Agentic Interfaces in Action: How Generative UI Turns AI from Chatbot to Co-Pilot

Meta Description: Explore how agentic interfaces and Generative UI transform AI from a simple chatbot to an interactive co-pilot, using LLM-driven dynamic user interfaces.

Introduction

Artificial intelligence has made incredible strides with large language models (LLMs) and other AI systems, but many AI-powered products still struggle to deliver full value due to a disconnect between AI and the user interface (AI Native Frontends). Too often, users interact with advanced AI through static, one-size-fits-all screens or a basic chat box (AI Native Frontends). The result is a clunky experience that fails to match the sophistication of the AI. The success of ChatGPT showed how a simple, intuitive chat UI can unlock massive adoption of complex AI, yet even ChatGPT is limited to a text conversation. To truly harness AI’s potential, we need interfaces that are as adaptive and intelligent as the AI itself (AI Native Frontends). This is where agentic interfaces come into play. By leveraging Generative UI (GenUI) - user interfaces dynamically generated by AI in real time - we can turn AI from a passive chatbot into an active co-pilot. In this post, we’ll explore what agentic interfaces are and why they’re emerging now, how generative UIs empower LLMs to create actionable dynamic interfaces, and concrete examples of AI moving beyond chat into interactive UIs. By the end, you’ll see how generative frontends might be the missing piece in today’s AI stack - the key to transforming raw AI power into intuitive, context-aware user experiences.

What Are Agentic Interfaces (and Why Now)

Agentic interfaces refer to UIs that an AI agent can dynamically create or control to help users achieve their goals, rather than being fixed in advance. In an agentic interface, the AI isn’t confined to replying in text - it has the agency to generate interactive elements (buttons, forms, charts, etc.) on the fly. Essentially, the interface becomes an extension of the AI agent’s reasoning, allowing the AI to take actions through the UI itself. This concept is becoming relevant now for a few key reasons:

• AI’s Evolving Capabilities: Modern LLMs can not only converse, but also reason and plan outputs. They can decide not just what to say, but how to present information. With generative UI techniques, an LLM can choose the optimal way to show results - for example, returning a chart or a form when appropriate, rather than a text paragraph. This turns the AI into more of a problem-solving partner. It’s a natural evolution: as AI agents get smarter, giving them control over the interface lets them actively assist users instead of just talking. Industry leaders have begun noting this shift - as one Cisco executive put it, generative AI is sparking “sporadic spikes of demand” but truly agentic AI creates “sustained perpetual demand” for interactive intelligence. In other words, when AI can operate through an interface continuously, it becomes far more useful day-to-day.
• The Chatbot Limitations: While chat-based interfaces have proven the appeal of conversational AI, they are inherently limited. A plain chat log forces all interactions into text. It’s easy to miss details in a long response, and complex tasks can become cumbersome. Users often need more than text - they may want interactive filters, visualizations, or multi-step workflows. Traditional UI design could provide these, but only if anticipated and coded in advance. Agentic interfaces remove that limitation by allowing the AI to generate whatever UI is needed in the moment. This is why we see a push from simple chatbots to more capable “AI co-pilots” embedded in apps. A co-pilot doesn’t just talk; it can act within a UI. For example, instead of telling a user a list of items, a shopping assistant can show product panels with images and “Buy” buttons. Instead of describing data trends in text, an analytics AI can present a live chart. These dynamic capabilities turn a static chatbot into an active helper. Companies like Cisco are already experimenting with this concept - Cisco’s new AI Canvas is described as an “agentic interface” that dynamically generates the management dashboards IT teams need. In effect, the AI Canvas is an AI ops assistant that creates its own UI for each task, rather than sticking to a fixed dashboard. This kind of agentic UI is becoming a competitive differentiator.
• Emergence of Generative UI Tools: The rise of generative AI-specific frontends and frameworks makes agentic interfaces feasible in practice. It’s only recently that developers have access to tools to turn LLM outputs into UI components live. For example, C1 by Thesys (more on this later) is the first Generative UI API - it enables any LLM to output dynamic interface elements. Similarly, open-source libraries (like those in the React ecosystem) and research prototypes have appeared that let LLMs control UI components. With these building blocks, the concept of an AI agent “designing” parts of the UI in real time has moved from theory to reality. In short, the technology and infrastructure needed for agentic interfaces are now coming online. This timing aligns with a broader trend: enterprises are desperate to improve AI product adoption. Analysts have observed that a majority of companies experimenting with AI have struggled to get real user uptake due to poor interfaces (AI Native Frontends). Now that solutions exist, there’s a surge of interest in making AI interfaces more dynamic and context-driven. We’re seeing AI-native software design emerge, where the entire application (not just the model) is built around AI’s capabilities (AI Native Frontends). Agentic interfaces are central to that vision, allowing the UI to keep up with the fluid, intelligent behavior of modern AI.

