GetAutonomousAI
Complete Deployment Guide

Deploy Your AI Agent
in 30 Minutes

Step-by-step instructions to get your custom AI agent running in production. No DevOps experience required.

System Requirements

Python
3.9 or higher
Required
RAM
2GB minimum, 4GB recommended
Required
Storage
10GB available space
Required
Network
Stable internet connection
Required
OpenAI API Key
For AI functionality
Required
Integration APIs
Based on your specific agent
Optional

6-Step Deployment Process

Follow these steps to deploy your AI agent to production

1

Receive Your Agent Code

Within 4 hours of payment, you'll receive an email with:

Complete Python source code
requirements.txt with all dependencies
Custom README with your specific setup
Environment variable template (.env.example)
Docker configuration (optional)
Testing scripts and examples
2

Set Up Your Environment

Prepare your server or local machine:

Python 3.9+ installed
pip or conda package manager
Git for version control
Text editor or IDE
Access to your integration APIs (if applicable)
# Install Python dependencies
pip install -r requirements.txt

# Or use virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
3

Configure API Keys

Set up your environment variables:

Copy .env.example to .env
Add your OpenAI API key
Add integration API keys (ChiroTouch, Google, etc.)
Configure webhook URLs if needed
Set up email/SMS service credentials
# Example .env file
OPENAI_API_KEY=sk-...your-key-here
GOOGLE_CALENDAR_API_KEY=...
CHIRO_TOUCH_API_KEY=...
TWILIO_ACCOUNT_SID=...
TWILIO_AUTH_TOKEN=...
SMTP_HOST=smtp.gmail.com
SMTP_PORT=587
[email protected]
SMTP_PASSWORD=your-app-password
4

Test Locally

Run your agent in development mode:

Start the agent with test mode enabled
Verify API connections work
Test with sample data
Check logs for any errors
Validate output format
# Run in test mode
python agent.py --test

# Run with verbose logging
python agent.py --verbose

# Test specific function
python test_booking.py
5

Deploy to Production

Choose your deployment method:

Deploy to your own server (VPS, dedicated)
Use cloud platforms (AWS, Google Cloud, Azure)
Deploy with Docker for easy management
Set up process manager (PM2, systemd)
Configure automatic restarts
Set up monitoring and alerts
# Deploy with Docker
docker build -t my-ai-agent .
docker run -d --env-file .env my-ai-agent

# Or use PM2 for Node.js-style process management
pm2 start agent.py --name "booking-agent"
pm2 save
pm2 startup
6

Monitor & Maintain

Keep your agent running smoothly:

Check logs regularly for errors
Monitor API usage and costs
Update dependencies periodically
Back up your configuration
Scale resources as needed
Contact us for support if issues arise
# View logs
tail -f logs/agent.log

# Check process status
pm2 status

# Monitor resource usage
htop  # or top on Linux

Deployment Options

Choose the deployment method that fits your infrastructure

Self-Hosted Server

Deploy on your own VPS or dedicated server

Full control
No vendor lock-in
Predictable costs
Best for:
For businesses with existing infrastructure

Cloud Platform

Use AWS, Google Cloud, or Azure

Auto-scaling
High availability
Managed services
Best for:
For businesses expecting growth

Docker Container

Containerized deployment anywhere

Portable
Consistent environment
Easy updates
Best for:
For teams familiar with containers

Need Help with Deployment?

Every agent comes with personalized deployment support. If you run into issues, we're here to help you get up and running.

Ready to Deploy Your AI Agent?

Get production-ready code with complete deployment instructions in 4 hours