A Simple Workflow Represented in Python

Published November 11, 2025

A Simple Workflow Represented in Python

Workflows are everywhere in our daily tasks. Whether you're processing data, managing files, or running reports, you follow a series of steps to get from start to finish. Let's look at a simple workflow example in Python that shows how we can organize these steps in code.

High-Level Workflow Example

Here's a basic workflow that processes customer orders:

def process_order(order_id):
    """Process a customer order through multiple stages"""

    # Step 1: Validate the order
    order = fetch_order(order_id)
    if not validate_order(order):
        return "Order validation failed"

    # Step 2: Check inventory
    if not check_inventory(order.items):
        return "Insufficient inventory"

    # Step 3: Calculate total with taxes
    total = calculate_total(order.items)

    # Step 4: Process payment
    payment_result = process_payment(order.customer, total)
    if not payment_result.success:
        return "Payment failed"

    # Step 5: Update inventory
    update_inventory(order.items)

    # Step 6: Send confirmation
    send_confirmation_email(order.customer, order_id)

    return "Order processed successfully"

This workflow has clear steps that happen in order. Each step depends on the one before it. If any step fails, we stop and return an error message.

How This Can Be Automated

The real power comes when we automate this workflow to run without human intervention. Instead of manually processing each order, we can set up the system to handle orders automatically as they come in. This saves time and reduces errors.

Automation works best when we have:

  • Clear triggers: What starts the workflow? (new order, scheduled time, file upload)
  • Defined steps: What needs to happen and in what order?
  • Error handling: What should happen when something goes wrong?
  • Notifications: Who needs to know about successes or failures?

Example Automation Procedure

Here's how we can automate the order processing workflow:

import schedule
import time
from datetime import datetime

def automated_order_processor():
    """Automatically process pending orders"""

    # Get all pending orders from the database
    pending_orders = get_pending_orders()

    print(f"Found {len(pending_orders)} orders to process at {datetime.now()}")

    # Process each order
    for order in pending_orders:
        try:
            result = process_order(order.id)
            log_result(order.id, result, "success")
        except Exception as e:
            log_result(order.id, str(e), "error")
            notify_admin(f"Order {order.id} failed: {str(e)}")

    print("Batch processing complete")

# Schedule the automation to run every 5 minutes
schedule.every(5).minutes.do(automated_order_processor)

# Keep the automation running
while True:
    schedule.run_pending()
    time.sleep(1)

The Complete Solution

With this automation in place, here's what happens:

  1. Every 5 minutes, the system checks for new orders
  2. Each order goes through the validation and processing steps automatically
  3. Successful orders are completed without any manual work
  4. Failed orders are logged and an admin is notified
  5. Customers receive confirmation emails automatically

This simple automation can process hundreds of orders per day without human intervention. The workflow handles the routine work while people can focus on handling exceptions and improving the system.

The key to successful automation is starting simple. Begin with one clear workflow, test it thoroughly, and then expand. Before you know it, you'll have automated many of your repetitive tasks, freeing up time for more important work.

Remember: good automation doesn't replace people—it amplifies their capability by handling the repetitive tasks so they can focus on work that requires creativity and judgment.

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