Operations | Monitoring | ITSM | DevOps | Cloud

Here’s a problem I run into constantly: something breaks in production, I can see the 500 errors in my logs, but I can’t reproduce it locally. The trace shows me the dependency graph but not the actual request that failed. This is especially painful in microservices. I was looking at a CNCF example the other day (a simple demo app, like 4 pods) and it already had so many cross-service dependencies that understanding what broke required looking at the whole system at once.

IBM Think 2026 made one thing clear: infrastructure leaders are being asked to support more AI, more automation, and faster decision-making without adding unnecessary complexity or risk. Held earlier this month in Boston, IBM Think 2026 focused heavily on enterprise AI, hybrid cloud, automation, governance, and operational transformation.

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.