Operations | Monitoring | ITSM | DevOps | Cloud

When should you reach for Grafana MCP? It’s one of the two “hands” in Grafana’s AI toolkit — and the easy one at that. MCP lets you bring Grafana into the tools you already use, like ChatGPT, Claude, or Cursor, without changing your workflow. No terminal? MCP. Want to stick with your favorite AI tool? MCP. Want easy tool discovery out of the box? MCP. Here’s where it fits, and when to use it — explained by Nicole van der Hoeven.

Platform teams spend 6–12 months building the thing Konstruct gives you on day one. Secrets, certs, DNS, cluster provisioning, then doing it all again for the next team, the next cloud. Konstruct is one control plane that scales from a solo builder to a hundred-person platform org, without you ever re-architecting. Climb the responsibility ladder on your terms, hosted app, hosted cluster, hosted control plane, fully self-hosted. You decide where the line sits.

AI agents are moving fast. For enterprises, the bigger question is how to make them useful, trusted, and safe enough to take action. In this episode of Agents of IT, Zach Austin and Ian Coppock unpack what it takes to move from experimenting with AI to operationalizing agentic AI across the enterprise. They explore why organizations are racing to deploy AI before defining the problems they want to solve, why governance and scoped responsibilities are critical, and why the real value of AI comes from fixing and orchestrating the workflows underneath it.

Join Terry Chevalier, Founder and Managing Director, Sunstone Associates to explore why rural markets are emerging as AI hotspots, what Neocloud developers need, and how broadband providers can capitalize on this growing opportunity.

Six engineers, two QA, a product owner and a Scrum Master used to be a normal squad. With today's tooling, that's bloat. Teams are being rebuilt around three people. Not because the work got smaller — because agentic tooling absorbed the parts that needed all those hands. The engineer's job shifts from writing the code to directing the agents that write it, and owning the outcome that comes out the other end.

With SIGNL4, planned and unplanned absences can be managed right from the mobile app.

Learn more: speedscale.com.

The model, the serving layer, and the agent asking questions all sit on local hardware. The first thing it debugs is the AI stack it is running on. No external inference provider in the path.

There’s a new class of cloud provider on the block in AI infrastructure, built specifically for GPU access instead of trying to do everything. We’ve got something in the works for exactly this kind of connectivity too Follow for weekly cloud watching with @digital.byte_ comment “cloud” to connect with our experts.

Learn how to implement end to end observability and monitoring for Grok Build, xAI's terminal coding agent, using OpenTelemetry and SigNoz. In this video, we walk through turning on Grok Build's native OpenTelemetry exporter, collecting metrics and structured session events, and visualizing everything in SigNoz to gain real time visibility into token usage, sessions and turns, tool calls and their outcomes, error categories, and startup latency. Grok Build ships its own exporter, so instrumenting it is a matter of configuration, with no library to install and no collector to run.