From Chatbots to Co-Pilots: Chat UI vs Interactive AI UI

A basic chat interface - a text box and a stream of messages - was a great starting point for AI because of its simplicity. Anyone could use ChatGPT through a chat window, and that accessibility fueled massive adoption. However, a chat-based UI treats the AI like a fancy text oracle. The user asks something, the AI prints words back. It’s useful for Q&A and writing assistance, but not ideal for more complex, interactive tasks.

In contrast, an interactive AI UI turns the AI into a true co-pilot that can guide and collaborate with the user. There are several clear differences between a chatbot interface and a generative, agentic interface:

• One-Size-Fits-All vs. Contextual UI: A chat UI is the same for every user - just a blank input and text output. An interactive AI UI can tailor itself to each context. For example, in a chatbot, if you ask for a spreadsheet analysis, you’d likely get back a long textual explanation. In a generative UI, the AI could actually present a mini spreadsheet or data table you can sort and filter. The interface adapts to the query. As UX experts at Nielsen Norman Group define it, “a generative UI is a user interface that is dynamically generated in real time by artificial intelligence to provide an experience customized to fit the user’s needs and context.” (AI Native Frontends) In other words, the UI no longer needs to be static or identical for everyone - it can reconfigure on the fly per user and per task. A chatbot can’t easily do that; a co-pilot can.
• Limited Interaction Modes vs. Multi-Modal Interactions: Chatbots primarily handle text (and sometimes voice). But many tasks are better served with interactive widgets. Think of scheduling a meeting - a chatbot might ask you a series of questions: “What date? What time? Does this work?” etc. An interactive AI assistant, on the other hand, could pop up a calendar picker and highlight available slots, making the process faster and more intuitive. Likewise, a chatbot can describe a trend (“Sales spiked in July and dipped in August.”), but a dynamic UI can show a real-time adaptive chart or graph of those sales figures (AI Native Frontends). The ability to use graphics, forms, sliders, and other UI controls means the AI can communicate more effectively and take structured input. It’s a richer two-way conversation: the AI presents options or visuals, and the user can directly click or adjust them, which the AI then responds to. This moves the interaction from a linear Q&A to an interactive loop, much like working with a human assistant who hands you tools to solve a problem.
• Passive Response vs. Proactive Assistance: With a simple chat, the AI mostly waits for queries and then responds. An AI co-pilot with an agentic interface can proactively guide the user. Because it can modify the interface, it might highlight important information or suggest next steps unprompted. For instance, after analyzing data, it could automatically show a “Filter results” button or a form to drill down further. Or if it notices an anomaly while monitoring something, it could flash a dashboard view or alert, rather than waiting for the user to ask. The AI essentially can take the initiative by presenting UI elements that draw the user’s attention or prompt action. This makes the AI feel much more like a partner working alongside you - hence the term co-pilot. It’s similar to how GitHub Copilot suggests code while you’re typing, integrated into your IDE, rather than you having to explicitly ask for every suggestion in a chat. A co-pilot interface blurs the line between “user controlling software” and “AI assisting user”; the AI can steer the interaction when appropriate.

In summary, chat-based interfaces introduced millions to AI, but they treat AI like a neutral answering machine. Interactive, generative UIs let AI step out of the chat bubble and become a co-pilot embedded in the user’s workflow. Instead of every solution being delivered as text, the AI can provide the right interface for the task: a map for a location query, a form for data input, buttons for quick choices, or a composed dashboard for a high-level overview. This not only makes complex tasks easier, it also makes the AI feel more present and helpful. Users get to interact with AI in a familiar yet dynamic way - clicking buttons, selecting options, seeing information laid out - rather than parsing everything from paragraphs. The end result is higher engagement and efficiency. Many early adopters report that users stay more engaged when AI responses include interactive elements rather than just long text (AI Native Frontends). By comparing chatbots vs. agentic UIs, it’s clear why so many products are now aiming to integrate LLM-driven UI components: to evolve their AI from a basic assistant into a full-fledged partner in productivity.

