different teams—and they operate on different layers of the technology stack. They’re not stages of maturity. They’re parallel areas that sometimes interact, but serve separate needs.

Today’s IT environments span legacy infrastructure, multiple cloud platforms, and edge systems—each producing fragmented data, inconsistent signals, and hidden points of failure. This scale brings opportunity, but also operational strain: fragmented visibility, overwhelming alert noise, and slower time to resolution. With good reason, public and private sector organizations alike are moving beyond basic visibility, demanding hybrid observability that’s context-aware and action-oriented.

Most internal AI projects for IT operations next exit pilot. Budgets stretch, priorities shift, key hires fall through, and what started as a strategic initiative turns into a maintenance burden—or worse, shelfware. Not because the teams lacked vision. But because building a production-grade AI agent is an open-ended commitment. It’s not just model tuning or pipeline orchestration. It’s everything: architecture, integrations, testing frameworks, feedback loops, governance, compliance.

Azure environments are growing fast, and so are the challenges of monitoring them at scale. In this blog, part of our Azure Monitoring series, we look at how real ITOps and CloudOps teams are moving beyond Azure Monitor to achieve hybrid visibility, faster troubleshooting, and better business outcomes. These real-life customer stories show what’s possible when observability becomes operational. Want the full picture? Explore the rest of the series.

Your systems are getting faster. More complex. More distributed. But your tools are still waiting for something to go wrong before they do anything about it. That’s the real limitation of most AIOps platforms. They highlight issues. They suggest next steps. But they stop short of action—leaving your team to connect the dots, chase down context, and manually fix what broke. Agentic AIOps doesn’t wait. It acts.

On May 1st, AWS corrected a long-standing billing bug tied to Elastic Load Balancer (ELB) data transfers between Availability Zones (AZs) and regions. That fix triggered a noticeable increase in charges for many users, especially for those with high traffic volumes or distributed architectures. The problem wasn’t new usage; it was a silent correction to an old error.

When an alert fires, your goal is clear: fix the problem—fast. But traditional troubleshooting rarely makes that easy. You’re immediately thrown into decision mode: All the while, the clock is ticking. The longer you’re stuck guessing what to do next, the longer your downtime drags on, and the more non-value-added engineering time you burn.