4 min read 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.
6 min read The acceleration of AI-assisted development has created an asymmetric problem. Developers got their force multiplier. SREs are still using the same playbook they had five years ago, except now they're responsible for exponentially more code, written by tools that prioritize speed over operational clarity.
6 min read Part 7 of our AI SRE in Practice Series. This scenario walks through how AI-augmented knowledge transfer changes the onboarding experience, using a real example from a containers team implementing changes to HiveMQ infrastructure.
6 min read Part 6 of our AI SRE in Practice Series. In this scenario we walk through an AWS CNI IP exhaustion incident where 15 services experienced outages before platform teams identified the root cause.
5 min read For an AI SRE to be safe and effective, it cannot rely on generic training data alone. It needs context. Klaudia solves this through a dual-layer approach to context engineering: the Organization Blueprint and the Knowledge Base Integration.
6 min read Part 5 of our AI SRE in Practice Series. This scenario walks through a policy enforcement incident where a seemingly minor configuration change caused widespread pod failures that required deep investigation across the cluster to understand the scope and root cause.
5 min read This article explores the technical realities of building Klaudia, an agentic AI solution for Cloud-Native infrastructure.
6 min read When a new, competing open-source Kubernetes troubleshooting agent was launched, we thought it would be a good idea to put both tools through identical real-world failure scenarios our customers typically encounter. The objective was to benchmark Klaudia Agentic AI and the open-source AI agent, and compare their performance across common Kubernetes failure scenarios.
6 min read Part 4 of our AI SRE in Practice Series. In this part we examine what happens when a node terminates unexpectedly, and dealing with the harder question of why it happened and how to prevent it from happening in the future.