How Generative UI Empowers LLMs (Turning Outputs into Interfaces)

Generative UI (short for Generative User Interface) is the key technology behind agentic interfaces. It enables an AI model to generate actual interface elements as part of its output, effectively bridging the gap between backend intelligence and frontend experience (AI Native Frontends). But how does this work under the hood? Let’s break down how an LLM can produce a dynamic UI in real time:

• LLM UI Components: Developers start by providing a library of pre-built UI components that the AI can use - think of widgets like charts, tables, buttons, text inputs, forms, and so on (AI Native Frontends). Each component has a name and some parameters. The generative AI doesn’t literally draw the button or chart pixel-by-pixel; instead, it outputs a structured specification indicating which component to use and with what data or content (AI Native Frontends). For example, when asked a data question, the AI might return a JSON object like:jsonCopyEdit{ "component": "bar_chart", "title": "Sales by Region",
"data": [ ... ], "xAxis": "Region", "yAxis": "Revenue" }
  This is essentially the AI saying, “I want to show a bar chart with this data.” A rendering engine on the front end will interpret that and display an actual chart in the app (AI Native Frontends). These LLM-driven UI components act as the bridge between the model and the interface. The developers define what components exist and what they require (ensuring nothing unsafe or out-of-scope can be generated), and the LLM’s job is to choose and configure those components appropriately. In essence, the AI’s textual output includes special instructions that the application recognizes as UI elements to render.
• Frontend Automation & Rendering: On the client side, a generative UI system includes a runtime that listens for these component specifications and renders them live. In a web application, this is often done with a JavaScript or React framework that maps the AI’s output to real DOM elements or React components (AI Native Frontends). You can think of it as having a mini front-end developer built into the app, automated by the AI (AI Native Frontends). For instance, if the LLM outputs the JSON for a chart, the front-end code takes that and says “OK, create a Chart component in the UI with these properties.” The magic is that this happens immediately, at runtime - no human in the loop. So the interface can literally update itself in response to the AI’s decisions. This frontend automation means developers don’t have to hard-code every possible dialog or screen; they define the building blocks and let the AI assemble them on demand. It’s analogous to how a web browser renders HTML/CSS that a server generates on the fly, but here the “server” is an AI and the “HTML” is a structured UI spec.
• Context Interpretation and UI Decisions: The LLM is typically guided by prompts or system instructions to output UI components when appropriate. It “interprets” the user’s intent and decides not only what to respond with, but how to present it. For example, if a user asks, “Compare the sales performance of Product A vs Product B this quarter,” a well-instructed LLM might realize a chart is the best answer format (AI Native Frontends). It could output both a textual summary and a chart component showing the comparison, perhaps with a dropdown to select different time ranges. If the user then follows up with, “Actually, show me last year instead,” the LLM can output an updated chart component. The interface morphs accordingly without needing a pre-built “comparison dashboard” - it’s generated on the fly by the AI. Real-time adaptive UI is achieved because the AI is essentially performing a UX designer role in microseconds: taking the user’s request and producing an appropriate layout or widget for it (AI Native Frontends). The ability to generate UI from a prompt means the AI’s “answer” can be far more actionable. Instead of the user having to read and then navigate elsewhere or copy-paste, the AI directly gives them something they can interact with.
• Not Just Design-Time, but Run-Time: It’s important to clarify that Generative UI is different from AI tools that help designers or developers during development (often called AI UX tools). Those “prompt-to-UI” tools might generate a Figma mockup or some HTML code from a description, but that happens before deployment and results in a fixed UI (AI Native Frontends). In contrast, generative UI is about the live application changing its interface continuously. The UI is an ongoing creation of the AI agent, not a one-off design artifact. This is why an LLM agent user interface can be so powerful - it’s as if the software’s UI is a conversation that evolves, rather than a static form. The AI can “speak” in UI elements just as fluidly as in text (AI Native Frontends). For developers, this means thinking of the UI layer as another intelligent layer of the app. You provide a palette of components and some guardrails (e.g. design guidelines, security constraints on what can be shown), and then trust the AI to handle a lot of the immediate UI decisions. It’s a paradigm shift from manually coding every interaction flow to orchestrating AI outputs and AI UX behavior. Development teams that embrace this report significantly reduced UI coding and faster iteration cycles (AI Native Frontends) - essentially, the AI does the “glue code” and repetitive work of wiring up data to visuals, freeing engineers to focus on core logic.

