The Evolution of AI App Interfaces: Past, Present, and Future

Parikshit Deshmukh

June 19th, 2025⋅9 mins read

Introduction
User interfaces have come a long way (Bridging the Gap). This gap between 74% of companies have yet to see tangible value from their , with poor user adoption being a key culprit (Bridging the Gap). The lesson is clear:

To build truly (Bridging the Gap). Enter Generative UI, a new paradigm where UIs are dynamically created by LLM-driven components, real-time adaptive UI, and frontend automation could redefine how we interact with software. We’ll look at how we moved from static dashboards and forms, through early chatbots, to today’s nascent generative interfaces. We’ll also examine what’s on the horizon: that let apps generate their own UI from a prompt, and the dawn of truly adaptive, context-aware user experiences. By understanding this trajectory, developers, designers, and tech leaders can prepare for the next era of UI/UX building UI with becomes the norm rather than the exception.

Static Interfaces: The Era of Dashboards and Forms (Past)

In the past, user interfaces followed a static paradigm. Applications were built around fixed screens, dashboards, and data forms that were manually crafted by developers and designers. These traditional UIs assumed relatively stable requirements; once a screen or form was designed, it rarely changed unless an update was deployed. For users, this meant navigating predefined menus, filling out static forms, and viewing information through rigid dashboards. Such interfaces worked fine when software functionality was predictable, but they were inherently limited in flexibility. If a user needed a new view or custom report, it often required someone to redesign the UI or build a new feature.

This static approach made UIs labor-intensive to build and maintain. Developers had to write code for every button, dialog, and layout, and updating the UI meant another development cycle. As a result, businesses sometimes avoided changes, even when user needs evolved, because changing the UI was costly and slow (Bridging the Gap). In the era of enterprise dashboards, for example, product teams would try to anticipate all the key metrics a user might want, then lay them out on a fixed panel. But if a user’s question fell outside those predefined charts, the interface couldn’t adapt on the fly.

Early attempts to introduce flexibility often came in the form of configuration panels or custom report builders embedded in software. These were useful but still bound by what the original developers had foreseen. The interface remained static by default, reactive by design manual updates and predefined layouts driving every change. In short, the past was dominated by UI that was predictable, but not very smart.

Early

Even before today’s conversational interface Conversational UIs marked a shift from clicking buttons toward simply asking for what you need.

However, these early chat-style UIs were limited. Many were rule-based or relied on simple scripts, meaning they could handle only narrow scenarios. For example, a customer support bot might guide users through a fixed decision tree (essentially an automated form disguised as chat). If the user’s query fell outside its script, the bot hit a dead end. Thus, while chatbot interfaces offered a glimpse of a more dynamic,

It’s important to note that these early conversational UIs did not fundamentally change how the rest of the interface worked. Outside the chat window, the application was still static. A chatbot could suggest how to file an expense report, but it couldn’t actually open the expense form in your UI unless a developer had explicitly wired that up. In short, early (Generative UI Is Not a Text-Only Chatbot): As one expert put it, a *chatbot can suggest how to file an expense, but Generative UI can build the actual expense form In the next section, we’ll see how the present state of

The Shift to

Today, we are witnessing a major shift in how UIs are built for Large Language Models (LLMs), have unlocked a new possibility: instead of just responding with text or numbers, the design, where the UI is not entirely pre-coded but is partially assembled in real time based on (Bridging the Gap).

How does this work in practice? Rather than having a static set of screens, developers now define a set of LLM-interpretable UI components structured instructions that describe which UI components to display. In simpler terms, the JSON data saying “show a line chart of sales over time” or “generate a form with fields A, B, C” (Bridging the Gap). A rendering engine on the frontend (often a JavaScript/React layer) then takes these instructions and automatically builds the interface for the user (Bridging the Gap).

This approach is often called frontend automation. Instead of a developer hand-coding every dialog, chart, or workflow step, they can use generative user interface (Bridging the Gap). For example, if a user asks an analytics app, “Show me the top 5 products by growth this month,” a generative UI system might decide to display a bar chart answering that query. If the user then asks to see those results in a table, the

A concrete illustration of this is the idea of an . Traditionally, dashboards are built by developers who decide which charts or metrics to show. In an instead of pre-defining every dashboard view, users can request custom analytics and get an interactive chart instantly (Bridging the Gap). The front-end doesn’t need a pre-built “screen” for that specific request LLM UI components are assembled dynamically. Companies adopting this approach have seen a significant acceleration in development: Gartner predicts that by 2026, organizations using Bridging the Gap).

