AI started out as a cool chatbot that you could ask questions to and get responses in real-time, like an enhanced search engine. Fast forwarding a few years and it's changed
As AI Agents begin to increase in usage, knowledge, and understanding of specific tasks, there may be a time when a task is too great for one Agent's knowledge. When that
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,
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