Deploying Your AI Agent › Deploying to the Cloud

For cloud deployment, you have many options. Let's walk through a straightforward path using a platform-as-a-service provider, which handles much of the infrastructure complexity for you.

First, add a file that tells the platform how to run your application. For example, if using Heroku, you'd create a Procfile:

web: python assistant.py

Or if you're using Docker (a containerization tool that packages your app with all its dependencies), you'd create a Dockerfile:

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "assistant.py"]

This Docker container includes everything your agent needs to run, making deployment consistent across different environments.

Most platforms follow a similar pattern:

1. Create an account and a new project
2. Connect your code repository (GitHub, GitLab, etc.)
3. Configure environment variables (your API keys)
4. Deploy with a single command or button click

For example, with Railway:

The platform builds your application, starts it running, and gives you a URL where it's accessible. Your agent is now live on the internet.

Once deployed, you need visibility into what's happening. Most platforms provide logs you can view in their dashboard or via command-line tools: