GenOps Framework
Bridging the gap between tech, business & innovation
Thirty-five years ago, a group of people came together to work on a common objective: to drive digital transformation, the technological revolution of our time. The story of how people interact, collaborate, and function together, according to shared principles, to achieve common goals is called Team Management.
With their expertise recognized, the team began to answer clients’ problems through tailor-made software solutions, transforming innovative ideas into tangible outcomes. The story of solving customer challenges, surmounting obstacles, and achieving objectives while consistently prioritizing customer interests, is called Project Success.
Other similar teams in different organizations stopped advancing. However, this team started to identify patterns, reuse code, and create metamodels to optimize their production processes. They used Symbolic Generative AI to automate tasks, improve efficiency, and avoid human error. The story of moving from manual labor to technology-driven processes is called Automation.
More complex projects required dedicated, permanent teams of experts, proactive anticipation of needs, and roadmaps outlining the vision, pointing the direction, and measuring progress. The story of how projects evolve into products and how these products are designed, developed, adopted by the market, maintained, and improved is called Product Management.
Over time, as the team managed multiple projects, produced numerous products, and implemented automation, a broader understanding of their purpose, environment, and competitive advantages emerged. The story of formulating and executing these long-term plans to ensure sustainability and success is called Strategy.
Explore the GenOps framework and the fascinating intertwining stories of Team Management, Project Success, Automation, Product Management, and Strategy, seen through Quidgest’s eyes as engaged disruptors.
Generative AI: Neural vs. Symbolic
Neural Generative AI refers to a class of AI that uses neural networks to generate new content similar to the content on which it was trained. This can include text, images, music, code, and other forms of media or data.
Symbolic Generative AI, in contrast to Neural Generative AI, relies on explicitly programmed rules and logic to generate new content or data. It’s a part of AI that uses symbolic reasoning, which is logic-based, instead of pattern recognition, which is typical in neural networks.
Generative AI for Software Engineering
Generative AI has transitioned from a niche domain to a mainstream technological phenomenon in just a year. While the concept was previously intriguing to a select group of tech enthusiasts, it’s now a topic of interest for a broad spectrum of professionals and impacting everyone’s job. Thus, there’s an amplified demand for comprehensive resources on Generative AI.
However, much of the journey toward applying Generative AI to industrial-grade software engineering remains uncharted. GenOps guides you on a transformative journey about the potential of Generative AI in reshaping the landscape of software and business management.
GenOps stands out in the market by blending the technological prowess of Neural Generative AI with the controlled use of models and Symbolic Generative AI to disrupt software production.
Furthermore, GenOps merges solutions powered by the combined Generative AI with its tangible business outcomes. This duality addresses both tech and management professionals, providing them with a common language. The inclusion of real-world applications and success stories magnifies its appeal. You not only grasp the theory but also witness its business impact, benefiting from actionable insights.
Given the surge in Generative AI’s popularity, GenOps responds directly to market demand. It’s timely, relevant, and caters to the current technological momentum.
While Generative AI’s rising popularity inevitably leads to increased content creation around it, GenOps unique angle of addressing software engineering, combining Neural and Symbolic Generative AI, and merging technical know-how with business strategy ensures it stands apart.
GenOps addresses a growing need at the intersection of tech and business, harnessing the momentum of a trending technological paradigm. The blend of depth, practicality, and the unique bridge between technology and management positions it as a must-have resource in the contemporary tech-business best and next practices.
Genio, the core of GenOps
Genio is Quidgest’s platform to model management software solutions using business expertise and Neural Generative AI and to generate code using symbolic generative AI.The use of both Neural and Symbolic Generative AI in Genio provides a unique and powerful combination of capabilities. Here’s why:
- Neural AI excels at processing unstructured data, learning from experience, and dealing with uncertainty, while symbolic AI is excellent for logical reasoning, rule-based decisions, and explicit knowledge representation. By integrating both, Genio leverages the strengths of both AI worlds to deliver more comprehensive and effective solutions.
- Symbolic AI’s rule-based nature offers a clear understanding of the decision-making process. Combined with Neural AI, it provides robust solutions while still maintaining transparency and explainability. This is crucial for auditing, debugging, consistently improving, and building user trust.
- Neural Generative AI adapts to new information and learns from data autonomously. Symbolic Generative AI, on the other hand, incorporates domain knowledge and business rules directly into the system, allowing for faster adaptation to specific business requirements. This combination results in systems that evolve over time while remaining aligned with business objectives.
- Generative AI enables the automated creation of code, saving significant development time and effort. Neural AI suggests and improves small code snippets as templates, while symbolic AI generates the whole code for complex management solutions based on predefined rules, merging those templates with models.
- Symbolic AI, bundled with industrial quality concepts, avoids errors. It provides specific rules for the code generation to follow, reducing the chances of mistakes close to zero. In the unlikely event of a failure, the Mean Time to Recovery is measured in minutes.
In summary, the dual use of Neural and Symbolic Generative AI in Genio leads to systems that are robust, adaptive, comprehensible, efficient, and capable of learning from experience while conforming to specific rules and logic.
Why GenOps?
GenOps is a potent combination set to redefine the limits of what’s possible in software development. GenOps, the fusion of Patterns, Models, Neural Generative AI, Symbolic Generative AI, Product Management, and DevOps, is the next frontier.
The framework on the central pages of this QuidNews provides a multi-layered visualization of GenOps architecture, showcasing its complexity and interdependencies. The graph is organized into five distinct layers, each representing a specific focus, from strategy to team empowerment.
Each symbol scattered across these layers represents an essential attribute of GenOps and, most of the time, a disruption regarding software engineering mainstream practices. Practices that need to be changed when Generative AI, data-first modeling, and hyper-automation disturb the software development process.
While this is just a snapshot of the GenOps framework, having these layers and interdependencies present helps to grasp the bigger picture. The graph is also helpful to spread GenOps as a “kata”, not a specific solution but rather a structured approach and routine for problem-solving and continuous organization improvement, where the main driver is the people’s behavior.
