Operations | Monitoring | ITSM | DevOps | Cloud

AI in network operations has moved past the demo. In some shops it's already cutting detection time and clearing the alert noise that used to bury the engineering team. In others, a promising rollout has quietly stalled the moment it met production. That difference between the two rarely comes down to the model, it comes down to how the AI gets integrated into the network and most importantly, the team around it.

Time-series view of how consistently a reservation has been utilized and which resources are consuming it. Makes underutilization or coverage gaps visible at a glance instead of requiring manual investigation.

Alerts when a schedule has been disabled for longer than a set threshold (e.g., 7+ days). Solves the common failure mode where a team disables a shutdown schedule for maintenance and forgets to turn it back on, quietly costing money.

A dynamic alternative to static schedules. Define filters (subscription, resource group, tag) once, and matching VMs are automatically picked up, including new ones added later. Supports both metric-based triggers (e.g., CPU under 5% after 8pm) and time-based power on/off.

Sometimes there's a vulnerability in a package you can't remove. Legacy dependencies don't always come with an easy fix. The one thing you don't do is bury it in the backlog with no dates and no reasoning attached. This video covers what a defensible response actually looks like: Your ability to show your work on unfixed vulnerabilities is more important than simply identifying them.

Many times, it feels like you're maintaining multiple versions of the same dashboard (with a slight modification), *OR* simply spending more time writing queries rather than actually looking at the actual data? In this Campfire community call, we're taking a deep dive into two things that are reshaping how Grafana dashboards get built: Dynamic Dashboards and the Grafana AI Assistant and showing you how to combine them to go from a blank canvas to a reusable, production-ready dashboard in minutes.

You get an answer from the agent and no way to check how it got there. The route it took is recorded, and so is the reason it gave for taking it. AURA emits OpenTelemetry spans, and the Docker quickstart wires them straight into Phoenix. Four services come up together: AURA Web Server as the persistent agent harness, LibreChat as a browser interface for chatting with the agent, Phoenix to receive the spans, and MongoDB to store stateful data for LibreChat. The Compose file arrives pre-configured to point AURA at Phoenix and to enable content recording for the local demo.

The same agent that reads a Kubernetes cluster reads a Windows machine. One MCP server, one config entry, and the questions are the ones you already ask.

End-to-end testing should not slow your delivery pipeline down. But for many teams, Playwright test suites still live in disconnected jobs, produce noisy failures, and make it hard to tell whether a failed test is a real regression, a flaky test, or just another false positive. In this walkthrough, Shibam Dhar, DevRel Engineer at Harness, shows how Harness AI Test Automation helps teams run, analyse, and trigger Playwright tests directly from their software delivery workflows.

Solution Architect Jeff Schnitter walks through arrays of primitives in Workflow User Inputs, turning an object you have to transform into an array you can pass straight through. What it does.