Operations | Monitoring | ITSM | DevOps | Cloud

In the last post we discussed why we’re building our own Certificate Transparency (CT) search tool. There’s good background on the CT ecosystem in that post, so check it out if you haven’t. This post assumes a certain understanding of terminology covered previously. Now that we know where the CT logs live, and the different kinds of logs, we need to start reading them.

Puppet Edge Workflows, available with Puppet Enterprise Advanced, provide the orchestration tools to define multistep workflows to run against your infrastructure. This allows Puppet experts to create workflows that Ops teams can run without having deep Puppet language knowledge or the underlying infrastructure.

To have a fast and reliable experience digitally you would need to do more than resolving issues. This is why people prefer synthetic monitoring which simulates real user actions with regular intervals. Using this method, businesses can detect performance shortcomings and any technical issues. From testing website load to full flow checkout, everything can be tested before users face any issues.

The hidden blockers slowing down your incident response and how to remove them before they become reliability risks. Incidents rarely go wrong because of one big failure. Most of the time, it’s a handful of small, familiar mistakes that slow teams down, muddy communication, or create confusion in the heat of the moment. Fortunately, these mistakes are predictable and fixable.

Six months of safer releases, sharper AI insights, and a unified platform experience for feature flags and experimentation.

The rise of autonomous AI attacks operating at machine speed demands that network security evolve beyond human capacity and manual processes. Kentik AI Advisor counters this threat by using AI for good, reasoning across full network context to proactively eliminate vulnerabilities and guide immediate, confident defense.

Artificial intelligence (AI) infrastructure requires four pillars working in tandem as a system (compute, storage, networking, and orchestration) tailored to your actual workload needs, not hype. Artificial intelligence (AI) infrastructure isn’t just more hardware. It’s a new class of system—highly distributed, resource-intensive, and tightly coupled across compute, storage, and network layers.

Monitoring AI systems isn’t business as usual. Monitoring AI isn’t like monitoring traditional systems. You can’t just track uptime or response times and call it a day. AI models evolve, data shifts, and behavior drifts over time, which means your monitoring has to evolve, too. If you’re running AI workloads in production, you already know this. Your models might look healthy according to your infrastructure metrics, but they’re still making bad predictions.

AI workloads break every assumption you have about infrastructure management. AI is everywhere. Machine learning-based tools are answering customer service questions, accelerating incident resolution, catching fraudulent transactions, spotting defects on production lines, and powering late-night searches that delve into the random topic that pops into your head right before bedtime. Behind every prediction, response, or generated sentence is massive computing power doing serious, continuous work.

AI observability closes the gap between “something’s wrong” and “here’s what to fix.” If you run AI in production, you might have felt the whiplash. Yesterday, your LLM answered in 300 milliseconds (ms). Today p99 crawls, costs spike, and nobody’s sure if the culprit is model behavior, data freshness, or GPUs stuck at the ceiling. Dashboards light up, but they don’t tell you which issue puts customers at risk. That’s the gap AI observability closes.