In practice, how do you implement such a system? A number of solutions are emerging. For example, the open-source LangChain framework has introduced support for streaming LLM outputs as React components, and projects like CopilotKit let an AI manipulate a web app’s interface via components. On the enterprise side, specialized platforms like C1 by Thesys provide a production-ready way to plug generative UI into your apps. C1 by Thesys is described as an “AI frontend API” that lets developers turn LLM outputs into live, dynamic interfaces in real time. Under the hood, it uses LLMs to interpret natural language or other signals and output JSON definitions of UI elements, which are then rendered in the application. With tools like this, a developer can send a prompt or user query to an AI model and get back (for example) a menu, a chart, and a paragraph of explanation - all formatted as a response that the front-end knows how to display. The heavy lifting of maintaining state, updating the UI, and integrating with external data sources can be managed by the generative UI framework. In short, Generative UI empowers LLMs to produce not just answers, but interfaces, effectively making the AI an active participant in the front end of the software. This fusion of AI and UX is what enables the leap from static chatbot to interactive co-pilot.

Enterprise Examples: AI Dashboard Builders, Ops Assistants, Automation Co-Pilots

To concretely illustrate agentic interfaces, let’s look at a few enterprise use cases where generative UIs can shine:

• AI Dashboard Builder: Imagine a data analyst who typically uses a BI tool or dashboard with dozens of preset charts. In an agentic interface scenario, the analyst could simply ask an AI assistant, “Show me last week’s website uptime by server and highlight any anomalies.” Instead of the user manually configuring a dashboard, the AI acts as an AI dashboard builder - it queries the relevant internal data and generates a custom dashboard view on the fly. For example, it might display a line chart of server uptime for the last week, with the problematic downtime periods highlighted in red, and perhaps a table below listing any incident reports during those periods. This temporary, context-specific dashboard appears instantly, without any developer or IT team involvement (AI Native Frontends). If the user then asks a follow-up like, “Compare that to the previous month and show by region,” the AI can reconfigure the interface live - maybe adding a region filter dropdown and updating the charts. In essence, the AI agent assembles an entire analytics interface tailored to the question at hand, and when the task is done, the UI can dissolve or reset. Enterprises are very interested in this capability, as they have mountains of data and varied user needs. A generative UI allows each employee to get a personalized analytics UI for their query, rather than everyone being stuck with the same static set of charts. This not only saves huge amounts of time (no waiting on dashboard customizations), but also empowers non-technical users to explore data through AI. Internal prototypes and case studies have shown companies saving significant effort by using AI to generate 70 - 80% of internal dashboard and form UIs, freeing developers from routine reporting pages (AI Native Frontends).
• Operational Assistant (Agentic Ops): In IT operations and devops, time is critical during incidents. An agentic interface can serve as an ops co-pilot that brings the right tools to the forefront in real time. Cisco’s AI Canvas is a great example of this in action: it’s an AI-driven ops console that “dynamically generates management dashboards that IT pros need to handle different tasks.” During a network outage, for instance, an ops engineer could describe the issue to the AI (or the AI might automatically detect anomalies). The AI Canvas can then spin up a custom dashboard showing the affected network segments, relevant logs, suggested remediation steps, and buttons to execute those fixes. Cisco terms this approach “agentic ops,” because the interface itself is generated by an AI agent to address the situation. The benefit is that the human operator isn’t stuck swiveling between dozens of tools and tabs; the AI consolidates what’s needed into one adaptive UI. After the incident, that UI can disappear or transform for the next task. This concept can extend beyond networking - imagine a security operations center AI that, when a threat is detected, opens up a tailored view with threat details, impacted systems, and one-click containment actions. Or a customer support AI that, hearing the user’s issue, immediately pulls up a mini-app with account info and troubleshooting scripts. All of this happens because the AI can generate interfaces on demand. Early results are promising: companies piloting these AI ops assistants report faster incident response and less training needed for staff, since the AI presents an intuitive interface for each scenario rather than expecting users to master myriad dashboards.
• Automation Co-Pilot: Not every employee is a programmer, but many could benefit from light automation in their workflows. An agentic interface can act as a no-code automation co-pilot. For example, a marketing manager could tell an AI, “Every week, take our website analytics, compile the key metrics into a slide deck, and email it to the team.” A traditional approach would require someone to script this or use a complex automation tool. An AI automation co-pilot, however, could walk the manager through it in an interactive dialog: it might generate a form saying “Pick the metrics you want to include” with a checklist, then a prompt “Choose the email recipients” with an email input field, and perhaps a preview of the first report. The user fills these in, and the AI sets up the automation behind the scenes (using internal APIs or tools), all through a conversational UI that the AI generated on the fly. Essentially, the AI built a little app for the user to configure their workflow, then executed it. This dramatically lowers the barrier for automation in the enterprise. Platforms like Nexla are moving in this direction - their AI, called NOVA, provides an agentic interface for composing data pipelines via natural language. Users describe how data should flow, and the AI creates the pipeline with the necessary transformations and schedules, presenting a visual flow that the user can tweak. We can expect more “co-pilots” for business users: finance co-pilots to build custom reports, HR co-pilots to generate survey tools or forms, etc. All of these turn complex multi-step processes into an AI-guided UI where the user just makes high-level decisions. The AI frontend generates the rest. The enterprise impact is significant - experts note that companies adopting generative UI for internal tools have seen faster product cycles and “millions in annual savings” by automating tedious frontend work and reducing engineering effort. In short, an automation co-pilot democratizes solution-building: employees get bespoke mini-apps created in real time by the AI, rather than having to request new software or do things manually.

