Operations | Monitoring | ITSM | DevOps | Cloud

If your organization runs on tech, everyday issues can be expected. This includes application downtime, erratic connectivity, and failures in remote access, database reachability, site-to-site VPNs, and web-based services. But how do you know if an issue is caused by: Sysadmins usually learn the root cause of an issue after a ticket comes in from the team or customer.

Non-deterministic UIs are the future and will replace your dashboards, but they’re not here yet. So until then, we’re stuck with conversational interfaces. In an effort to try and describe what I consider the future of UIs to look like, I wrote about how you (and I) have been designing dashboards wrong. The core insight was that we've been designing for static representations of data that sit on a TV in the office, when the actual use case is someone at a desk using them to debug an issue.

As AI agents move from copilots to autonomous systems, they’re generating and consuming data at unprecedented scale. The result is a new kind of infrastructure pressure — one that’s quietly reshaping how organizations think about data, cost, and control. Across IT, Security, and Observability, leaders are realizing a hard truth: too much data is too costly.

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 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.