I'm curious about how AI and large language models (LLMs) can be integrated into Kubernetes environments. While I can see their potential in summarizing logs and identifying issues, I'm particularly interested in what a safe and efficient workflow looks like for production clusters. Any insights on how AI can help streamline tasks for developers and avoid common pitfalls would be greatly appreciated!
2 Answers
Honestly, I’m not really interested in bringing AI anywhere near my clusters. I already face enough challenges managing infrastructure on Azure and AWS without adding AI into the mix. It feels like a risky move I’d rather avoid!
I think AI could really shine in suggesting adjustments for resource quotas and limits. Also, using it for log and metric analysis could help in identifying trends or issues quickly. However, it’s important that these agents don’t take too much control—to me, they should act more like a helpful assistant rather than having full agency.
That’s an interesting point! I agree that log analysis is somewhat covered by tools like Sentry, but having an AI that truly understands context would be beneficial. What specific features would you want from such an agent?
I totally get it! It's hard enough dealing with the current setup without introducing more complexity.