Consumer Examples: Scheduling Assistants and Personalized Shopping AIs

Agentic, generative interfaces aren’t just for enterprise tools - they have huge potential in consumer-facing apps and services as well. Here are two examples that highlight how AI co-pilots can elevate everyday user experiences:

• Smart Scheduling Assistant: Virtual assistants today (like those in our phones or smart speakers) often fall back to sending calendar invites or listing times via text, which can be awkward. A generative UI approach can make a scheduling assistant far more intuitive. Imagine texting an AI, “Schedule a 30-minute meeting with Alice next week.” Instead of just replying “I’ve sent an invite for Monday at 10,” the assistant could present an interactive interface: it might pop up a few suggested time slots (pulled from both your and Alice’s calendars) as clickable options. Perhaps it also shows a small calendar view of your week with colored blocks where you’re both free. You can tap the slot that works best, or drag to select a different time. The AI then finalizes the invite. If more information is needed (say the meeting location), the AI could generate a quick form or a follow-up question UI. All of this happens in-line, without you opening a separate calendar app - the AI has effectively become the UI for scheduling. The experience is seamless: you made a request in natural language, and the AI provided a visual, interactive way to complete the task. This is much closer to how a human assistant might operate (“Here are some options, which do you prefer?”) rather than the rigid back-and-forth of a typical chatbot. Companies are already exploring these ideas: for instance, some email scheduling tools with AI will now offer buttons for “Accept this time / Propose new time” directly within the email interface generated by the AI. A truly agentic scheduling assistant could live in messaging apps or voice interfaces but summon graphical widgets as needed (for dates, maps, attendee details, etc.). The result is a faster, friendlier scheduling workflow. Users don’t have to manually cross-check calendars or decipher time-zone math; the AI co-pilot handles it and presents the results with an appropriate UI. Given how much time people spend coordinating meetings, a smart scheduling co-pilot could save a lot of hassle and feels more “human” than standard digital calendar tools.
• Personalized Shopping AI: E-commerce is another arena ripe for generative UI. Consider how online shopping works today: you search for a product and get a generic grid of results and filters, the same interface every other user sees. Now picture an AI shopping assistant that knows your preferences and can dynamically curate the interface. You might start by telling it, “I’m looking for a gift for a 5-year-old who loves science.” A chatbot would return some product recommendations in text. But an agentic shopping AI would actually render a tailored store for you: it could display a few science-themed toys or kits with images and prices, perhaps pulled from various categories. It might also generate a sidebar with filters like age range, price range, or brand, already tuned to likely values (maybe it knows 5-year-olds usually fall in a certain toy category). As you interact - say you click on a chemistry set to learn more - the AI can adapt the UI further, maybe showing a comparison table of that set vs. a similar one, or a short quiz “Is this child more into experiments or reading?” to refine the suggestions, with the quiz presented as interactive cards. Essentially, the AI is acting as your personal shopper and the storefront is morphing to your needs in real time. Each user could get a unique interface optimized for them: if you hate scrolling, the AI might present results in a slideshow; if you love deals, it might highlight a special offer banner it generated just for you. The technology to do parts of this is becoming available (some retailers are experimenting with AI-driven recommendation UIs), but a full generative UI shopping assistant would be a game-changer. It merges conversational commerce with a rich visual experience. By building UI with AI, retailers can deliver a level of personalization that was previously only possible in a 1:1 in-store shopping experience. The AI can even handle complex tasks like configuring a custom product. For example, for buying a laptop, instead of the user navigating a complicated configurator, they could just say, “I need a laptop for video editing under $1500,” and the AI co-pilot would present a few pre-configured options with spec highlights, maybe an interactive slider to adjust budget, and a Q&A panel for detailed questions - all generated on the spot. This kind of real-time adaptive UI in shopping could lead to higher customer satisfaction and conversion rates, because the interface is literally shaped around the customer’s needs and questions in that moment, rather than a static catalog.

