From Glue Code to Generative UI: A New Paradigm for AI Product Teams

Meta Description: From glue code to Generative UI: see how AI-native, LLM-driven interfaces boost development speed and enable adaptive user experiences for AI product teams.

Introduction

Building AI-powered products has often meant writing a lot of "glue code", those ad-hoc scripts and connectors that bind AI models to user interfaces and back-end systems. This glue code is tedious to maintain and slows down releases. Teams still operate in fragmented pipelines where product managers define features, designers create mockups, and engineers hand-code UIs to integrate AI outputs. Each handoff introduces friction and misalignment, making it nearly impossible to adapt quickly in fast-changing environments. The result is that AI product teams spend much of their time stitching components together rather than innovating.

Enter Generative UI: a new paradigm where UIs are not static artifacts but dynamic, AI-generated experiences. Instead of painstakingly coding every interface element, teams can leverage Large Language Models (LLMs) and AI frontend automation to generate user interfaces on the fly. This shift from manual glue code to AI-native software has strategic implications for how products are designed, built, and iterated. In this post, we explore why moving from glue code to Generative UI is a game-changer, and how it transforms workflows, prototyping, UX design, frontend-backend collaboration, and release velocity for forward-thinking product teams.

The Glue Code Bottleneck in AI Products

In traditional software projects, a significant amount of development time goes into glue code, the boilerplate integration code that connects disparate systems. In AI products, this often means wiring an AI model’s output to a web UI, orchestrating API calls, and manually updating interfaces based on model predictions. Such front-end glue code is not only labor-intensive, but also brittle. Every time an AI feature changes or a new use case emerges, engineers must tweak the UI and business logic by hand. This adds delay and technical debt.

Moreover, the product development workflow remains largely linear and static. A product manager writes specs, designers draw up wireframes, and frontend engineers translate those into code, often duplicating logic that's already in the AI backend. This handoff-heavy process, once acceptable for predictable feature updates, has become a liability in the era of AI. When an app’s capabilities are powered by ever-learning models or third-party AI services, a hard-coded interface can’t keep up. If your AI agent gains a new skill, you might have to manually build a UI component to expose it, creating a constant game of catch-up.

The glue code approach also stifles experimentation. For example, adding an AI-powered feature (say an LLM-driven chatbot that should display charts) might require developers to manually build new UI components and logic. It’s possible but slow. As a result, teams often limit an AI’s capabilities to what they can easily support with custom UI, so users get a basic chat window when they could have had an interactive dashboard. In short, glue code forces us to design for the lowest common denominator, constraining the potential of AI-native experiences.

What Is Generative UI?

Generative UI (short for Generative User Interface and sometimes nicknamed GenUI) is a fundamentally new approach where the user interface is dynamically generated by AI in real time, rather than pre-built by developers. In other words, the app’s UI can morph and adapt on the fly to suit each user’s needs and context (AI Interface Article). A Generative UI assembles LLM UI components tailored to each user and situation.

Instead of every user seeing the same screen layout or navigation, a generative UI uses AI “smarts” to whip up a personalized interface for each person (AI Interface Article). For example, a conventional analytics dashboard shows identical charts to every user, whereas a Generative UI could rearrange and highlight widgets based on an individual’s priorities (hiding irrelevant metrics and surfacing pertinent ones automatically).

Crucially, Generative UI is not about AI simply suggesting designs or writing code that developers then implement. It’s about the AI UI itself deciding and rendering the interface in real time for the end-user. As Thesys, a pioneer in this space, explains: a chatbot might tell you how to file an expense report, but a Generative UI will actually build the expense form for you, pre-filled and ready to submit. This represents a shift from conversational assistance to actionable UI generation. The interface becomes an intelligent agent in its own right, not just a static layer waiting for updates.

Generative UI has become feasible only recently, thanks to converging advances: smarter LLMs that better understand context, improved infrastructure for low-latency AI rendering, and a new focus on adaptive, user-specific design practices (AI Interface Article).

Rethinking Workflows and Prototyping with LLM-Driven UI

Moving to Generative UI requires product teams to rethink their workflows. Instead of a rigid sequence from design to engineering, teams can adopt a more iterative, prompt-driven process. For instance, a product manager or designer might describe a desired interface in natural language (e.g., “show a sales graph by date and region”) and let an AI UI engine generate a working prototype in seconds. Teams can spin up new UI variations by tweaking a prompt or high-level spec rather than coding from scratch, which fosters rapid experimentation and continuous improvement.

Generative UI also blurs the line between design and development. We increasingly see “design engineers” working at the intersection of UX and code, using AI tools to generate interface components and streamline implementation. With LLMs able to produce UI elements that follow established guidelines, designers can focus on high-level user flows instead of pushing pixels, and developers concentrate on guiding and integrating the AI’s outputs. In essence, design and development merge into a single generative loop.

Additionally, prototyping AI-centric features becomes far more efficient. Instead of spending weeks coding a throwaway interface for a new idea, a team can use a build UI with AI approach to get a basic UI running in hours. The result is a tighter feedback loop with users: when the UI is malleable and generated on demand, teams can tweak the experience in real time and immediately see the impact, leading to a more user-centered development process.

