deploy

name	deploy
description	Deploy TypeScript LangChain agent to Databricks. Use when: (1) User wants to deploy, (2) User says 'deploy', 'push to databricks', 'production', (3) After making changes that need deployment.

Deploy to Databricks

Quick Deploy

# Validate configuration
databricks bundle validate -t dev

# Deploy to dev environment
databricks bundle deploy -t dev

# Start the app
databricks bundle run agent_langchain_ts

Deployment Targets

Development (dev)

databricks bundle deploy -t dev

Characteristics:

• Default target
• User-scoped naming: db-agent-langchain-ts-<username>
• Development mode permissions
• Auto-created resources

Production (prod)

databricks bundle deploy -t prod

Characteristics:

• Production mode
• Stricter permissions
• Fixed naming: db-agent-langchain-ts-prod
• Requires explicit configuration

Step-by-Step Deployment

1. Prepare Code

Ensure code is committed and tested:

# Test locally first
npm run dev

# Run tests
npm test

# Verify build works
npm run build

2. Validate Bundle

databricks bundle validate -t dev

This checks:

• databricks.yml syntax
• app.yaml configuration
• Resource references
• Variable interpolation

3. Deploy Bundle

databricks bundle deploy -t dev

This will:

• Create MLflow experiment if needed
• Upload source code
• Configure app environment
• Grant resource permissions
• Create app instance

4. Start App

databricks bundle run agent_langchain_ts

Or manually:

databricks apps start db-agent-langchain-ts-<username>

5. Verify Deployment

# Check app status
databricks apps get db-agent-langchain-ts-<username>

# View logs
databricks apps logs db-agent-langchain-ts-<username> --follow

# Test health endpoint
curl https://<workspace-host>/apps/db-agent-langchain-ts-<username>/health

Managing Existing Apps

Bind Existing App

If app already exists:

# Get app details
databricks apps get db-agent-langchain-ts-<username>

# Bind to bundle
databricks bundle deploy -t dev --force-bind

Delete and Recreate

# Delete existing app
databricks apps delete db-agent-langchain-ts-<username>

# Deploy fresh
databricks bundle deploy -t dev

Configuration Files

databricks.yml

Main bundle configuration:

bundle:
  name: agent-langchain-ts

variables:
  serving_endpoint_name:
    default: "databricks-claude-sonnet-4-5"

resources:
  experiments:
    agent_experiment:
      name: /Users/${workspace.current_user.userName}/agent-langchain-ts

  apps:
    agent_langchain_ts:
      name: db-agent-langchain-ts-${var.resource_name_suffix}
      source_code_path: ./
      resources:
        - name: serving-endpoint
          serving_endpoint:
            name: ${var.serving_endpoint_name}
            permission: CAN_QUERY

app.yaml

Runtime configuration:

command:
  - npm
  - start

env:
  - name: DATABRICKS_MODEL
    value: "databricks-claude-sonnet-4-5"
  - name: MLFLOW_TRACKING_URI
    value: "databricks"
  - name: MLFLOW_EXPERIMENT_ID
    value_from: "experiment"

resources:
  - name: serving-endpoint
    serving_endpoint:
      name: ${var.serving_endpoint_name}
      permission: CAN_QUERY

Viewing Deployed App

Get App URL

databricks apps get db-agent-langchain-ts-<username> --output json | jq -r .url

Access App

Navigate to:

https://<workspace-host>/apps/db-agent-langchain-ts-<username>

Test Deployed App

# Health check
curl https://<workspace-host>/apps/db-agent-langchain-ts-<username>/health

# Chat request
curl -X POST https://<workspace-host>/apps/db-agent-langchain-ts-<username>/api/chat \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <databricks-token>" \
  -d '{
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Monitoring

View Logs

# Follow logs in real-time
databricks apps logs db-agent-langchain-ts-<username> --follow

# Get last 100 lines
databricks apps logs db-agent-langchain-ts-<username> --tail 100

# Filter logs
databricks apps logs db-agent-langchain-ts-<username> | grep ERROR

View MLflow Traces

See MLflow Tracing Guide for viewing traces in your workspace.

App Metrics

# Get app details
databricks apps get db-agent-langchain-ts-<username> --output json

# Check app state
databricks apps get db-agent-langchain-ts-<username> --output json | jq -r .state

Updating Deployed App

Update Code

# Make changes to code
# Then redeploy
databricks bundle deploy -t dev

# Restart app
databricks apps restart db-agent-langchain-ts-<username>

Update Configuration

Edit app.yaml or databricks.yml, then:

databricks bundle deploy -t dev
databricks apps restart db-agent-langchain-ts-<username>

Adding Resources

Add Serving Endpoint Permission

Edit app.yaml:

resources:
  - name: serving-endpoint
    serving_endpoint:
      name: "your-endpoint-name"
      permission: CAN_QUERY

Then redeploy:

databricks bundle deploy -t dev

Add Unity Catalog Function

Edit databricks.yml:

resources:
  - name: uc-function
    function:
      name: "catalog.schema.function_name"
      permission: EXECUTE

Update app.yaml to pass function config:

env:
  - name: UC_FUNCTION_CATALOG
    value: "catalog"
  - name: UC_FUNCTION_SCHEMA
    value: "schema"
  - name: UC_FUNCTION_NAME
    value: "function_name"

Redeploy:

databricks bundle deploy -t dev

Troubleshooting

"App with same name already exists"

Either bind existing app:

databricks bundle deploy -t dev --force-bind

Or delete and recreate:

databricks apps delete db-agent-langchain-ts-<username>
databricks bundle deploy -t dev

"Permission denied on serving endpoint"

Ensure endpoint is listed in app.yaml resources:

resources:
  - name: serving-endpoint
    serving_endpoint:
      name: "databricks-claude-sonnet-4-5"
      permission: CAN_QUERY

"Experiment not found"

Create experiment:

databricks experiments create \
  --experiment-name "/Users/$(databricks current-user me --output json | jq -r .userName)/agent-langchain-ts"

Or update databricks.yml to auto-create:

resources:
  experiments:
    agent_experiment:
      name: /Users/${workspace.current_user.userName}/agent-langchain-ts

"App failed to start"

Check logs:

databricks apps logs db-agent-langchain-ts-<username>

Common issues:

• Missing dependencies in package.json
• Incorrect npm start command in app.yaml
• Missing environment variables
• Build errors

"Cannot reach app URL"

Verify:

1. App is running: databricks apps get <app-name> | jq -r .state
2. URL is correct: databricks apps get <app-name> | jq -r .url
3. Authentication token is valid

CI/CD Integration

GitHub Actions Example

name: Deploy to Databricks

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Set up Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '18'

      - name: Install dependencies
        run: npm install

      - name: Run tests
        run: npm test

      - name: Install Databricks CLI
        run: |
          curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh

      - name: Deploy to Databricks
        env:
          DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
          DATABRICKS_TOKEN: ${{ secrets.DATABRICKS_TOKEN }}
        run: |
          databricks bundle deploy -t prod
          databricks bundle run agent_langchain_ts

Best Practices

1. Test Locally First: Always test with npm run dev before deploying
2. Use Dev Environment: Test deployments in dev before prod
3. Monitor Logs: Check logs after deployment
4. Version Control: Commit changes before deploying
5. Resource Permissions: Verify all required resources are granted in app.yaml
6. MLflow Traces: Monitor traces to debug issues
7. Incremental Updates: Make small changes and test frequently

Related Skills

• quickstart: Initial setup and authentication
• run-locally: Local development and testing
• modify-agent: Making changes to agent configuration