Across both enterprise and consumer domains, these examples show the versatility of generative user interfaces. The common theme is that AI-driven interfaces can present exactly the right tools or information at the right time, making interactions feel smoother and more intuitive. Whether it’s helping an employee troubleshoot a server outage or helping a consumer find the perfect gift, agentic interfaces turn AI into a proactive partner. They represent a shift from AI being in a product to AI as the product’s interface.

Conclusion: Toward an AI-Native Frontend Era

We are at the dawn of a new era in software design, one where interfaces can be as intelligent and fluid as the AI models powering the backend. These agentic interfaces - powered by generative UI - transform user interactions by letting AI shape the experience in real time. Instead of rigid screens designed only for an “average” user, applications can now present LLM-driven product interfaces that adapt on the fly to each user’s needs. In essence, AI is graduating from the role of chatbot to the role of co-pilot. It’s a shift that promises more intuitive software for users and less upfront UI development for teams.

Of course, making this shift requires robust infrastructure. This is where companies like Thesys come in. Thesys is building the AI frontend infrastructure that enables these generative experiences at scale. Their flagship product, C1 by Thesys, is a Generative UI API specifically designed to turn LLM outputs into live, interactive UIs. With C1 by Thesys, developers can give their AI tools a way to render charts, forms, buttons, and entire layouts directly from the AI’s responses - effectively plugging an AI agent into the front-end of their application. Thesys’s platform handles the heavy lifting of translating the AI’s output into real interface elements securely and consistently, so teams can focus on defining the components and rules without reinventing the wheel. In short, C1 by Thesys is a new way of helping turn any AI into a true co-pilot with its own dynamic cockpit.

Thesys (https://thesys.dev) envisions a future where AI-native software is the norm, and it’s actively building the tools to make that happen. Backed by a team with deep expertise in both AI and frontend engineering, Thesys has already helped hundreds of teams deploy adaptive AI interfaces. By using C1 by Thesys - Thesys’s Generative UI API - companies can elevate their AI products from static chat interactions to rich, context-aware UIs in real time. The result is smarter software that users actually love to use, because it meets them where they are. If you’re interested in exploring how generative UIs can enhance your own applications, be sure to check out Thesys for more information and guides. You can even dive into the technical details and interactive demos in the Thesys docs (https://docs.thesys.dev) to see how an LLM can generate UI on the fly.

The age of agentic interfaces has only just begun. As AI continues to advance, having an equally adaptive interface will be crucial to unlocking its full potential. A chatbot in a box is fine - but an AI co-pilot with a dynamic UI can truly empower users. Generative UI is the technology that makes this possible, and companies like Thesys are leading the charge in turning the vision into reality. It’s an exciting time for developers and designers: the frontier of AI UX is here, and it’s transforming how we think about building software. By embracing AI-driven frontends, we can create applications that not only think for the user, but also show the user exactly what they need, when they need it. The endgame is software that is smarter, more helpful, and more human-centric - and that’s a future worth building.

For those who want a head start, exploring C1 by Thesys is a great option since it’s purpose-built for this scenario. It abstracts much of the complexity: you send high-level instructions and it returns ready-to-render UI descriptions. The Thesys documentation (https://docs.thesys.dev) offers examples and SDKs that show how to integrate C1 by Thesys into web applications, mobile apps, or even chatbots, so that your AI’s outputs aren’t limited to text. Essentially, using a platform like C1 by Thesys or similar, developers can quickly add generative UI capabilities to their app without building everything from scratch. The bottom line is that building AI-driven dynamic UIs is now within reach - it requires blending LLM capabilities with modern front-end development, and the payoff is interfaces that can adapt like never before.

