Operations | Monitoring | ITSM | DevOps | Cloud

When an unexpected alert fires these days, most engineers' first move is to ask their AI assistant for help.You ask why your checkout service is slow and the assistant gets to work, but it can't get any meaningful insights—at least not quickly—without the proper guidance. So, the next thing you know you're sharing deals about your existing data sources, the services you have running, how they connect, which labels and metrics matter, and on and on.

I recently gave a talk at Experts Live India 2026 about SquaredUp, and even before getting into the demo, there was one question I knew I had to address: Is the dashboard era over? It's something we're all hearing more. "Just ask AI." "Agentic AI will build your dashboards automatically." "Why bother with static views when a chatbot can answer anything?" It's a fair question. Answering it requires a clear understanding of what a dashboard represents.

Across the IT industry, AI is being positioned as the next evolution of operations. But for many IT teams, AI still feels disconnected from the tools they rely on every day. Dashboards get smarter. Reports get faster. But workflows stay the same. Stuck in vendor silos or a CLI, IT teams have been looking for ways to bolt AI into workflows, but what often comes out is a Frankenstein-like web of APIs and MCP hosts. AI is meant to make life easier for IT teams – not make it more difficult.

Modern IT operations aren’t short on tools. Monitoring tools. Ticketing systems. Alerting platforms. Documentation repositories. Dashboards. Scripts. Runbooks. And yet, when something breaks, the workflow still looks strangely familiar: Somewhere along the way you’re asking yourself: Is the problem even here? This is the everyday friction of IT operations. Not the big outages. Its the constant small mysteries that take far longer to solve than they should.

A Playwright Check fails at 2 am. The login flow is broken. Until today, that alert triggered a human to get up, open the Checkly dashboard, copy Rocky AI root cause analysis (RCA), and then tell an agent to get to work. There were two AI agents, one incident, and no way for them to talk to each other. The extended checkly checks and new checkly rca CLI commands close that gap. Your coding agent can now pull Rocky AI's analysis into its ongoing work, read the diagnosis, and go fix the code.