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Manual Vault Secret Setup

If you prefer to configure secrets manually instead of using the automated script:

Get Vault Token​

# Extract root token
kubectl get secret vault-root-token -n vault -o jsonpath="{.data}" | \
  jq -r 'to_entries[] | "\(.key): \(.value | @base64d)"'

Access Vault UI​

Open https://vault.cnoe.localtest.me:8443/. When you are asked to log in to the Vault UI, use the root token from the previous step.

Configure Global LLM Settings​

Navigate to secrets/ai-platform-engineering in Vault UI: https://vault.cnoe.localtest.me:8443/ui/vault/secrets/secret/kv/list/ai-platform-engineering/

The global secret is required and contains LLM provider configuration shared across all agents:

For Azure OpenAI (azure-openai):

LLM_PROVIDER: "azure-openai"
AZURE_OPENAI_API_KEY: <your-api-key>
AZURE_OPENAI_ENDPOINT: <your-endpoint>
AZURE_OPENAI_API_VERSION: <your-api-version>
AZURE_OPENAI_DEPLOYMENT: <your-deployment-name>

For OpenAI (openai):

LLM_PROVIDER: "openai"
OPENAI_API_KEY: <your-api-key>
OPENAI_ENDPOINT: <your-endpoint>
OPENAI_MODEL_NAME: <your-model-name>

For AWS Bedrock (aws-bedrock):

LLM_PROVIDER: "aws-bedrock"
AWS_ACCESS_KEY_ID: <your-access-key>
AWS_SECRET_ACCESS_KEY: <your-secret-key>
AWS_REGION: <your-region>
AWS_BEDROCK_MODEL_ID: <your-model-id>
AWS_BEDROCK_PROVIDER: <your-provider>

For Google Gemini (google-gemini):

LLM_PROVIDER: "google-gemini"
GOOGLE_API_KEY: <your-api-key>
GOOGLE_MODEL_NAME: <your-model-name>

For GCP Vertex (gcp-vertex):

LLM_PROVIDER: "gcp-vertex"
GCP_PROJECT_ID: <your-project-id>
GCP_LOCATION: <your-location>
GCP_MODEL_NAME: <your-model-name>

Configure Agent-Specific Secrets​

For each agent you plan to use, populate all required fields in their respective secrets (e.g., github-secret, pagerduty-secret, jira-secret). All fields are required for the agent to function properly.