Say When..

Series of blog posts show progress of updating/adding to EKS Cluster, this post covers adding Kubectl-Ai, & Kiro under Ai Operations. See below for past posts:

Kubectl-Ai: AI operations assistant for troubleshooting, explanations, not a security scanner – It can translate intent into Kubernetes operations and use tools like kubectl/bash.

Convert plain English into commands or YAML manifests like;
- kubectl ai “create a deployment with 3 replicas”
Flow goes;
- Terminal → ~/.kube/config → EKS cluster → AWS profile → Amazon Bedrock → kubectl-ai
- kubectl-ai recommendation
- → edit say_when_git_ops
- → git commit/push
- → Argo CD reconciles
Sources:
- https://github.com/GoogleCloudPlatform/kubectl-ai

Abstract/Table of Contents:

Below is the order of items addressed:
- 3 Steps below
  - Config kubectl-ai NOT pushed to get
    - 2 options to install locally
  - Configure AWS Profile
  - Check Available Bedrock Models
- Important Note: 1st time AWS request-form to utilize AWS Bedrock Anthropic Model
- Helpful Kubectl-ai commands
  - Example cost messing around

3 Steps below:

Config kubectl-ai: NOT pushed to git

https://github.com/GoogleCloudPlatform/kubectl-ai#using-aws-bedrock
- Hit the drop-down for verify your configuration –

			
=====================
ls -la ~
=====================
ls -la ~/.config
=====================
mkdir -p ~/.config/kubectl-ai/config.yaml
=====================
llmProvider: bedrock
model: us.anthropic.claude-sonnet-4-20250514-v1:0
kubeconfig: ~/.kube/config
skipPermissions: false
maxIterations: 10
quiet: false
removeWorkdir: true
uiType: terminal
=====================
sed -n '1,200p' ~/.config/kubectl-ai/config.yaml
=====================
~/.config/
├── argocd/
├── helm/
├── htop/
├── k8sGPT/
├── k9s/
└── kubectl-ai/
    └── config.yaml

		

Option 1 – Install Locally: curl github repo w/sh

Quick install
- https://github.com/GoogleCloudPlatform/kubectl-ai#installation

			
=====================
curl -sSL \
  https://raw.githubusercontent.com/GoogleCloudPlatform/kubectl-ai/main/install.sh |
bash
=====================
kubectl-ai version
=====================

		

Option 2 – Install Locally: Git-Tracked: kubectl-ai/config.example.yaml:

YAML config file:
- https://github.com/GoogleCloudPlatform/kubectl-ai#configuration
- https://github.com/GoogleCloudPlatform/kubectl-ai#using-aws-bedrock
  - As mentioned above – hit the drop-down for verify your configuration

			
cd ~/x/y/z
=====================
mkdir -p docs/kubectl-ai
=====================
nano/vi/IDE docs/kubectl-ai/config.example.yaml
=====================
# kubectl-ai local configuration example
#
# This file is tracked in Git for documentation.
# Copy it to ~/.config/kubectl-ai/config.yaml before using kubectl-ai.
#
# AWS credentials are intentionally not stored here.
# kubectl-ai uses the normal AWS SDK credential chain.
# Amazon Bedrock provider
llmProvider: bedrock
# Bedrock cross-region inference profile
model: us.anthropic.claude-sonnet-4-20250514-v1:0
# Use the current user's Kubernetes configuration
kubeconfig: ~/.kube/config
# Require approval before commands are executed
skipPermissions: false
# Limit the number of agent/tool iterations per request
maxIterations: 10
# Use interactive terminal output
quiet: false
uiType: terminal
# Remove temporary working files after each session
removeWorkdir: true
=====================
sed -n '1,200p' docs/kubectl-ai/config.example.yaml
=====================
git add docs/kubectl-ai/config.example.yaml
git commit -m "document kubectl-ai Bedrock configuration"
git push
=====================
command -v kubectl-ai
=====================
kubectl-ai version
=====================

		

The 8 settings are official Bedrock documentation options/recommendations

Configure AWS Profile

Ensure IRSA role for bedrock K8sgpt is in the ServiceAccount
- https://github.com/GoogleCloudPlatform/kubectl-ai#using-aws-bedrock

