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The final Cyberpunk-themed AI chat interface we will build.

January 5, 2026

Building an Agent User Interface

First Steps for a Professional Chat User Interface for your Agents

Kartik Marwah

4 min read

The world of AI development has moved at breakneck speed. We have mastered the art of building agents using technologies like MCP (Model Context Protocol), A2A (Agent2Agent Protocol), ADK (Agent Development Kit) and LangChain. We have powerful backends running on local machines or servers that can perform complex reasoning tasks.

However, there is a gaping hole in the workflow for many developers: Presentation. Most of these powerful agents live and die in the command line interface (CLI).

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This article will provide a step-by-step walkthrough on using Google AI Studio's "Build" feature to generate a fully functional, visually stunning React frontend for your AI agents, complete with chat history, model switching, and image generation capabilities.

Quick Basics Recap

Before we dive in, let's establish the stack we are looking at:

  • Google AI Studio: A web-based prototyping environment for developers to experiment with Gemini models.
  • Gemini 3.0 Pro: Google's advanced reasoning model, which we will use not just for chatting, but as the "coder" to build our app.
  • React: The most popular JavaScript library for building user interfaces.
  • Local Storage: A web API that allows JavaScript sites and apps to store key/value pairs in a web browser with no expiration date (perfect for saving chat history locally).
  • Artifacts/Previews: The ability within AI Studio to render code in real-time to see your application as you build it.

What is the User Interface We'll Build?

We are going to build a "ThinkChat" application. This is a dual-mode application: a text-based chat interface for reasoning agents and an "Imagine" mode for image generation. We will do this by prompting Google AI Studio to write the code, utilizing the gemini-3-pro-preview model for superior coding capabilities.

Overview of the Build Process

The best way to understand this process is to view it as an iterative conversation with a senior frontend engineer (the AI). We will break it down into the following steps:

  1. Setting the Stage: Configuring Google AI Studio.
  2. The Initial Prompt: Generating the core chat logic.
  3. Iterative Refinement: Adding sidebar history and settings.
  4. Expanding Functionality: Adding Image Generation.
  5. The "Beyond UI" Reality: Moving from prototype to production.

1. Setting the Stage

To get started, navigate to aistudio.google.com/apps. This is a relatively new section of the AI Studio suite designed specifically for building applications.

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Once logged in, you are greeted with a prompt bar. A crucial step here is model selection. By default, it might select Gemini Flash. For coding complex React applications, you want the strongest reasoning capabilities available.

Pro Tip: Switch the model to Gemini 3.0 Pro Preview. This model handles context window and complex coding instructions significantly better than the Flash variants.

2. The Initial Prompt

We don't want to write create-react-app. We want to describe the outcome. My initial prompt involves describing the functional requirements of the app:

"Build me a web application where a user can chat with an agent… text streams back to the interface… The interface should have an option to attach a file… and an option to clear the chat. The chats should be stored in the front end local storage for now."

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Google AI Studio acts as a "Code Assistant." It doesn't just give you a block of code; it creates a file structure (App.tsx, components/, styles.css) and renders a live preview on the right side of the screen. You can immediately interact with the app to test if the basic chat functionality works.

3. Iterative Refinement

The first version is rarely the final version. Now that we have a basic chat, we want to make it feel like a real application. This means adding persistent history and organization.

"Build a side bar on the left, which shows me a history of chats, and this should be read from Local Storage."

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The AI automatically refactors the code. It updates App.tsx to handle state management for multiple sessions and modifies the local storage logic to save arrays of chat sessions rather than just a single conversation.

We can further refine this by asking for specific UI elements, like a Model Selector dropdown. This allows the user to switch between "Gemini 3.0 Flash" (for speed) and "Gemini 3.0 Pro" (for quality) directly from the header.

4. Expanding Functionality: Image Generation

To make the app truly robust, let's add a second mode. We can ask the AI to create a navigation toggle between "Chat" and "Imagine" modes.

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The AI handles the conditional rendering. It creates a new view where the input is specifically designed for image prompts, potentially utilizing a different backend model (like Gemini Nano or Imagen) depending on your API configuration.

At this stage, we also asked for a Theme Toggle. With a simple prompt, the AI implemented a dark/light mode switch, instantly making the app look modern and polished (Cyberpunk dark mode is a favorite!).

5. The "Beyond UI" Reality

We have a beautiful interface, but it's currently running entirely in the browser using a temporary API key or local storage. This is great for demos, but not for production.

Toward the end of the session, we asked the AI to explain how to separate the architecture for a real-world deployment.

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To take this live, you need to solve four problems that the UI generator cannot solve for you:

  1. Database & Models: Moving from Local Storage to a cloud database (like Firebase or PostgreSQL) to persist user data across devices.
  2. Auth & Security: Implementing Google Auth or standard login flows to protect user data.
  3. Agent Integration: Connecting this React frontend to your actual Python/Node.js backend where your sophisticated agents live.
  4. Deployment: Hosting the frontend and the backend on a cloud provider.

Conclusion

You are well on your way to becoming a full-stack AI developer. By leveraging Google AI Studio, you can bypass the tedious hours of setting up frontend boilerplate and focus on what matters: the logic of your agents and the user experience. You now have a template that looks professional, functions smoothly, and is ready to be connected to your powerful backend logic.

Thanks for reading!

To access code for all my articles, visit my website https://theailanguage.com

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