Operations | Monitoring | ITSM | DevOps | Cloud

Datadog Bits Agent Builder lets you build AI agents that use your observability data to automate operational tasks. In this walkthrough, see how to build an agent that analyzes monitor and alert activity, identifies patterns, and provides actionable recommendations to improve your monitoring strategy. With Bits Agent Builder, you can give agents access to Datadog data and tools, customize their instructions and models, and run them automatically to continuously analyze and act on your environment.

What if you could counterspell an agent action? GitKraken Desktop 12.4 pulls the whole AI agent workflow into one place, so you stay in the flow. Back in 12.0 we shipped Agent Sessions, where you kick off AI coding agents right inside the context of your repo. GitKraken 12.4 builds on that. What's new in 12.4: This release is not about handing more of your work to agents. It's about seeing everything they do, and deciding what actually changes.

--- Building a CMDB used to mean weeks of manual work before you had anything useful to show for it. In this video, Matt Beran walks through how to build a CMDB in InvGate Asset Management, from collecting configuration items to mapping the relationships between them automatically.

Last year, Lumen shared its AIOps roadmap. This year: two updates. First, how Lumen's thinking on AI-era org design has shifted — including why the instinct to cut junior headcount is a trap, and what sustainable team structures look like instead. Second, what Lumen built as a result: a single AI core (AskGreg) extended into a customer-facing email agent (NORA), a chatbot, and an in-progress voice agent — all leveraging the same modular platform. Live video demos show the system diagnosing network problems from alarm data and driving real-time network decisions. The closing thesis ties it together.

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.