Deploy

Deploy to Databricks Apps

App Naming Convention

Unless the user specifies a different name, apps should use the prefix agent-*:

• agent-data-analyst
• agent-customer-support
• agent-code-helper

Update the app name in databricks.yml:

resources:
  apps:
    {{BUNDLE_NAME}}:
      name: "agent-your-app-name"  # Use agent-* prefix

Deploy Commands

IMPORTANT: Always run BOTH commands to deploy and start your app:

# 1. Validate bundle configuration (catches errors before deploy)
databricks bundle validate

# 2. Deploy the bundle (creates/updates resources, uploads files)
databricks bundle deploy

# 3. Run the app (starts/restarts with uploaded source code) - REQUIRED!
databricks bundle run {{BUNDLE_NAME}}

Note: bundle deploy only uploads files and configures resources. bundle run is required to actually start/restart the app with the new code. If you only run deploy, the app will continue running old code!

The resource key {{BUNDLE_NAME}} matches the app name in databricks.yml under resources.apps.

Handling "App Already Exists" Error

If databricks bundle deploy fails with:

Error: failed to create app
Failed to create app <app-name>. An app with the same name already exists.

Ask the user: "Would you like to bind the existing app to this bundle, or delete it and create a new one?"

Option 1: Bind Existing App (Recommended)

Step 1: Get the existing app's full configuration:

# Get app config including budget_policy_id and other server-side settings
databricks apps get <existing-app-name> --output json | jq '{name, budget_policy_id, description}'

Step 2: Update databricks.yml to match the existing app's configuration exactly:

resources:
  apps:
    {{BUNDLE_NAME}}:
      name: "existing-app-name"  # Must match exactly
      budget_policy_id: "xxx-xxx-xxx"  # Copy from step 1 if present

Why this matters: Existing apps may have server-side configuration (like budget_policy_id) that isn't in your bundle. If these don't match, Terraform will fail with "Provider produced inconsistent result after apply". Always sync the app's current config to databricks.yml before binding.

Step 3: If deploying to a mode: production target, set workspace.root_path:

targets:
  prod:
    mode: production
    workspace:
      root_path: /Workspace/Users/${workspace.current_user.userName}/.bundle/${bundle.name}/${bundle.target}

Why this matters: Production mode requires an explicit root path to ensure only one copy of the bundle is deployed. Without this, the deploy will fail with a recommendation to set workspace.root_path.

Step 4: Check if already bound, then bind if needed:

# Check if resource is already managed by this bundle
databricks bundle summary --output json | jq '.resources.apps'

# If the app appears in the summary, skip binding and go to Step 5
# If NOT in summary, bind the resource:
databricks bundle deployment bind {{BUNDLE_NAME}} <existing-app-name> --auto-approve

Note: If bind fails with "Resource already managed by Terraform", the app is already bound to this bundle. Skip to Step 5 and deploy directly.

Step 5: Deploy:

databricks bundle deploy
databricks bundle run {{BUNDLE_NAME}}

Option 2: Delete and Recreate

databricks apps delete <app-name>
databricks bundle deploy

Warning: This permanently deletes the app's URL, OAuth credentials, and service principal.

Unbinding an App

To remove the link between bundle and deployed app:

databricks bundle deployment unbind {{BUNDLE_NAME}}

Use when:

• Switching to a different app
• Letting bundle create a new app
• Switching between deployed instances

Note: Unbinding doesn't delete the deployed app.

Query Deployed App

IMPORTANT: Databricks Apps are only queryable via OAuth token. You cannot use a Personal Access Token (PAT) to query your agent. Attempting to use a PAT will result in a 302 redirect error.

Get OAuth token:

databricks auth token | jq -r '.access_token'

Send request:

curl -X POST <app-url>/invocations \
  -H "Authorization: Bearer <oauth-token>" \
  -H "Content-Type: application/json" \
  -d '{ "input": [{ "role": "user", "content": "hi" }], "stream": true }'

If using memory - include user_id to scope memories per user:

curl -X POST <app-url>/invocations \
  -H "Authorization: Bearer <oauth-token>" \
  -H "Content-Type: application/json" \
  -d '{
      "input": [{"role": "user", "content": "What do you remember about me?"}],
      "custom_inputs": {"user_id": "user@example.com"}
  }'

On-Behalf-Of (OBO) User Authentication

To authenticate as the requesting user instead of the app service principal:

from agent_server.utils import get_user_workspace_client

# In your agent code
user_client = get_user_workspace_client()
# Use user_client for operations that should run as the user

This is useful when you want the agent to access resources with the user's permissions rather than the app's service principal permissions.

See: OBO authentication documentation

Debug Deployed Apps

# View logs (follow mode)
databricks apps logs <app-name> --follow

# Check app status
databricks apps get <app-name> --output json | jq '{app_status, compute_status}'

# Get app URL
databricks apps get <app-name> --output json | jq -r '.url'

Important Notes

• App naming convention: App names must be prefixed with agent- (e.g., agent-my-assistant, agent-data-analyst)
• Name is immutable: Changing the name field in databricks.yml forces app replacement (destroy + create)
• Remote Terraform state: Databricks stores state remotely; same app detected across directories
• Review the plan: Look for # forces replacement in Terraform output before confirming

FAQ

Q: I see a 200 OK in the logs, but get an error in the actual stream. What's going on?

This is expected behavior. The initial 200 OK confirms stream setup was successful. Errors that occur during streaming don't affect the initial HTTP status code. Check the stream content for the actual error message.

Q: When querying my agent, I get a 302 redirect error. What's wrong?

You're likely using a Personal Access Token (PAT). Databricks Apps only support OAuth tokens. Generate one with:

databricks auth token

Q: How do I add dependencies to my agent?

Use uv add:

uv add <package_name>
# Example: uv add "mlflow-skinny[databricks]"

Troubleshooting