References

Parikshit Deshmukh. (2025). AI-Native Frontends: What Web Developers Must Know About Generative UI. Thesys.

Parikshit Deshmukh. (2025). The Role of Frontend Infrastructure in AI Applications (Explained). Thesys.

Parikshit Deshmukh. (2025). Bridging the Gap Between AI and UI: The Case for Generative Frontends. Thesys.

Parikshit Deshmukh. (2025). Glue Code Is Killing Your AI Velocity: How Generative UI Frees Teams to Build Faster. Thesys.

Parikshit Deshmukh. (2025). Why Every Enterprise Needs a Generative AI Frontend Strategy. Thesys.

Moran, K., & Gibbons, S. (2024). Generative UI and Outcome-Oriented Design. Nielsen Norman Group.

Firestorm Consulting. "The Rise of Digital Solutions in Traditional Industries" Firestorm Consulting.

Sharwood, S. (2025, June 10). Cisco borgs all its management tools into a single Cloud Control console. The Register.

Krill, P. (2025, April 25). Thesys introduces generative UI API for building AI apps. InfoWorld.

Firestorm Consulting. "Rise of AI Agents" Firestorm Consulting.

InsideAI News Staff. (2025, March 4). Nexla expands AI-powered integration platform for enterprise-grade GenAI. insideAI News.

FAQ

Q: What is Generative UI?
A: Generative UI (GenUI) refers to a user interface that is dynamically created by AI in real time, rather than designed entirely by humans beforehand. In a generative UI system, an AI (often an LLM) can assemble UI components on the fly based on the user’s needs and context. This means the layout, visuals, and options you see are generated “just in time” by the AI. For example, if you ask a generative UI-powered app for a data report, the AI might generate a chart or table interface specifically for that request. Generative UIs make software highly adaptive - the interface tailors itself to each interaction, providing a customized experience for every user. This is different from traditional UIs, which are fixed and only change when developers update them. In essence, generative UI lets the AI become a real-time UI/UX designer, building an interface that best presents its output and engages the user.

Q: What are agentic interfaces in AI?
A: An agentic interface is a user interface that an AI “agent” can control or generate as part of its operation. The term comes from giving the AI agent agency over the interface. In practical terms, an agentic interface allows an AI to go beyond text replies and actually perform actions or display information through UI elements. For instance, an AI with an agentic interface could present buttons, forms, or interactive charts to the user, not just suggestions in text. This makes the AI behave more like a co-pilot: it can take initiative by showing relevant controls or visuals, guiding the user through tasks. Agentic interfaces are enabled by generative UI technology - the AI can dynamically create the interface needed for the situation. They are becoming popular now because they make AI systems much more useful and user-friendly. Instead of a static chatbot or fixed dashboard, an agentic interface lets the AI adapt the UI to each goal. Early examples include Cisco’s AI Canvas (which generates IT dashboards on demand) and various “AI assistant” features in enterprise software that pop up context-aware tools. In summary, an agentic interface is one where the AI isn’t just in the interface - it is the interface, in the sense that it determines what the user sees and can do, moment to moment.

Q: How is a generative UI different from a normal chatbot interface?
A: A normal chatbot interface is typically just a text input box and a chat log of messages. All the AI can do is output text (and maybe images or links) in the conversation. A generative UI, on the other hand, allows the AI to create a much richer interface dynamically. The difference comes down to flexibility and interactivity. In a chatbot, if you want to change something or input new info, you usually have to type another message. In a generative UI, the AI could provide interactive elements - for example, after answering in text, it might display a slider or buttons for follow-up options that you can click. The UI can change layout entirely depending on the query (showing a map, a table, a form, etc.). Essentially, a chatbot interface is static (same for every query), whereas a generative UI is adaptive (the AI can alter the UI per query). From the user’s perspective, a chatbot is like messaging a person, while a generative UI can feel like using a mini-app that the AI built for you on the spot. The generative UI turns the AI into more of a full-service assistant: it not only tells you information but also presents tools to act on that information. This makes complex tasks (like scheduling, data analysis, multi-step workflows) much more user-friendly compared to doing them via pure chat commands.

