Generative AI - The Next Generation of Middleware

The opinions in this article are my own and do not necessarily reflect the opinions of my employer.

Middleware has long been an unsung hero in enterprise application development. While sometimes overshadowed by flashy UIs or cloud-based going-to-solve-all-of-your-business-problems back-end systems, middleware plays a crucial role in connecting applications to enterprise data and processes. Traditional middleware platforms have served their purpose well, however, the rapidly evolving technology landscape calls for lower-cost solutions, easier development and maintenance, and adaptive/self-repairing.

Enter Generative AI - a technology set to revolutionize middleware development. Generative AI leverages machine learning algorithms and natural language processing and can generate designs, data models, configuration, code, and even entire software applications. In the middleware context, it promises to transform how applications and systems communicate, lowering costs, increasing efficiency, and automating adaptability.

The opportunity lies not just in Generative AI middleware's potential to expedite development by interpreting natural language descriptions of business needs and what needs to be built to address those needs but also in its ability to consume and remember all of the documentation required for solution delivery. As Generative AI middleware navigates its way through the documentation for complex matrices of public and proprietary products, applications, systems, and the related APIs of data and processes, it will learn, adapt, and even repair itself, promising a future of self-managed, resilient, and connected systems. The dawn of Generative AI middleware is here, opening the door to a new era of enterprise IT middleware and application development.

Generative AI is an innovative subfield of artificial intelligence that employs machine learning models to produce new content, effectively 'generating' data, and it can be argued, ?information, knowledge, and wisdom (DIKW). It's the technology behind creations like AI-composed music, AI-written documents, and, in this context, AI-driven middleware development.

Generative AI middleware will be designed to employ these generative capabilities in enterprise environments. Instead of grinding through today’s processes for design, development, testing, and deployment, middleware delivery teams will describe in natural language the business need and what they want to deliver. The Generative AI middleware, leveraging natural language processing, will interpret and translate these descriptions into the needed work product. For example, a middleware team that has a task to ensure that new accounts created in SAP are also created in Salesforce would work with the Generative AI middleware to design this solution and then for all three systems, write the epics and user stories, write the configuration and code, write the test data, write the manual and automated unit test scripts, run the tests, deploy the solution to the system test environment, run system tests, and deploy the solution to production. Work that spans three teams, takes planning, prioritization negotiation, and budget negotiation, and runs across calendar months will, by leveraging Generative AI middleware, be completed in weeks or even days. This symbiosis between human and AI will enable a much more streamlined middleware delivery process, drastically reducing the time and effort required to build the middleware components of enterprise applications.

What are the significant differences between today’s middleware and Generative AI middleware development?

Traditionally, middleware development has been a time-intensive and detail-oriented series of tasks with differing perceptions of team responsibilities. Middleware teams see their responsibility as the intake of well-defined data fields, the output of well-defined data fields, and perhaps a little bit of data transformation. In the example above, the Salesforce and SAP teams see their role as writing callouts and web services or simply allowing the middleware to directly perform DML operations on their data and to call their methods. Each team specializes in their technology and focuses on what they do with their technology. This leaves a gap between what the data means, the business needs the methods address, and the capacity and performance of each system when pushing data back and forth. This gap is addressed with a voluminous set of epics and user stories, documentation, such as functional design documents, technical design documents, and interface control documents, multiple meetings with representatives from all three teams, system integration testing, and end-to-end testing.

The advent of Generative AI middleware will change this approach.

Generative AI has a comprehensive and complete memory. It can consume the complete set of formal product documentation, public Data Information Knowledge Wisdom (DIKW) about the products being used, proprietary DIKW about how the products have been configured, customized, and are being used, and all ongoing plans and schedules for updates and further configuration and customization.

The ability of the business subject matter experts, product owners, and business analysts to use natural language to describe their business needs and decompose those needs to actionable product configuration and customization reduces the prerequisite knowledge required and will make the development process more accessible. The phrase “citizen developer” has been used, and perhaps overused, across the last decade, but with Generative AI middleware its use is reasonable. Citizen developers will be able to use Generative AI middleware to decompose business needs into actionable activities and then generate, test, and refine configuration and customization activities at a pace far beyond human-only capabilities.

APIs are crucial enablers of system interoperability.

APIs provide defined methods of communication between diverse systems and applications, enabling them to interact and exchange data. API interactions thus play a significant role in the operation and functionality of enterprise software applications. Generative AI middleware takes this communication to the next level. By 'consuming' an API description, Generative AI middleware will have a deep understanding of the API’s underlying data and the processes exposed. The Generative AI middleware can then leverage this understanding to interact correctly and efficiently with the API. It can optimize the middleware’s behavior in response to the interactions with the API, enhancing the efficiency of enterprise application operations. These interactions will also facilitate effective communication between applications and systems, optimizing performance and ensuring smooth functionality.

Generative AI middleware's ability to comprehend and adapt to API documentation creates a dynamic development and operational environment. As APIs and the data and methods behind them evolve and change, the Generative AI middleware can automatically adjust its interactions to accommodate these changes, reducing downtime and unplanned maintenance. This attribute signifies a significant leap forward in automating and optimizing software development and maintenance.

One of the game-changing features of Generative AI middleware will be its self-repairing capability, promising a future where it not only detects and reports errors but also fixes them on the fly. The self-repair mechanism is a testament to Generative AI's adaptability. Upon encountering an error, Generative AI middleware will review the corresponding documentation. It will use this information to analyze the error, deduce its origin, and formulate a solution to remediate it. This remarkable ability means that Generative AI middleware can proactively maintain system integrity, significantly reducing the frequency and duration of unscheduled downtime.

For lower-risk errors, the Generative AI middleware will autonomously implement fixes. For more complex errors, when the Generative AI middleware develops a solution to an error, it can place the solution in a queue for human review and approval. This process will ensure that AI-generated solutions align with business goals and quality standards, underscoring the continued importance of human judgment in the process. As time advances and confidence in AI technology grows, the scope of self-repair autonomy will expand, providing enterprises with an ever more resilient, self-reliant, and efficient middleware solution.

The dawn of Generative AI middleware ushers in a transformative era in enterprise application development. It promises to redefine traditional delivery and operations methodologies, intertwining the capabilities of AI with human ingenuity to deliver faster, more efficient, and adaptable middleware. Generative AI middleware doesn't simply hint at a technological shift; it signals a paradigm shift that will touch every aspect of enterprise application development.

The journey into this new world will not be without challenges. Businesses will need to navigate considerations around costs, skills, data privacy, ethics, and transparency. The launch and evolution of Generative AI middleware will be a shared journey where product vendors, business users, developers, testers, and AI experts will all come together to shape the path forward.

Finally, keep in mind that as Ajay Agrawal , Joshua Gans , and Avi Goldfarb describe in their book Power and Prediction , Generative AI middleware will be an application solution for the between times. It is not the end state for middleware but simply another step in the journey to connect people and applications to data, information, knowledge, and wisdom.

If you made it this far, three questions for you:

1. What do you think about the potential of Generative AI middleware?
2. How do you envision it transforming enterprise application development?
3. What hurdles must be addressed/overcome before enterprise data flows through Generative AI middleware?