AI Meets Ignite UI: Building with AI Agents 

Developers should always maintain control over their code output, performance, and architecture when building applications. Even when they use automation tools. The idea is to build faster without sacrificing quality. With new trends emerging or already being firmly established, including AI agents, vibe coding, vibe design, and low-code app development, teams are able to blend visual composition, production-grade UI components, AI-assisted workflows, and code generation techniques faster than ever. 

All of these should be aimed at efficiency. Often, the goal is to speed up and shorten app development cycles. But what’s the code quality then? Is it still structured, maintainable, performant, and scalable to fit complex scenarios? 

Two of our most prominent app development tools have come together – Ignite UI and App Builder™. Not as a collection of disconnected features, but as foundational drivers for AI-assisted application development, where Ignite UI meets AI and AI agents workflows to operate within a well-defined UI and application model. 

One that developers can trust. 

Let’s further explain our AI-driven development approach. 

What “Building Apps with Agents” Actually Means

AI agents represent a system or program that can autonomously carry out tasks instead of a user. In other words, these could be perceived as digital workers. However, AI agents are only as effective as the systems they’re allowed to operate on. Without a clear application model and a stable UI base, agent-driven development quickly becomes mere guesswork. 

In our context, an AI agent is not simply a chatbot that replaces a developer and produces code automatically. It’s a complex AI system that can: 

• Clearly understand the structure and intent of a given app. 
• Generate UI and views using a known component model. 
• Modify existing applications without discrepancies. 
• Operate across tools (visual builders, IDEs, repositories). 

This is a critical distinction, and AI agents are expected to work within boundaries, including clear APIs, known components, and predictable patterns. When agents generate UI without a foundation, developers spend their time fixing structure and technical debt instead of advancing on functionality. 

Ignite UI As the Foundation: What It Means in Practice 

Ignite UI supports modern frameworks, such as Angular, React, Blazor, and Web Components, delivering enterprise-grade and feature-packed components that are built for large datasets, complex interaction models, and consistent, and fully customizable theming. 

When we say Ignite UI is foundational for AI-assisted development, we mean this: 

AI shouldn’t invent or draw your UI system. It should assemble your UI system. 

Instead of generating arbitrary HTML/CSS patterns, the workflow anchors UI construction in: 

• Data grids and charts tested for hundreds of thousands to millions of records. 
• Standardized form input components and validation behaviors. 
• Layout and navigation components that enforce consistency. 
• Theming systems that keep UI coherent across views. 
• Reusable patterns that match what teams already maintain and are familiar with. 

Why is this so crucial? In essence, it turns AI from a “creative guesser” into an “optimized builder” that operates safely within known design standards and a well-established engineering environment.  

App Builder plays a critical role in this approach, for it defines the app model that sits between visual composition, generated code, and runtime behavior and user experience. While Ignite UI provides the enterprise-grade building blocks, our AI-powered App Builder understands how those blocks are assembled into views, routes, data bindings, and interactions. This shared model is what allows AI, and later, AI agents, to operate with specific intent rather than do guesswork. 

Ignite UI Developer Productivity + Performance + AI Agents via MCP 

As mentioned earlier, Ignite UI provides the foundation, while App Builder (exposed through MCP) serves as the execution layer on which AI agents can rely. 

Ignite UI has long been focused on consistent interaction patterns and scalable UIs under heavy data loads. These qualities are essential in an agent-driven world. If agents are helping teams generate dashboards, forms, or data-heavy views faster, those screens must still perform like production-grade software, not prototypes. 

Recent grid performance improvements that we released, including faster sorting, grouping, and filtering on very large datasets, weren’t just optimizations. They ensure that AI-assisted development can scale. As AI agents accelerate UI creation, Ignite UI ensures the result remains responsive and reliable. 

This serves as known vocabulary. Instead of inventing UI patterns, agents assemble applications from standardized, enterprise-grade components: grids, charts, dialogs, forms, navigation, and layouts that already encode best practices. As a result, the generated code remains recognizable, maintainable, and aligned with existing frameworks and documentation. 

MCP is what makes this practical inside any agentic workflow. Through an MCP server, App Builder exposes its application model and the Ignite UI components in a way that agents can directly invoke. Rather than guessing how to modify a project, an agent can explicitly call App Builder to generate a view, wire routing, or compose UI using Ignite UI components. 

In one of our past webinars, we demonstrated precisely this.  

Everything took place entirely within the IDE. An agent running in VS Code was instructed to add a registration page to an app. Behind the scenes, several things happened: 

• The agent invoked App Builder via MCP to generate a new view based on Ignite UI components 
• Missing Ignite UI dependencies were detected and installed automatically 
• Application routing was updated to include the new page 
• Navigation elements were adjusted to expose the new view navigation in the UI 

At no point did the developer need to switch tools or manually wire pieces together. What makes this approach different is that the agent isn’t improvising or inventing UI patterns. It’s assembling the UI from trusted, enterprise-grade controls within a known application structure. The output behaves like the rest of the app because it is the rest of the app, built from the same Ignite UI foundations. 

Just as important, developers remain in control. The generated result is standard source code, built with real Ignite UI controls. 

Why AI-Generated UI Often Breaks Down in Real Projects? 

Many AI development initiatives focus on speed: “Describe an app, get code.” The challenge is that in real product teams, speed is only useful if it comes with: 

• Consistent structure across screens and teams. 
• Predictable behavior under real data load. 
• Maintainable code that matches existing conventions. 
• Accessibility, localization, and theming. 
• Patterns that scale with the app over time. 

Generic AI code generation often produces code that looks acceptable but is difficult to scale and, in some cases, to maintain. It may use inconsistent layouts, improvised UI patterns, and logic across components with no shared structure. Developers end up rewriting long lines of code they haven’t produced, while, unfortunately, “AI acceleration” becomes “AI prototyping.” 

Ignite UI changes that dynamic because it gives AI something solid to build on: a component model designed for data-rich apps, tested performance, and standardized UI behaviors across frameworks.