Automate AI Workflows with OpenAI & Anthropic

🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI. For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here.

<span>Tutorial</span>
<span>Advanced</span>
<span>⏱ 45 min read</span>
<span>© Gate of AI 2026-07-08</span>

Learn how to automate complex AI workflows using the latest OpenAI and Anthropic APIs to enhance efficiency and scalability, with a focus on GCC/Middle East applications.

Prerequisites

• Python 3.10 or later
• OpenAI and Anthropic API keys
• Advanced understanding of AI and API integrations

What We're Building

In this tutorial, we will create an automated AI workflow that leverages the capabilities of both OpenAI's latest models and Anthropic's Claude models. This project will demonstrate how to integrate multiple AI models to handle complex tasks such as natural language understanding, sentiment analysis, and data summarization.

The final application will automate data processing tasks, reducing manual intervention and enhancing accuracy and efficiency. It will serve as a robust foundation for integrating AI into business processes, enabling scalable and intelligent solutions, particularly in the GCC/Middle East region, aligning with initiatives like Saudi Vision 2030.

Setup and Installation

To begin, we need to set up our development environment. This involves installing the necessary Python packages and configuring environment variables for API keys.

pip install openai anthropic

Next, we need to set up our environment variables to securely store our API keys. Create a .env file in your project directory with the following content:

OPENAI_API_KEY=your_openai_api_key_here

ANTHROPIC_API_KEY=your_anthropic_api_key_here

Step 1: Integrating OpenAI API

In this step, we'll integrate the OpenAI API to handle tasks such as language generation and sentiment analysis. This integration will allow us to automate the process of interpreting and generating text-based data.

from openai import OpenAI

import os

from dotenv import load_dotenv

load_dotenv()

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

def generate_text(prompt):

    response = client.chat.completions.create(

        model="gpt-4o",

        messages=[{"role": "system", "content": "You are an assistant."}, {"role": "user", "content": prompt}]

    )

    return response.choices[0].message.content

print(generate_text("Explain the benefits of AI automation."))

This code initializes the OpenAI client and defines a function generate_text that interacts with the GPT-4o model to generate responses based on the input prompt.

Step 2: Integrating Anthropic API

Next, we'll integrate the Anthropic API to utilize Claude's capabilities for tasks such as data summarization and context understanding. This will complement our OpenAI integration by providing additional AI functionalities.

from anthropic import Anthropic

anthropic_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

def summarize_text(text):

    response = anthropic_client.messages.create(

        model="claude-3-5-sonnet-20241022",

        prompt=f"Summarize the following text: {text}",

        max_tokens=150

    )

    return response.choices[0].text

print(summarize_text("OpenAI and Anthropic are leading AI research companies..."))

This segment of code sets up the Anthropic client and defines the summarize_text function, which requests a summary of the provided text from the Claude model.

Step 3: Automating Workflow Integration

In the final step, we will integrate both APIs into a unified workflow that automates the processing of data from input to output. This workflow will demonstrate the use of both models in a cohesive application.

def process_data(input_text):

    # Step 1: Generate contextual information using OpenAI

    context = generate_text(f"Provide context for: {input_text}")

# Step 2: Summarize the contextual information using Anthropic
summary = summarize_text(context)

return {
    "context": context,
    "summary": summary
}



    

    




input_data = "The impact of AI on modern industries is profound..."

result = process_data(input_data)

print("Context:", result["context"])

print("Summary:", result["summary"])

This function, process_data, orchestrates the workflow by generating context using OpenAI and summarizing it with Anthropic, thus providing a streamlined process for handling complex data tasks.

⚠️ Common Mistake: Ensure that your API keys are correctly set up in the environment variables. Misconfigured keys can lead to authentication errors that are difficult to debug.

Testing Your Implementation

To verify the workflow, run the script and check the output. You should see a detailed context and a concise summary of the input text, demonstrating the effective integration of both AI models.

# Command to run the script

python automate_workflow.py

What to Build Next

• Extend the workflow to include more complex data processing tasks such as classification and anomaly detection.
• Integrate a database to store and retrieve processed data efficiently.
• Build a web interface to allow users to input data and receive AI-generated insights in real-time.