Q: What are some real-world examples of generative UI use cases?
A: There are many emerging examples across different domains:

• Data Analytics and Dashboards: AI “dashboard builders” can generate custom charts and analytics dashboards in response to a user’s question. For example, an employee might ask an AI for “monthly sales by region,” and the AI creates a dashboard view with the appropriate chart and a filter to switch regions. This on-the-fly dashboard disappears or updates when the user’s next question comes.
• IT Operations (Agentic Ops): In network operations or DevOps, an AI assistant can generate incident-specific interfaces. If there’s an outage, the AI could open a dashboard highlighting the failing components and include buttons to execute diagnostic or remediation scripts. Cisco’s AI Canvas is an example, where the interface for troubleshooting is generated as needed by the AI.
• Personal Assistants (Scheduling, Email): An AI scheduling assistant might generate a calendar interface or suggested meeting times that you can click on, instead of just chatting about dates. Similarly, an AI email triage tool could turn a long email thread into an interactive summary with action buttons (like “approve” or “schedule call”) that it creates on the spot.
• E-commerce and Shopping: Personalized shopping AIs can build a UI of product recommendations tailored to the user. For instance, if you describe what you want, the AI can lay out product cards with images and comparison features, effectively creating a mini storefront for you. This has been trialed in some retail sites where an AI guides users through product finders with dynamic forms and visuals.
• Internal Tools and Forms: Enterprises are using generative UI for internal apps like ticketing systems or CRM. An AI can generate form fields dynamically based on context. If a support agent is filing a ticket and mentions a certain product, the AI might automatically add the relevant product-specific fields into the form UI. This makes data entry and retrieval more efficient, as the interface adapts to each case.

These examples all share the idea that the interface isn’t predetermined - it’s created in response to the user and context. This results in more efficient interactions. Users don’t waste time navigating irrelevant menus or constructing complex queries; the AI provides exactly the UI needed for the task at hand.

Q: How can developers build AI-driven, dynamic UIs?
A: Developers can build generative UIs by combining AI models with a front-end framework that supports dynamic component rendering. Here’s a simplified roadmap:

1. Choose or Develop UI Components: First, define the set of UI components that your AI is allowed to create (charts, buttons, text inputs, etc.). These could be custom React/Vue components in a web app, mobile UI elements, or even HTML templates. Ensure you have a way for the AI to invoke these (for example, a JSON schema or function calls representing each component).
2. Integrate an LLM with Instructions: Use a capable LLM (like GPT-4 or similar) and give it instructions (via prompt engineering or fine-tuning) to output structured data for UI components when appropriate. For instance, you might frame responses like: “If the user asks for data or an action, respond with a JSON describing the UI along with explanation text.” Many frameworks now support function calling or structured outputs from LLMs, which is useful here.
3. Use a Generative UI API or Library: To simplify development, you can use existing tools like C1 by Thesys or open-source libraries designed for generative UI. For example, C1 by Thesys provides an API where you send user prompts and it returns UI component specs that your app can render directly. This saves a lot of time - as InfoWorld noted, “C1 lets developers turn LLM outputs into dynamic, intelligent interfaces in real time.” If you prefer open source, libraries like LangChain’s UI modules or CopilotKit for React can serve as starting points.
4. Implement the Rendering Engine: On the client side, implement a module that takes the AI’s structured output and maps it to actual UI updates. In a web app, this could be a function that receives JSON and uses a framework (React, Angular, etc.) to render or update components accordingly. Essentially, this is the piece that automates the front-end, applying changes without a full page refresh or deploy. Ensure it’s secure (e.g., don’t allow arbitrary code execution - only allow known component types and validate the inputs).
5. Iterate with Design and Guardrails: Generative UIs are powerful but can confuse users if not designed carefully. Work with UX designers to set guardrails - for example, maintaining consistent look & feel even though components may appear in varying combinations. You might impose rules through system prompts (like instruct the AI to always include a title for generated charts, or not to generate more than 3 new elements at once to avoid clutter). Testing with real users is important: see if the AI’s choices of UI make sense and adjust prompts or component offerings as needed.
6. Monitor and Refine: Once deployed, monitor how the AI-generated interfaces are being used. You might find, for example, that users are often confused by a certain generated form, indicating the AI might need to provide more guidance, or that a new component type is needed (maybe a specific kind of graph). Generative UI development is an iterative process - you continuously refine the AI’s “UI vocabulary” and the rendering logic to improve the experience.