I'm creating opensource AWS DevOps Agent for SRE on-call engineers LLM root-cause hypothesis).

A developer has built an open-source AWS DevOps agent for SRE on-call engineers that automatically gathers context when a Kubernetes alert fires and sends an LLM-generated root-cause hypothesis to Telegram, complete with supporting evidence and a way to verify if the hypothesis is wrong. The tool is designed to be read-only and never modify the cluster, and in its first run on a real EKS cluster it cost 13 cents. In testing, the local model scored 5 out of 7 on tricky scenarios while the cloud model got 6 out of 6.

Day 1/100. For the past two months, I’ve been building an open-source tool for on-call engineers in my spare time. When a Kubernetes alert fires, it automatically gathers context and sends a hypothesis about the "why" to Telegram, complete with evidence and a way to verify if the hypothesis is incorrect. By design, it doesn't touch anything in the cluster. Yesterday, I ran it on a real EKS cluster for the first time; the cost was 13 cents. I measured accuracy honestly: the local model got 5 out of 7 right on tricky scenarios, while the cloud model got 6 out of 6. I’m asking for one of two things: if you use Kubernetes and handle on-call duties, I’d love a 25-minute chat about your most recent overnight incident. Alternatively, you could install and break it, it’s a single helm install command link in the comments . ⭐ I’m not asking for star on GitHub, I’m asking for your any opinion .