Operations | Monitoring | ITSM | DevOps | Cloud

What is an AI sandbox? A developer's guide

An AI sandbox is an isolated environment where code from an AI coding agent runs without direct access to your machine or your production systems. If the agent runs a destructive command or a mistaken script, the damage stays contained inside the sandbox. Developers use AI sandboxing to let agents execute code freely while keeping the host and its credentials out of reach.

Meet GCX: Give Your AI Coding Agent Production Context

Your AI agents are only as good as the context it has. Without access to what's happening in production, it can only make educated guesses. Chapters: In this video, you'll meet GCX. The bridge between AI coding agents like Claude Code, Codex, Cursor, and your Grafana observability stack. You will learn how GCX securely gives AI agents access to metrics, logs, traces, dashboards, and other production telemetry so they can investigate issues, answer questions, and help you debug with real operational context.

Bring Your Own VLAN: Moving VMs to Kubernetes Without Changing a Single IP

For many organizations, modernizing their VMs before migrating them is not a realistic option, especially when external events trigger the migration. Mapping dependencies and refactoring network configurations before the deadline is impractical, forcing VMs to move as they are. The mechanics of moving a VM are largely solved.

9 Best Log File Analysis Tools for IT and DevOps Teams

An incident is open and the evidence is scattered. The application logs point to a connection timeout; the load balancer shows nothing unusual, and the container that produced the original error was replaced eighteen minutes ago. Three engineers are logged into three separate hosts running the same search, and the log line that would explain it has already rotated away. That is the moment most teams start shopping for a log file analysis platform.

10 Best IT Operations Management Tools (ITOM) Compared for 2026

No IT team sets out to run ten monitoring tools. It happens one purchase at a time. You add a network monitor after one outage, a server monitoring tool after the next, then a log analyzer, then an APM product when the app team gets tired of guessing. Then something breaks, and every one of those tools has an opinion. Each fires its own alerts. None of them agrees on the cause, and the first hour of the incident goes to deciding which screen to believe.

The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust

Dynatrace gives engineering teams deep, real-time visibility into every service they run. That visibility is the foundation of every effective reliability practice, and it's exactly the foundation Gremlin is built to extend. Once you can see how your distributed systems behave today, the next step is knowing how they'll behave under failure tomorrow—and to do it before those failures happen.

The New AI Mandate: Smarter Usage, Lower AI Compliance Risk, Better Outcomes

For the last two years, enterprise AI strategy has largely revolved around one message: use AI as much as possible. CIOs encouraged experimentation, CFOs approved budgets, and organizations pushed employees to adopt tools like ChatGPT, Claude, Copilot, and Gemini in the hope that productivity gains would naturally follow. The prevailing assumption was that simply increasing usage would accelerate innovation and unlock efficiency across the organization. But the enterprise conversation is changing quickly.