Crucially, this generative UI approach means the interface can adapt continuously. The UI is no longer a static backdrop; it’s part of the intelligent behavior of the app. If the underlying function calling and plugins, allowing it to return rich outputs or trigger UI-like elements instead of just text responses (Bridging the Gap). This blurs the line between “conversation” and “application,” as chatbots gain the ability to produce interactive content (buttons, images, etc.) within the chat. Meanwhile, libraries like CopilotKit and LangChain UI are enabling developers to connect LLMs to UI frameworks (Bridging the Gap).

Perhaps the most direct example of the present shift is C1 by Thesys, which is described as a Generative UI API or . C1 by Thesys lets developers send prompts to an Bridging the Gap). In essence, it’s an Open (Bridging the Gap). The returned UI specification is then interpreted by a React-based SDK to display actual components on the user’s screen (Bridging the Gap). This means a developer can build UI with by simply describing what the interface should do in a prompt, and letting C1 by Thesys handle the heavy lifting of layout and component generation (Bridging the Gap). As InfoWorld reported, “C1 lets developers turn LLM outputs into dynamic, intelligent interfaces in real time” (Bridging the Gap). C1 by Thesys effectively automates a huge portion of frontend work for Bridging the Gap).

Already, over 300 teams are using generative UI tools in production, speeding up their releases and reducing manual UI coding (Thesys Introduces C1 by Thesys workflows, enterprise teams creating , and developers adding intelligent agent UIs to their apps. With C1 by Thesys, for example, one team turned what used to be a static chatbot into a rich, interactive support assistant that can show images, forms, and data visualizations to users without any front-end overhaul. The key takeaway for the present is that UI design is becoming a collaborative process between humans and

Future Interfaces: Real-Time Adaptive UI and

Looking ahead, the way we interact with software could be transformed by interfaces that are highly adaptive, context-aware, and generative. In the future, we can expect living interfaces that assemble on-the-fly to best serve the task at hand.

One major trend will be the rise of real-time adaptive UI. This means the interface will adjust instantly as conditions change without the user explicitly asking. Early research already demonstrates UIs that modify themselves based on context (Firestorm Consulting, 2024). Future could take this further, combining user behavior analytics with generative design so that each user gets a truly personalized application interface.

To power such experiences, we will likely see the maturation of . These are services or frameworks (like C1 by Thesys) that handle the complex task of turning Generative User Interface capabilities out of the box. In effect, these platforms function as an “interface engine” driven by “ may become as common in conversation as “web framework” is now, as product teams realize they need this layer to deliver truly intelligent and flexible UX.

Another exciting aspect of future UIs is how they will enable interaction with . Today, building a user interface for an autonomous build its own dashboard to show you its progress on a task, updating it in real time as it works. We’re already seeing the first patterns of this “agentic UI.” A Medium tech article described it as giving users a colleague, not just a static tool, by allowing the (Sgobba, 2025). Frontends for collaborative workspaces shared between the human and the

From a design perspective, these changes herald a new creative era. Developers and designers will need to establish guardrails and design systems so that generative UIs remain coherent and on-brand. We might have style guidelines and constraints (colors, typography, allowed component types) to ensure a consistent look and feel (Bridging the Gap). Design tooling is likely to evolve to support this: imagine design prototypes that include “Bridging the Gap).

Ultimately, the future of magical to users. Software will no longer be a static sequence of pages, but a shapeshifting assistant that molds itself to each user’s context. Generative UI has been called “the biggest shift since the advent of the graphical user interface”, and it’s easy to see why. It changes the role of UI from a passive, pre-built medium to an active, outcome-oriented (Bridging the Gap)

Of course, reaching this future will require continued advances in (Bridging the Gap). The tools are already emerging, and as they mature, they promise to redefine how we design and experience digital products.

Conclusion
The journey of fixed maps of an application’s capabilities; today, they are starting to become responsive conversations, and tomorrow they could evolve into contextual collaborators. Traditional dashboards and forms gave us consistency but couldn’t adapt. Early chatbots added interactivity but lived largely separate from the rest of the UI. Now, generative UI and LLM-driven components are blending the power of

Thesys’ introduction of C1 by Thesys, the first Generative UI API, is a bellwether of this new era. It signifies that the technology to make UIs adaptive and intelligent is here and ready for practical use. With solutions like C1 by Thesys, teams can dramatically accelerate product design and development by letting .

