Operations | Monitoring | ITSM | DevOps | Cloud

Ivanti Launches Agentic AI on the System of Record You Trust

Investors and enterprises are finally asking the question they'd been avoiding: which software companies will survive the AI revolution, and which will be made obsolete by it? The answer is becoming clear. Companies that serve as the system of record, the authoritative source of truth that AI itself depends on, are essential.

Uptrace MCP Server: Auto-Generate Dashboards with AI in Minutes

Tired of clicking through menus to build observability dashboards? In this video I walk through how to configure the Uptrace MCP (Model Context Protocol) server and connect it to an AI assistant so your dashboards get created automatically from natural-language prompts. You'll learn how to: By the end you'll have a working setup where describing what you want to monitor is enough to get a real, shareable dashboard in Uptrace.

How Observability Powers Autonomous IT in Hybrid Environments

Autonomous IT only works when observability gives it the context to act with confidence. On any given day, a mid-size enterprise generates tens of thousands of alerts across on-prem infrastructure, multiple clouds, SaaS tools, Internet dependencies, and AI workloads. Most of them don’t need a human. A few of them do. Telling the difference, fast enough to matter, is exactly where IT teams are losing ground.

Monitor Databricks with Grafana Cloud for instant visibility into your workloads

If you're running Databricks workloads, you've probably asked yourself these types of questions: How much is this costing me? Why did that job fail last night? Why are my dashboard queries suddenly slow? We've been there, too. Databricks is fantastic for data engineering, ML, and analytics. But once you start running jobs, pipelines, and SQL queries at scale, you need a way to keep tabs on what's happening. That's why we built the Databricks integration for Grafana Cloud.

How to solve key site reliability engineering challenges

Modern site reliability engineering challenges stem from the difficult requirement of confirming why complex systems fail in ways staging cannot replicate. While observability tools signal failures, and AI SREs reason over data, they leave observability gaps regarding the actual state of running code. By utilizing runtime context, teams capture live execution data to accelerate production debugging, resolving incidents in minutes without requiring manual redeploy cycles.

"Deployment Visibility for Platform Teams | ENV Zero Topic Talk"

Welcome to another ENV Zero Topic Talk! Today, we discuss the importance of deployment visibility for platform teams. In today's fast-paced development environment, real-time insights into the deployment pipeline are crucial to ensure smooth operations, manage risks, and maintain control. ENV Zero provides comprehensive deployment visibility, allowing teams to track every stage from code commit to post-deployment monitoring. With our intuitive dashboard, platform teams can identify bottlenecks, resolve issues faster, and optimize resource management for quicker, more stable deployments.

From GIGO to Digital Twin: How DCIM G2 Cleans Up Your Data Center Data Quality

“Garbage in, garbage out.” Everyone who has ever worked in computing or other data-adjacent fields has heard this adage at least once. This phrase or acronym (GIGO) reflects the fundamental concept in both computing and data governance that the quality of your data is the critical determinant of successful results in any system, regardless of whether your focus is IT or OT.