Operations | Monitoring | ITSM | DevOps | Cloud

Modern IT infrastructure is messy, spanning multiple vendors, cloud platforms, and on-premises systems, with critical data spread across separate tools. This patchwork makes troubleshooting harder for IT teams and AI agents alike, forcing them to piece together operational context from different domains and interfaces before they can act with a complete understanding of the environment. But what if AI agents could pull operational data from across your diverse IT environment and correlate it for you?

What is SharePoint document lifecycle? A guide to the stages a SharePoint document moves through, the Microsoft Purview tools that manage each stage, and where Squirrel handles the archive tier.

Last month, an AI agent running inside Cursor wiped PocketOS's entire production database, including its backups, in roughly nine seconds. The agent found an API token in an unrelated file, originally created for managing custom domains, and used that token to execute the deletion. The backups sat inside the same blast radius as the database the agent was operating against. Nine months earlier, a Replit AI agent had done the same thing to a SaaStr database during a designated code freeze.

TCO policies have always been one of the most impactful cost levers in Coralogix. Route business-critical data to High, push monitoring data to Medium, archive compliance logs to Low. With the addition of DataPrime expressions (DPXL) – a subset of the DataPrime query language designed for inline filtering at ingest – that routing became even more precise, matching on any field in the event payload, not just application, subsystem, and severity.

Kubernetes has come a long way since its debut in 2014. It’s gone from running a couple of containerized microservices to orchestrating fleets of production workloads spanning everything from AI agents to full scale VMs running in pods. As Kubernetes adoption grows, and its use cases stretch to cover more ground, managing its increasingly complex networking and security landscape demands operational maturity and a platform that supports it.

Monitoring a single server should not require running four daemons. Yet the default open-source recipe for “I just want to watch this one box” still looks like this: install node_exporter, stand up a Prometheus server to scrape it, add Grafana to draw the graphs, and bolt on Alertmanager so you actually hear about a full disk. That is a lot of moving parts — and a lot of YAML — for one machine. This post shows a lighter path.

When an error appears in production, the hardest part often isn’t seeing what broke. It’s understanding why. That’s why we built Root Cause Analysis (RCA). It helps connect the dots between an error and its likely cause, so you can spend less time investigating and more time moving forward. Until now, RCA was only available through plans that included AI credits. Starting today, free plan users can purchase an AI credit subscription and use RCA without changing plans.

Observability has always been about seeing clearly under pressure. But the pressure has changed. Applications are more distributed. Kubernetes environments keep expanding. Digital experiences depend on services, APIs, networks, third-party providers, and now AI models and agents that can make decisions faster than a human team can review every signal.

AI is changing incident management, but not in the way most people think. For years, operations teams focused on getting better at detecting problems. Monitoring improved. Observability improved. AI is now helping teams correlate signals, reduce noise, and identify issues faster than ever before. That’s all valuable, but many organizations are discovering that finding the problem is no longer the hardest part. The harder part is everything that happens next. Who owns the issue?

CloudZero continues to ship new features and enhancements at a fast pace. We’re publishing these product update posts to keep you up to date on our velocity and how we’re adding customer value.