Operations | Monitoring | ITSM | DevOps | Cloud

Observability is a design problem: Live Laugh Logs ep. 1 - KubeCon Amsterdam 2026

What happens when 20,000 engineers descend on Amsterdam to talk about Kubernetes and AI? Welcome to Episode 1 of Live Laugh Logs, the podcast from Annie, Lewis and Andre from the Coralogix Developer Relations team where we will get together and recap everything going on in our worlds! We had an amazing time at KubeCon in Amsterdam and had loads of insights from the talks we went to around designing observability systems, all the AI tools being created and how to observe them, and using agent-generated code.

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.