Operations | Monitoring | ITSM | DevOps | Cloud

Training a model in a notebook is easy. What breaks teams is the step after, serving it reliably without haemorrhaging cloud budget or burying your SREs in YAML. The common trap: picking a platform that handles the model but not the surrounding stack. An AI deployment platform should orchestrate the full application graph (inference endpoints, vector databases, caching layers, and frontends) inside a single VPC, with GPU autoscaling that doesn't require a dedicated platform engineer to babysit.

An alert in the middle of the night warns of a potential business failure. Manual incident response becomes more complex due to the overwhelming data from distributed and dynamic digital services. With an SRE agent, your engineering team can cut through alert clutter. They can sort through various signals quicker, decreasing burnout and achieving faster, more affordable resolutions. Operational resilience will see its next evolution with Agentic AI.

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.

Context engineering is the practice of managing the information an AI model sees (documents, tool outputs, memory, and structured metadata about the systems it reasons over) so it can make accurate decisions inside a real engineering organization. Most engineering teams have access to the same AI coding agents: Claude, GPT, Gemini, the major variants everyone is shipping. The model is no longer the differentiator.

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.