Final Paragraph
Forward-thinking teams don’t have to wait for the future to arrive C1 by Thesys, a developer-friendly API to generate UIs from LLM outputs. Imagine turning an idea or prompt directly into a working interface: with C1 by Thesys, it’s as simple as calling an API. Whether you want to create a smart that assembles charts on demand, design multi-step workflows that adapt to user input, or build an agent UI that lets users collaborate with an visit Thesys.dev to learn more or check out the documentation. Don’t ship your

References

  • Krill, Paul. “Thesys introduces generative UI API for building InfoWorld, 25 Apr. 2025.
  • Thesys Introduces C1 to Launch the Era of Generative UI (Press Release). Business Wire, 18 Apr. 2025.
  • Thesys. Bridging the Gap Between . Thesys Blog, 10 Jun. 2025
  • Firestorm Consulting. "Rise of AI Agents." Firestorm Consulting, 14 June 2025. Vocal Media.
  • Louise, Nickie. “Cutting Dev Time in Half: The Power of TechStartups, 30 Apr. 2025.
  • Brahmbhatt, Khyati. “Generative UI: The Medium, 19 Mar. 2025
  • Deshmukh, Parikshit. . Thesys Blog, 11 Jun. 2025
  • Firestorm Consulting. "Stop Patching, Start Building: Tech’s Future Runs on LLMs." Firestorm Consulting, 14 June 2025. Vocal Media.
  • Firestorm Consulting. "The Builder Economy’s AI-Powered UI Revolution." Firestorm Consulting, 18 June 2025. Vocal Media.

Meta Description: These use cases all share a common thread: they leverage LLM-driven product interfaces that adapt in real time to user intent. Generative UI shines most in scenarios where users benefit from a custom or context-specific interface that would be impractical to pre-build. As more companies experiment with this, we’re likely to see even more creative applications, from education apps that generate personalized lesson UIs to gaming or creative tools where the interface morphs based on what the user is trying to achieve.

FAQ

Q1: What is Generative UI in simple terms?
A1: Generative UI (GenUI) refers to a new approach where the user interface is partly generated by an automated front end (AI Native Frontend).

Q2: How is an
A2: A regular chatbot typically lives in a single text box display a chart or create a mini-dashboard for those sales numbers (AI Native Frontend). Essentially, conversational is about dialogue, while generative UI is about dynamically changing the actual interface. As one comparison put it, a chatbot might tell you how to do something, whereas a generative UI will do it by producing the needed interface (e.g. generating a form and highlighting where to click).

Q3: Do developers need to learn new skills to build generative UIs?
A3: To some extent, yes prompt engineering (crafting instructions that guide the LLM to output correct UI specifications) and setting up guardrails (AI Native Frontend). However, the goal of these tools is to make life easier (AI Native Frontend).

Q4: Can generative UI work with any
A4: Generative UI is a broad approach, not something locked to a single vendor, but it does require certain capabilities. You need an C1 by Thesys are designed to be model-agnostic endpoints (C1 by Thesys currently supports models from Anthropic and plans to support Open API Access (AI Native Frontend). You can integrate C1 by Thesys into any React application via its SDK, so you don’t have to rewrite your app (AI Native Frontend). There are also open-source projects and libraries that let you do something similar with custom setups. In summary, generative UI can be added to existing apps, but you need the right middleware. It’s not “plug-and-play” with any random

Q5: What are some real use cases of generative UI today?
A5: Real-world use cases of generative UI are already emerging across various domains. A few examples:

  • Instead of showing the same charts to every user, an analytics platform can let users ask questions in natural language and then generate custom visualizations or reports on the fly. For instance, a user might type, “Show me our revenue vs. cost for each quarter in 2023,” and the system will produce that comparison chart dynamically (essentially acting as an frontend) (AI Native Frontend).
  • Intelligent Workflows: In enterprise software, generative UI can create multi-step forms or workflow screens as needed. Imagine an generates a UI with the email draft and a scheduler, so the user can tweak details before confirming. The UI wasn’t there a moment ago
  • Customer Support & Chatbots:
  • Early ““action buttons” like “Purchase item 1” or “Show more details on item 2” so you can easily give feedback. This kind of interface makes using