Workloads are meant to be managed in a declarative way within Kubernetes. When it comes to thinking about what declarative means in this case, it's "tell me what to do,
Agents and Agentic Infrastructure give engineers the ability to have a 24/7/365 engineering helper (with the right implementation of course). The current perdiciment is when using public/cloud-based LLMs (Claude, GPT,
Without a good understanding of how Services are performing, you'll never truly know what's important to focus on when optimizing environments, and that's a make or break
Where kagent shines, aside from the ability to use just about any LLM, is the ability to use Agents as a tool for troubleshooting your environment. The goal is to reduce the "
One of the most popular advancements in cloud-native at this time is LLMs and AI Agents, which can help you troubleshoot your environment, build out new environments, and even template out a code