Operations | Monitoring | ITSM | DevOps | Cloud

Platform Engineering leaders are caught between two competing imperatives. You’re under pressure to flatten cloud spend but your team is still provisioning defensively because nobody wants to be the person who causes a production incident. You try to optimize, but six months later, when someone pulls a report, nothing has changed.

Cluster API (CAPI) is transforming how organizations deploy and manage fleets of Kubernetes clusters by introducing declarative, Kubernetes-style APIs to automate cluster provisioning and lifecycle management. While CAPI excels at creating consistent and repeatable cluster deployments across different infrastructure providers, operating it at a massive scale introduces unique day-to-day challenges.

Once upon a time, a monolith running on a handful of servers meant that incident management, even at 2:17 AM, was something a single generalist could handle. One person with enough context across the stack could reasonably diagnose whether the database was choking, a config had changed, or a server was running hot. They’d fix it and go back to sleep.

Out-of-the-box and bring-your-own AI agents that encode operational knowledge boost troubleshooting speed and accuracy across cloud native infrastructure TEL AVIV and SAN FRANCISCO, March 18, 2026 — Komodor, the autonomous AI SRE company for cloud-native infrastructure, today announced a new extensibility framework that transforms its Klaudia AI technology into a universal multi-agent platform for troubleshooting and optimizing performance of complex cloud native infrastructures and applications.

A FinOps analyst walks into a Monday morning meeting with a detailed spreadsheet showing $2.3M in potential Kubernetes cost savings. The recommendations look straightforward: reduce memory limits by 40%, scale down replicas during off-peak hours, consolidate workloads onto fewer nodes. The numbers are compelling, the methodology is sound, and the savings would make a material impact on quarterly cloud spend. The SRE team immediately objects.

Kubernetes troubleshooting traditionally requires deep platform expertise. Understanding pod lifecycle, decoding error messages, correlating events across resources, and identifying root cause all demand experience that takes years to build. This expertise gap creates a bottleneck where only senior engineers can handle production issues, limiting how quickly teams can resolve incidents.

This is part two of a series of the implications of AI generated code becoming mainstream. We recently wrote about how AI-generated code is overwhelming SRE teams with production complexity they can’t manage. Turns out that’s only half the problem. The other half shows up on the cloud bill. A prospect reached out to us last month. They’d been using Cursor and Claude Code for six months, shipping features at unprecedented velocity. Product was thrilled.