Operations | Monitoring | ITSM | DevOps | Cloud

The tech world has been building up towards the shift to a fully agentic development life cycle for a few years now. AI is changing how software gets built. Across the Puppet ecosystem, we’re seeing a shift toward more agentic engineering workflows. AI helps generate code, shape documentation, and accelerate how Puppet modules evolve.

Ask an AI for K-pop concert advice without saying the group, city, date, or budget. It may confidently send you to a BLACKPINK tribute night in Cleveland with a $400 resale ticket. The AI was plenty confident. It just had nothing real to go on. That is exactly what happens when developers test AI applications with invented traffic. The test may look reasonable. The result may even pass.

At first glance, Power BI and SquaredUp both look like dashboarding tools. In practice, they solve different problems. SquaredUp serves engineering teams and IT teams, including SREs, DevOps engineers, engineering managers, and technology leaders. It connects to monitoring, cloud, DevOps, and ITSM platforms. This gives teams a real-time insights into service health, performance, and key metrics in one place. Power BI is Microsoft's business intelligence platform, designed for analysts and business teams.

Model routing promises to cut your AI agent spend by offloading routine tasks to cheaper models like Claude Haiku while reserving frontier models like Claude Sonnet for complex reasoning. In the right configuration, routing strategies can reduce inference costs by 40–85%. But if you implement routing incorrectly in a multi-turn agent, you can end up paying more than if you’d never routed at all. Here’s why and how to fix it.

Enterprise teams can now add a Webhook alert channel that POSTs every DNS Spy alert — DNS record changes, domain outages, security check failures, WHOIS updates, phishing look-alike detections — to any HTTPS endpoint as structured JSON. Requests are optionally HMAC-signed, every delivery is logged and retried, and a Send Test button verifies your integration end to end.

Coding agents changed implementation economics faster than they changed confidence. They let us produce more code, more quickly, but they did not make reviewers any better at understanding system-wide consequences. In our Kubernetes automation stack, that gap became impossible to ignore once AI started generating meaningful amounts of controller code.

Most of your employees already spend their day in Slack. So when something breaks, when they need access to a tool, or when they just have a quick question for IT, the last thing they want to do is stop, switch context, open a portal, and fill out a form. That friction is exactly why so many issues never get logged, and why the ones that do often arrive late and half-described. The InvGate Service Management Slack integration closes that gap.

I was at the dentist the other day, getting ready for my appointment. The waiting room was pompously decorated. Each chair seemed to be from a different, expensive Danish designer. As I realize I’m about to get charged through the nose, I get a notification from my beloved Mail app. ** ding ** Screenshot of GitHub email notification It’s a new Pull Request on GitHub. This one is different though. I have no idea where it came from!

When organizations evaluate a software upgrade, the conversation often begins with features, functionality, and innovation. Those considerations are important, but they are rarely the primary concern for the teams responsible for executing the upgrade. Operations leaders are typically focused on a more practical question: can the upgrade be completed successfully, within the planned maintenance window, with clear support paths, and without creating unnecessary disruption for the business?