You\u2019ve built an intelligent AI agent \u2014 it works locally, it\u2019s smart, and it solves real problems. But now comes the big leap: deploying that agent so it runs securely, reliably, and at scale<\/strong> in the cloud.<\/p>\n\n\n\n Google Cloud\u2019s official blog outlines three hands-on labs to help you deploy AI agents using different cloud platforms<\/strong>. Each offers a trade-off between simplicity, control, and scalability \u2014 and each is ideal for a specific stage of your production journey. (Google Cloud<\/a>)<\/p>\n\n\n\n Before diving into the labs, let\u2019s set the stage. When moving an AI agent from development into production, you need to think about:<\/p>\n\n\n\n Google Cloud gives you three deployment targets:<\/p>\n\n\n\n Best for:<\/strong> Developers who want to deploy Python agents with minimal infrastructure to manage.<\/p>\n\n\n\n The Vertex AI Agent Engine<\/strong> lets you deploy your agent without provisioning servers or containers<\/em>. It\u2019s a fully managed endpoint tailored for Python agents built using the Agent Development Kit (ADK). (Google Cloud<\/a>)<\/p>\n\n\n\n Let\u2019s say you\u2019ve written a multi-agent assistant using the ADK framework:<\/p>\n\n\n\n To deploy this agent using Agent Engine<\/strong>:<\/p>\n\n\n\n This uploads your code to Vertex AI, where Google manages execution, scaling, and session state. (Google Cloud Documentation<\/a>)<\/p>\n\n\n\n Perfect for getting up-and-running quickly with running AI agents. (Google Cloud<\/a>)<\/p>\n\n\n\n Best for:<\/strong> Maximum flexibility without server management + support for multiple languages.<\/p>\n\n\n\n Cloud Run lets you deploy your agent as a containerized service<\/strong>. It automatically handles:<\/p>\n\n\n\n \u2705 Auto-scaling It\u2019s language-agnostic, so your agent can be in Python, Go, Java, or Node.js. (Google Cloud<\/a>)<\/p>\n\n\n\n Assume you have an AI agent in Then build and deploy:<\/p>\n\n\n\n Cloud Run will spin up instances when requests arrive and scale them down when idle \u2014 keeping costs optimized. (Google Cloud Documentation<\/a>)<\/p>\n\n\n\n Best for:<\/strong> Teams needing fine-grained control over deployment, autoscaling, networks, and multi-service setups.<\/p>\n\n\n\n GKE lets you run your agent inside a Kubernetes cluster with full control over:<\/p>\n\n\n\n This is ideal for complex AI systems using multiple interconnected services. (Google Cloud<\/a>)<\/p>\n\n\n\n
\n\n\n\n\ud83e\udde0 Why You Need Deployment Options<\/h2>\n\n\n\n
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\n\n\n\n\ud83e\uddea 1. Managed AI Agents with Vertex AI Agent Engine<\/h2>\n\n\n\n
\ud83d\udee0 What It Is<\/h3>\n\n\n\n
\ud83d\udccc Example: Deploying a Python Multi-Agent System<\/h3>\n\n\n\n
from adk import Agent\n\nagent = Agent(\n model=\"gemini-2.5-flash\",\n instruction=\"Answer sci-fi trivia questions\"\n)\n<\/code><\/pre>\n\n\n\n\n
adk deploy agent_engine \\\n --project=$GOOGLE_CLOUD_PROJECT \\\n --region=$GOOGLE_CLOUD_LOCATION \\\n --staging_bucket=$STAGING_BUCKET \\\n my_ai_agent\n<\/code><\/pre>\n\n\n\n\ud83d\udc47 Why Use It?<\/h3>\n\n\n\n
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\n\n\n\n\ud83c\udf00 2. Serverless Deployment on Cloud Run<\/h2>\n\n\n\n
\ud83d\udee0 What It Is<\/h3>\n\n\n\n
\u2705 HTTPS endpoints
\u2705 Zero cost when idle<\/p>\n\n\n\n\ud83d\udccc Example: Containerizing and Deploying<\/h3>\n\n\n\n
app.py<\/code>. A simple Dockerfile might look like:<\/p>\n\n\n\nFROM python:3.11\nWORKDIR \/app\nCOPY . .\nRUN pip install -r requirements.txt\nCMD [\"python\", \"app.py\"]\n<\/code><\/pre>\n\n\n\ndocker build -t gcr.io\/$GOOGLE_CLOUD_PROJECT\/my-agent .\ndocker push gcr.io\/$GOOGLE_CLOUD_PROJECT\/my-agent\n\ngcloud run deploy my-agent \\\n --image gcr.io\/$GOOGLE_CLOUD_PROJECT\/my-agent \\\n --region=us-central1 \\\n --allow-unauthenticated\n<\/code><\/pre>\n\n\n\n\ud83d\udc47 Why Use It?<\/h3>\n\n\n\n
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\n\n\n\n\u2699\ufe0f 3. Orchestrated Deployment with Google Kubernetes Engine (GKE)<\/h2>\n\n\n\n
\ud83d\udee0 What It Is<\/h3>\n\n\n\n
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\ud83d\udccc Example: Deploying with Kubernetes<\/h3>\n\n\n\n
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apiVersion: apps\/v1\nkind: Deployment\nmetadata:\n name: ai-agent\nspec:\n replicas: 3\n selector:\n matchLabels:\n app: ai-agent\n template:\n metadata:\n labels:\n app: ai-agent\n spec:\n containers:\n - name: agent\n image: gcr.io\/$GOOGLE_CLOUD_PROJECT\/ai-agent\n ports:\n - containerPort: 8080\n<\/code><\/pre>\n\n\n\n\n
kubectl apply -f ai_agent_deploy.yaml\nkubectl expose deployment ai-agent --type=LoadBalancer --port=80\n<\/code><\/pre>\n\n\n\n