CQRS and OpenAI: architectural patterns for command-oriented systems and intelligent responses

Introduction

In recent years, the evolution of software architectures and the growing demand for systems that can handle large volumes of data and real-time operations have led to increased interest in architectural patterns such as CQRS (Command Query Responsibility Segregation) and integration with artificial intelligence technologies, such as OpenAI. This article explores how these approaches can be combined to create more robust and efficient systems.

What is CQRS?

CQRS is an architectural pattern that separates reading and writing data in a system. This approach allows different models and techniques to be applied for read and write operations, optimizing the performance and scalability of the system (SABBAG FILHO, 2025).

Principles of CQRS

The main principles of CQRS include:

• Separation of Responsibilities: Commands and queries are handled separately.
• Scalability: Allows scaling parts of the system independently.
• Flexibility: Different data models can be used for reading and writing.

Example of Implementation in C#

Below is a basic example of CQRS implementation in C# using an order management system:

public class Order 
{
    public int Id { get; set; }
    public string Product { get; set; }
    public int Quantity { get; set; }
}

public interface ICommand 
{
    void Execute();
}

public class CreateOrderCommand : ICommand 
{
    private readonly Order _order;

    public CreateOrderCommand(Order order) 
    {
        _order = order;
    }

    public void Execute() 
    {
        // Logic to save the order in the database
    }
}

public interface IQuery<T> 
{
    T Execute();
}

public class GetOrderByIdQuery : IQuery<Order> 
{
    private readonly int _orderId;

    public GetOrderByIdQuery(int orderId) 
    {
        _orderId = orderId;
    }

    public Order Execute() 
    {
        // Logic to fetch the order from the database
        return new Order(); // Simplified example
    }
}

What is OpenAI?

OpenAI is an organization that develops artificial intelligence technologies, including the GPT-4 language model, which is capable of generating text and performing various tasks related to natural language (OPENAI, 2025). Integrating OpenAI into CQRS systems can provide an additional layer of intelligence and automation.

Applications of OpenAI in CQRS Systems

The applications of OpenAI in systems utilizing CQRS include:

• Generation of Intelligent Responses: Use language models to respond to complex queries.
• Automation of Commands: Commands that benefit from decisions based on historical data.

Example of Integration in C#

An example of how OpenAI can be integrated into a CQRS system could be the automation of a command using a language model:

public class GenerateResponseCommand : ICommand 
{
    private readonly string _input;

    public GenerateResponseCommand(string input) 
    {
        _input = input;
    }

    public void Execute() 
    {
        var response = OpenAIApi.GenerateResponse(_input);
        // Logic to process the generated response
    }
}

public static class OpenAIApi 
{
    public static string GenerateResponse(string input) 
    {
        // Call to the OpenAI API to generate a response
        return "Response generated by OpenAI"; // Simplified example
    }
}

Benefits of Integrating CQRS and OpenAI

Integrating CQRS with OpenAI offers several benefits, such as:

• Improved User Experience: Faster and more accurate responses to complex queries.
• Process Automation: Reducing manual workload through intelligent automation.
• Increased Scalability: The ability to scale queries and commands independently, leveraging artificial intelligence.

Challenges and Considerations

Despite the benefits, there are also challenges to consider:

• Complexity: Implementing CQRS and integrating with OpenAI can increase the complexity of the system.
• Cost: Using the OpenAI API may have associated costs that need to be managed.
• Latency: Calling external APIs may introduce latency in system operations.

Mitigating Challenges

Some strategies to mitigate these challenges include:

• Using caches to store frequent responses.
• Implementing an event-driven architecture to decouple components.
• Monitoring and optimizing system performance regularly.

Case Studies

Several case studies demonstrate the effectiveness of combining patterns such as CQRS with OpenAI. A notable example is Monty 2.0, the institutional chatbot of Montgomery County (Maryland), which clearly separates knowledge retrieval (queries), using Azure AI Search, from generating intelligent responses via the OpenAI model (commands/processing) (MICROSOFT, 2025).

Example of a Case Study: Customer Support Chatbot

A customer support chatbot can use CQRS to process queries efficiently. When receiving a command from a customer to check the status of an order, the system can use OpenAI to interpret the query and fetch the necessary information:

public class CheckOrderStatusCommand : ICommand 
{
    private readonly string _customerQuery;

    public CheckOrderStatusCommand(string customerQuery) 
    {
        _customerQuery = customerQuery;
    }

    public void Execute() 
    {
        var orderId = OpenAIApi.ExtractOrderId(_customerQuery);
        var order = new GetOrderByIdQuery(orderId).Execute();
        // Logic to return the order status
    }
}

Conclusion

The combination of CQRS and OpenAI represents a significant opportunity to develop smarter and more responsive systems. By separating the responsibilities of commands and queries and integrating artificial intelligence, organizations can create solutions that not only meet current demand but also prepare for the future.