			
=====================
export AWS_REGION=us-east-1
export AWS_DEFAULT_REGION=us-east-1
export AWS_PROFILE=<your-profile>
=====================
kubectl config current-context
=====================
kubectl cluster-info
=====================
kubectl get nodes
=====================
aws sts get-caller-identity
=====================
aws configure list
=====================
aws sts get-caller-identity --profile <your-profile>

		

Check available Bedrock models:

			
=====================
aws bedrock list-foundation-models \
  --region us-east-1 \
  --by-provider anthropic \
  --query 'modelSummaries[].{Name:modelName,ID:modelId}' \
  --output table
=====================
aws bedrock list-inference-profiles \
  --region us-east-1 \
  --query 'inferenceProfileSummaries[].{Name:inferenceProfileName,ID:inferenceProfileId,Status:status}' \
  --output table
=====================
kubectl-ai --quiet model
us.anthropic.claude-sonnet-4-20250514-v1:0
=====================

		

Important Note: First time users will fill out a request-form, wait about 15 minutes.

Sources:
- https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html?
- https://docs.aws.amazon.com/bedrock/latest/userguide/troubleshooting-api-error-codes.html

Kubectl-ai Costs:

For Claude Sonnet 4, the approximate on-demand rate is:
- Input: $3 per 1 million tokens
- Output: $15 per 1 million tokens

kubectl-ai task	Example usage	Approximate cost
Simple question	5K input + 1K output	$0.03
Normal diagnosis	10K input + 2K output	$0.06
Larger investigation	25K input + 5K output	$0.15
Very large session	100K input + 20K output	$0.60

Commands while your waiting for submission form to be approved:

			
=====================
- cd ~/x/y/z
- mkdir -p ~/.config/kubectl-ai
- cp docs/kubectl-ai/config.example.yaml \
- ~/.config/kubectl-ai/config.yaml
- chmod 600 ~/.config/kubectl-ai/config.yaml
=====================
sed -n '1,200p' ~/.config/kubectl-ai/config.yaml
=====================
- command -v kubectl-ai
- kubectl-ai version
- aws sts get-caller-identity
- kubectl cluster-info
=====================

		

15 minutes is up – lets check it

Should see an email like this

			
=====================
aws bedrock-runtime converse \
  --region us-east-1 \
  --model-id us.anthropic.claude-sonnet-4-20250514-v1:0 \
  --messages '[{"role":"user","content":[{"text":"Reply with exactly: Bedrock works"}]}]' \
  --inference-config '{"maxTokens":20}' \
  --output json \
  > /tmp/bedrock-test.json
=====================
jq -r '.output.message.content[0].text' \
  /tmp/bedrock-test.json
Bedrock works
=====================
 grep -E '^(llmProvider|model):' \
  ~/.config/kubectl-ai/config.yaml
llmProvider: bedrock
model: us.anthropic.claude-sonnet-4-20250514-v1:0
=====================

		

Kubectl-ai commands:

Can increase code iteration to 20 if 10 is not getting you enough

			
=====================
kubectl ai \
  "List unhealthy pods across all namespaces and explain the problems. Do not make any changes."
=====================
kubectl ai \
  "Check Argo CD Applications that are not Healthy or Synced. Do not modify anything."
=====================
kubectl ai \
  "Review pending pods and explain the scheduling failures. Do not make changes."
=====================
kubectl ai \
  "Inspect recent warning events across the cluster and summarize the most important problems. Do not change resources."
=====================
kubectl ai \
  "Diagnose the unhealthy Tempo StatefulSet and show the GitOps YAML change you recommend. Do not apply the change."
  "Review all Argo CD Applications and identify anything that is not Synced or Healthy. Do not modify the cluster."
=====================
kubectl-ai \
  "Show the current Kubernetes context and list unhealthy pods across all namespaces. Explain the problems but do not make any changes."
  "Review all Argo CD Applications and identify anything that is not Synced or Healthy. Do not modify the cluster."
=====================
kubectl-ai \
  "Review all Argo CD Applications and identify anything that is not Synced or Healthy. Do not modify the cluster."
=====================
kubectl ai
=====================
exit
=====================

		

Example cost messing around for about 45 minutes:

Model	Input cost	Output cost	Total
Claude Sonnet 4.6	43,286 × $3/M = $0.130	948 × $15/M = $0.014	$0.14
Legacy Claude Sonnet 4	438,571 × $3/M = $1.316	6,404 × $15/M = $0.096	$1.41
Combined			$1.56