adobe-deploy-integration

name	adobe-deploy-integration
description	Deploy Adobe-powered applications to Vercel, Cloud Run, and Adobe App Builder with proper credential injection and health monitoring. Use when deploying Adobe API integrations to production platforms. Trigger with phrases like "deploy adobe", "adobe Vercel", "adobe Cloud Run", "adobe App Builder deploy", "adobe production deploy".
allowed-tools	Read, Write, Edit, Bash(vercel:*), Bash(gcloud:*), Bash(aio:*)
version	1.6.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","design","adobe"]
compatibility	Designed for Claude Code

Adobe Deploy Integration

Overview

Deploy Adobe-powered applications to three platforms: Vercel (serverless), Google Cloud Run (containers), and Adobe App Builder (native Adobe Runtime). Each with proper OAuth credential management.

Prerequisites

• Adobe OAuth Server-to-Server credentials for production
• Platform CLI installed (vercel, gcloud, or aio)
• Application tested in staging environment

Instructions

Option A: Adobe App Builder (Native Adobe Hosting)

App Builder deploys serverless Runtime actions directly to Adobe infrastructure:

# Login to Adobe I/O CLI (requires IMS auth since AIO CLI v11)
aio login

# Select your project and workspace
aio console project select
aio console workspace select Production

# Deploy all actions, static assets, and event registrations
aio app deploy

# Check deployed actions
aio runtime action list

# View action logs
aio runtime activation list --limit 10
aio runtime activation logs <activationId>

// app.config.yaml — App Builder configuration
application:
  actions: actions
  web: web-src
  runtimeManifest:
    packages:
      my-adobe-app:
        actions:
          process-image:
            function: actions/process-image/index.js
            runtime: nodejs:20
            inputs:
              ADOBE_CLIENT_ID: $ADOBE_CLIENT_ID
              ADOBE_CLIENT_SECRET: $ADOBE_CLIENT_SECRET
            annotations:
              require-adobe-auth: true
              final: true

Option B: Vercel Deployment

# Set Adobe credentials as Vercel environment variables
vercel env add ADOBE_CLIENT_ID production
vercel env add ADOBE_CLIENT_SECRET production
vercel env add ADOBE_SCOPES production

# Deploy
vercel --prod

// vercel.json
{
  "functions": {
    "api/**/*.ts": {
      "maxDuration": 60
    }
  },
  "env": {
    "ADOBE_CLIENT_ID": "@adobe_client_id",
    "ADOBE_CLIENT_SECRET": "@adobe_client_secret",
    "ADOBE_SCOPES": "@adobe_scopes"
  }
}

// api/firefly/generate.ts — Vercel serverless function
import type { VercelRequest, VercelResponse } from '@vercel/node';
import { getAccessToken } from '../../src/adobe/client';

export default async function handler(req: VercelRequest, res: VercelResponse) {
  if (req.method !== 'POST') return res.status(405).end();

  try {
    const token = await getAccessToken();
    const fireflyResponse = await fetch(
      'https://firefly-api.adobe.io/v3/images/generate',
      {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${token}`,
          'x-api-key': process.env.ADOBE_CLIENT_ID!,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify(req.body),
      }
    );

    const result = await fireflyResponse.json();
    return res.status(fireflyResponse.status).json(result);
  } catch (error: any) {
    return res.status(500).json({ error: error.message });
  }
}

Option C: Google Cloud Run

# Store credentials in Secret Manager
echo -n "${ADOBE_CLIENT_ID}" | gcloud secrets create adobe-client-id --data-file=-
echo -n "${ADOBE_CLIENT_SECRET}" | gcloud secrets create adobe-client-secret --data-file=-

# Build and deploy
gcloud builds submit --tag gcr.io/${PROJECT_ID}/adobe-service

gcloud run deploy adobe-service \
  --image gcr.io/${PROJECT_ID}/adobe-service \
  --region us-central1 \
  --platform managed \
  --set-secrets="ADOBE_CLIENT_ID=adobe-client-id:latest,ADOBE_CLIENT_SECRET=adobe-client-secret:latest" \
  --set-env-vars="ADOBE_SCOPES=openid,AdobeID,firefly_api" \
  --min-instances=1 \
  --timeout=60s

Health Check Endpoint (All Platforms)

// api/health.ts
export async function GET() {
  const checks: Record<string, any> = {};

  // Test Adobe IMS token generation
  try {
    const start = Date.now();
    const token = await getAccessToken();
    checks.adobe = {
      status: 'healthy',
      latencyMs: Date.now() - start,
      tokenLength: token.length,
    };
  } catch (error: any) {
    checks.adobe = {
      status: 'unhealthy',
      error: error.message,
    };
  }

  const overall = Object.values(checks).every(
    (c: any) => c.status === 'healthy'
  ) ? 'healthy' : 'degraded';

  return Response.json({
    status: overall,
    services: checks,
    timestamp: new Date().toISOString(),
  });
}

Output

• Application deployed to chosen platform
• Adobe credentials injected via platform secret management
• Health check endpoint validates IMS connectivity
• Serverless function timeout configured for Adobe API latency

Error Handling

Issue	Cause	Solution
aio app deploy auth error	Not logged in to AIO CLI	Run aio login
Vercel function timeout	Adobe API takes > 10s	Increase maxDuration in vercel.json
Cloud Run cold start timeout	Token generation on cold start	Set min-instances=1
Secret not found	Wrong secret name	Verify with gcloud secrets list or vercel env ls

Resources

• Adobe App Builder Deployment
• Vercel Environment Variables
• Cloud Run Secrets

Next Steps

For webhook handling, see adobe-webhooks-events.