AI Workflow Automation with OpenAI & Anthropic

What We’re Building

In this tutorial, we will create a sophisticated AI workflow automation system that leverages the power of OpenAI’s GPT-5.2 and Anthropic’s Claude 3.5 Sonnet models. This system will be capable of performing complex tasks such as automated code review, hypothesis generation, and multi-step process execution efficiently.

The finished project will automate the AI research lifecycle, integrating model outputs with visualization tools and providing feedback loops for continuous improvement. This system is designed to enhance productivity and decision-making in research environments by automating repetitive and computationally intensive tasks.

Setup and Installation

To begin, we need to set up our development environment by installing the necessary libraries and configuring our environment for API access. This involves installing the OpenAI and Anthropic SDKs and setting up authentication using API keys.

pip install openai anthropic

Next, we need to configure 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
ANTHROPIC_API_KEY=your-anthropic-api-key

Step 1: Initialize the AI Clients

In this step, we will initialize the clients for OpenAI and Anthropic APIs. This setup allows us to easily interact with the models and make requests for tasks such as text completion and code generation.

import os
from openai import OpenAI
from anthropic import Anthropic

# Load API keys from environment variables
openai_api_key = os.getenv('OPENAI_API_KEY')
anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')

# Initialize OpenAI and Anthropic clients
openai_client = OpenAI(api_key=openai_api_key)
anthropic_client = Anthropic(api_key=anthropic_api_key)

We start by importing the necessary libraries and loading the API keys from our environment variables. Then, we create instances of the OpenAI and Anthropic clients using these keys, which will be used for subsequent API calls.

Step 2: Implement Automated Code Review

Now, we’ll implement a function that uses OpenAI’s GPT-5.2 to perform automated code reviews. This function will take a code snippet as input and return a critique of the code, identifying potential improvements or errors.

def automated_code_review(code_snippet):
    response = openai_client.chat.completions.create(
        model="gpt-5.2",
        messages=[
            {"role": "system", "content": "You are a code review assistant."},
            {"role": "user", "content": f"Please review the following code:\n{code_snippet}"}
        ]
    )
    return response.choices[0].message['content']

# Example usage
code_example = "def add(a, b):\n    return a + b\n"
review = automated_code_review(code_example)
print("Code Review Feedback:", review)

This function constructs a chat completion request to the GPT-5.2 model. It sets the role of the assistant as a “code review assistant” and passes the user’s code snippet for review. The response contains the review feedback which we print to the console.

Step 3: Hypothesis Generation with Claude 3.5 Sonnet

In this step, we’ll use Anthropic’s Claude 3.5 Sonnet to generate hypotheses based on provided research data or context. This functionality can be particularly useful in automating the ideation phase of research projects.

def generate_hypotheses(context):
    response = anthropic_client.messages.create(
        model="claude-3.5-sonnet",
        messages=[
            {"role": "system", "content": "You are a research assistant."},
            {"role": "user", "content": f"Generate hypotheses for the following context:\n{context}"}
        ]
    )
    return response.choices[0].message['content']

# Example usage
research_context = "Exploring the effects of climate change on marine biodiversity."
hypotheses = generate_hypotheses(research_context)
print("Generated Hypotheses:", hypotheses)

Similar to the previous step, we send a request to the Claude 3.5 Sonnet model to generate hypotheses based on the provided context. The system role is defined as a “research assistant” to guide the model’s output.

Testing Your Implementation

To verify that our AI workflow automation system is functioning correctly, we will test each component individually. Ensure that the code review and hypothesis generation functions return expected outputs for different inputs.

# Test code review function
print(automated_code_review("def subtract(a, b):\n    return a - b\n"))

# Test hypothesis generation function
print(generate_hypotheses("Analyzing the impact of AI on workforce dynamics."))

Run these tests to ensure that the functions are interacting with the APIs correctly and that the responses are logical and relevant to the inputs provided.

What to Build Next

• Integrate a visualization tool to graphically represent hypothesis results and code review metrics.
• Extend the workflow to include automated documentation generation for code and research outputs.
• Develop a dashboard interface to manage and monitor AI workflow processes in real-time.