Operations | Monitoring | ITSM | DevOps | Cloud

A big tool result does not have to be a big context cost. AURA moves it to disk and hands the model a pointer plus the tools to navigate what is there. A large MCP tool result can consume or overflow an agent's context window, and on a third-party server you do not control how much comes back. Scratchpad breaks the link between how big a tool result is and how much context it costs: the full output goes to disk, and only the slice the model asks for ever enters the window. Errors always pass through inline, so the model can react to them.

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.

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.

"Hey, I built this over the weekend. I want to get it in front of customers." And it always hits architecture, security, and infrastructure. Ross Hendrickson, CTO at Inspectiv, calls that gap the chasm. His team crosses it on Control Plane: AI-written code secured, reviewed, and released in a day. Control Plane combines AWS, GCP, Azure and your own hardware into one virtual cloud shaped to your workloads.

Give one agent every tool and every incident is a question of trust. This one hands each job to a worker that can only reach what that job needs. One AURA configuration defines a coordinator and three specialist workers. Qdrant stores the runbooks, Prometheus measures workload health, and Kubernetes provides inspection and remediation, and each of the three is wired to one worker.

AI agents don't need a server sitting there while they wait for the next prompt. They need infrastructure that shows up, does the job, and disappears. In this Product Highlights conversation, Nicolas Gommenginger, director of product at Upsun with more than four years leading the platform's core functionality, breaks down Upsun's newest primitive: task containers. His take: "What really triggered us to go ahead and implement it is the rise of AI agents, because tasks are something ideal for running them." We get into.