Operations | Monitoring | ITSM | DevOps | Cloud

The shift from AI copilots to autonomous agents is redefining infrastructure requirements. Discover how to build secure, stateful, and compliant Agentic AI systems using Kubernetes, sandboxing, and observability while meeting EU AI Act standards.

Not long ago, adding a new cost connector to CloudZero was a serious undertaking. We’d task multiple engineers, build in extended review cycles, run a private preview period. But a single connector could take up to two months from kickoff to customer hands. For the major cloud providers, that timeline was acceptable. The size of the investment matched the scale of the integration. But the tools landscape has changed. Our customers’ teams don’t just run on AWS and Azure.

Security teams face overwhelming amounts of data and complex interfaces, making it hard to access critical insights. AI tools promise solutions, but integration remains difficult as time ticks away and leadership wants the latest data to inform risk decisions. Most security platforms lack seamless integration, slowing access to important data and hindering AI-powered workflows.

If you’ve ever opened the alerts tab during a busy period, you know the problem. There are alerts you’ve already looked at, alerts someone on your team is handling, and alerts that fired on a known issue that’s being worked on. They all sit together in the same list alongside the new ones you haven’t seen yet.

Last week, a fleet of autonomous robotaxis in China suddenly stopped working—at scale. Over a hundred vehicles stalled across a city, stranding passengers in traffic and raising immediate concerns about safety, reliability, and trust in autonomous systems. This wasn’t just a bad day for self-driving cars. It was a distributed systems failure, one that happened in the physical world, not just in dashboards.

OpenTelemetry is great at answering one question: “what just broke?” The problem is that most teams need a different answer first: “what is about to break in this release?” That is where trace-based testing comes in, especially for teams running a vendor-neutral OTel stack (Collector + Tempo/Jaeger + Prometheus) and needing deterministic release gates.

Platform engineering teams have access to hundreds of metrics, yet over 40% of platform initiatives cannot demonstrate measurable value within the first year. Teams that cannot quantify their impact fail to obtain executive sponsorship, risk being defunded, and ultimately, face deprecation. To accurately calculate a platform’s ROI, platform engineering teams need to differentiate between signals that measure platform effectiveness and those that should be used solely for investigative purposes.