You’ve built an intelligent AI agent — it works locally, it’s smart, and it solves real problems. But now comes the big leap: deploying that agent so it runs securely, reliably, and at scale in the cloud.

Google Cloud’s official blog outlines three hands-on labs to help you deploy AI agents using different cloud platforms. Each offers a trade-off between simplicity, control, and scalability — and each is ideal for a specific stage of your production journey. (Google Cloud)

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🧠 Why You Need Deployment Options

Before diving into the labs, let’s set the stage. When moving an AI agent from development into production, you need to think about:

• Scalability — Can your agent serve many users at once?
• Operational overhead — Do you want to manage servers and infrastructure?
• Flexibility — Do you want complete control over the deployment stack?
• Cost efficiency — Are you paying for idle compute or only when needed?

Google Cloud gives you three deployment targets:

1. Managed Runtime with Agent Engine
2. Serverless Containers with Cloud Run
3. Orchestrated Deployment with Google Kubernetes Engine (GKE) (Google Cloud)

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🧪 1. Managed AI Agents with Vertex AI Agent Engine

Best for: Developers who want to deploy Python agents with minimal infrastructure to manage.

🛠 What It Is

The Vertex AI Agent Engine lets you deploy your agent without provisioning servers or containers. It’s a fully managed endpoint tailored for Python agents built using the Agent Development Kit (ADK). (Google Cloud)

📌 Example: Deploying a Python Multi-Agent System

Let’s say you’ve written a multi-agent assistant using the ADK framework:

from adk import Agent

agent = Agent(
    model="gemini-2.5-flash",
    instruction="Answer sci-fi trivia questions"
)

To deploy this agent using Agent Engine:

1. Ensure your project and billing are set up in Google Cloud.
2. Use the ADK deploy command:

adk deploy agent_engine \
  --project=$GOOGLE_CLOUD_PROJECT \
  --region=$GOOGLE_CLOUD_LOCATION \
  --staging_bucket=$STAGING_BUCKET \
  my_ai_agent

This uploads your code to Vertex AI, where Google manages execution, scaling, and session state. (Google Cloud Documentation)

👇 Why Use It?

• No container builds
• Sessions and memory managed automatically
• Integrated with Vertex AI services

Perfect for getting up-and-running quickly with running AI agents. (Google Cloud)

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🌀 2. Serverless Deployment on Cloud Run

Best for: Maximum flexibility without server management + support for multiple languages.

🛠 What It Is

Cloud Run lets you deploy your agent as a containerized service. It automatically handles:

✅ Auto-scaling
✅ HTTPS endpoints
✅ Zero cost when idle

It’s language-agnostic, so your agent can be in Python, Go, Java, or Node.js. (Google Cloud)

📌 Example: Containerizing and Deploying

Assume you have an AI agent in app.py. A simple Dockerfile might look like:

FROM python:3.11
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "app.py"]

Then build and deploy:

docker build -t gcr.io/$GOOGLE_CLOUD_PROJECT/my-agent .
docker push gcr.io/$GOOGLE_CLOUD_PROJECT/my-agent

gcloud run deploy my-agent \
  --image gcr.io/$GOOGLE_CLOUD_PROJECT/my-agent \
  --region=us-central1 \
  --allow-unauthenticated

Cloud Run will spin up instances when requests arrive and scale them down when idle — keeping costs optimized. (Google Cloud Documentation)

👇 Why Use It?

• Supports any language or custom runtime
• Integrates easily with CI/CD pipelines
• Perfect for APIs serving agent responses

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⚙️ 3. Orchestrated Deployment with Google Kubernetes Engine (GKE)

Best for: Teams needing fine-grained control over deployment, autoscaling, networks, and multi-service setups.

🛠 What It Is

GKE lets you run your agent inside a Kubernetes cluster with full control over:

• Pod configurations
• Resource quotas
• Autoscaling rules
• Networking policies

This is ideal for complex AI systems using multiple interconnected services. (Google Cloud)

📌 Example: Deploying with Kubernetes

1. Create a Kubernetes deployment manifest:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-agent
  template:
    metadata:
      labels:
        app: ai-agent
    spec:
      containers:
      - name: agent
        image: gcr.io/$GOOGLE_CLOUD_PROJECT/ai-agent
        ports:
        - containerPort: 8080

2. Deploy to GKE:

kubectl apply -f ai_agent_deploy.yaml
kubectl expose deployment ai-agent --type=LoadBalancer --port=80

You now have a scalable, resilient agent deployment managed by Kubernetes. (Google Cloud)

👇 Why Use It?

• Best for complex enterprise workloads
• Fine control over autoscaling and cost
• Easy integration with observability and networking

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📊 Choosing the Right Path: When to Use What

Deployment Path	Best Use Case	Key Benefit
Agent Engine	Quick Python agent deployment	Fully managed, minimal ops
Cloud Run	Flexible, language-agnostic API	Serverless scaling
GKE	Complex, multi-service AI systems	Full operational control

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🧠 Final Thoughts

Moving your AI agent from a prototype to production isn’t just about writing code — it’s about choosing the right cloud platform, understanding operational trade-offs, and preparing your agent for real-world traffic and security.

Google Cloud’s trio of hands-on labs gives you practical experience on all major deployment paths:

• Fully Managed → Vertex AI Agent Engine
• Serverless → Cloud Run
• Orchestrated → GKE (Google Cloud)

Each path offers a unique combination of performance, flexibility, and ease of use — so you can pick the one that’s right for your project and team.

Happy deploying! 🚀