Implications for UX Design and Frontend-Backend Collaboration

Adopting Generative UI doesn’t eliminate the need for design; it elevates it. UX designers in an AI-native team focus on creating the frameworks and guardrails within which the AI operates. Designers define a library of approved UI components, layouts, and style rules, and the generative system uses those building blocks to assemble interfaces (AI Interface Article). The AI isn’t inventing new widgets from scratch – it’s selecting and arranging vetted components in context. Designers set the boundaries and let the AI fill in the details based on user intent, spending less time on static mockups and more on guiding adaptive patterns.

For front-end developers, Generative UI changes the nature of work. Instead of hand-coding every button or form, developers now orchestrate the AI’s output. They integrate an AI frontend API (for example, C1 by Thesys API) that returns UI components in response to a query or user action. The developer’s job is to ensure the AI has the right context and that the generated components plug into the application properly. In this model, the AI handles the routine UI creation, while developers focus on integration, quality control, and any necessary custom logic.

Collaboration between frontend and backend also improves. In the past, backend engineers would implement an AI feature and then wait for frontend developers to build a UI for it. With Generative UI, the backend can simply provide data or suggestions and let the AI dynamically create the interface, reducing back-and-forth. Everyone works from the same understanding of user intent, fostering tighter product cohesion. Gartner notes that implementing adaptive AI systems requires coordination across business, design, and engineering teams, but those that do can deliver superior and faster user experiences. In practice, product managers, designers, and developers need to jointly refine the AI UI system (for example, feeding it design libraries and usage data) and continuously improve its outputs. The interface becomes a living system that adapts without a full team rebuild for each change.

Boosting Release Velocity and Innovation

A key advantage of the Generative UI paradigm is the acceleration of release cycles. When much of your UI is generated dynamically, adding a new feature or supporting a new user scenario no longer requires weeks of front-end work. If the AI backend gains a new capability, it can immediately generate the UI elements needed to expose it. This greatly reduces the lag between back-end innovation and user-facing functionality. In fact, early studies indicate that teams using generative AI in product development are already delivering features to market faster and with higher productivity than before.

Generative UIs also enable real-time adaptation post-release. Instead of deploying a new app version to improve the UI, the AI can adjust interfaces on the fly. If you discover that users are struggling with a certain workflow, a traditional team might not address it until the next release (by adding a tooltip or redesigning a screen). By contrast, a Generative UI could respond immediately – for example, by splitting a complex form into multi-steps for those who need it, or highlighting a commonly missed button. This kind of real-time adaptive UI allows continuous improvement without the usual release overhead. It also opens the door to hyper-personalization at scale, which has been shown to improve user engagement. Gartner predicts that enterprises embracing adaptive AI systems will outperform peers by at least 25% in the pace of operationalizing AI models by 2026. By extension, product teams that leverage generative UIs can iterate faster on user experience and deliver value in smaller, more frequent increments.

Conclusion

Shifting from glue code to Generative UI represents a fundamental change in how product teams build software. It’s not just a technical update, but a strategic realignment. Rather than treating the interface as a fixed afterthought that lags behind backend AI capabilities, teams can make the UI an active, generative part of the system. This enables software that genuinely adapts to users in real time, delivering on the long-promised ideal of user-centric design with AI at the core. Workflows become more fluid, with natural language prompts and AI logic replacing rigid spec-to-design-to-code handoffs. UX design evolves to designing the rules and components for the AI to use, and development shifts toward managing AI outputs and system integration. Teams that embrace this AI-native mindset can collaborate more seamlessly and focus on higher-level product goals, leaving repetitive UI coding to the machines.

Early adopters are already seeing faster iterations, more personalized user experiences, and big boosts in team productivity. As with any paradigm shift, there will be challenges and adjustments, but the trajectory is clear. Software is moving toward autonomous interfaces, and AI product teams that embrace this shift will outpace those that remain stuck in the glue code era.

Embracing Generative UI with Thesys

Thesys, a forward-thinking infrastructure company, is at the forefront of this Generative UI movement. Its flagship product, C1 by Thesys, is a first-of-its-kind Generative UI API that lets developers build live, LLM-driven user interfaces with minimal effort. With C1, you can prompt an AI to generate real UI components (from multi-step forms to data-rich dashboards) and get back interactive elements that users can click, fill, or navigate. There’s no need to write custom frontend code for each new AI skill; the AI frontend API handles the heavy lifting. In short, C1 acts like an AI dashboard builder and interface engine, enabling a dynamic UI from a simple prompt or agent output. Product teams can integrate C1 into their apps with just a few lines of code, eliminating brittle glue code and accelerating their release velocity.

Ready to move from glue code to Generative UI? Visit Thesys to learn more about the platform, and check out the C1 API documentation to see how you can start building real-time adaptive interfaces today.

References

Brahmbhatt, K. (2025, April 3). Thesys Is Reimagining UI: One Generative Interface at a Time. Medium medium.com.

Deshmukh, P. (2025, May 8). Generative UI: The Interface that builds itself, just for you. Thesys Blog thesys.devthesys.dev.

Gnanasambandam, C., Harrysson, M., Singh, R., & Yarlagadda, C. (2024, May 31). How generative AI could accelerate software product time to market. McKinsey & Company mckinsey.com.

Karaci Deniz, B., et al. (2023, June 27). Unleashing developer productivity with generative AI. McKinsey & Company mckinsey.com.

Li, J., & Li, Y. (2024, May 14). How Generative AI Is Remaking UI/UX Design. Andreessen Horowitz a16z.com.

Brethenoux, E. (2023). Why Adaptive AI Should Matter to Your Business. Gartner Research gartner.com.