Had a good chuckle here, hah.
But sigh does it really get to the state of the kubernetes ecosystem. All these projects need to be operated, can’t just set it and forget it.
Currently, we only handle the investigation piece and suggest a remediation to the on-call engineer. But to properly move into automatically applying a fix, which we hope to do at some point, we’ll need to integrate into CI/CD
As for the demo example, I agree that the issue itself isn’t the most compelling. We used it as an example since it is easy to visualize and set up for a demo. The agent is capable of investigating more complex issues we've seen in our customer's production clusters, but we're still looking for a way to better simulate these on our test environment, so if you/anyone has ideas we’d love to hear them.
We do think this has more value for engineers/teams with less expertise in k8s, but we think SREs will still find it useful
Pick Kubernetes offering from big 3, deploy it then blow it up.
(I couldn't get HackerNews to format properly and done fighting it)
On Azure, deploy a Kubernetes cluster with following:
Azure CNI with Network Policies
Application Gateway for Containers
External DNS hooked to Azure DNS
Ingress Nginx
Flexible PostGres Server (outside the cluster)
FluxCD/Argo
Something with using Workload Identity
Once all that is configured, put some fake workloads on it and start misconfiguring it with your LLM wired up. When the fireworks start, identify the failures and train your LLM properly.
There are two kinds of outages: people being idiots and legit hard-to-track-down bugs. SREs worth their salt don't need help with the former. They may find an AI bot somewhat useful to find root cause quicker, but usually not so valuable as to justify paying the kind of price you would need to charge to make your business viable to VCs. As for the latter, good luck collecting enough training data.
Otherwise, you're selling a self-driving car to executives who want the chauffeur without the salary. Sounds like a great idea, until you think about the tail cases. Then you wish you had a chauffeur (or picked up driving skills yourself).
Maybe you'll find a market, but as an SRE, I wouldn't want to sell it.
This is a cool proof of concept but almost useless otherwise in a production system
I can already feed Claude or ChatGPT my kubectl output pretty easily
Error handling and logging that are tailored for consumption of a specific pre trained model, thats where this will be ground breaking
Videos show CrashLoopBackOff pod and analyzing logs. This works if Pod is writing to stdout but I've got some stuff doing straight to ElasticSearch. Does LLM speak Elastic Search? How about Log Files in the Pod? (Don't get me started on that nightmare)
You also show fixing by editing YAML in place. That's great except my FluxCD is going revert since you violated principle of "All goes through GitOps". So if you are going to change anything, you need to update the proper git repo. Also in said GitOps is Kustomize so hope you understand all interactions there.
Personally, the stuff that takes most troubleshooting time is Kubernetes infrastructure. Network CNI is acting up. Ingress Controller is missing proper path based routing. NetworkPolicy says No to Pod talking to PostGres Server. CertManager is on strike and certificate has expired. If LLM is quick at identifying those, it has some uses but selling me on "Dev made mistake with Pod Config" is likely not to move the needle because I'm really quick at identifying that.
Maybe I'm not the target market and target market is "Small Dev team that bought Kubernetes without realizing what they were signing up for"