Generative UI: three approaches for AI

Generative UIs: Three Strategies for AI Agents

AI agents don't just generate text—they create dynamic UI components tailored to the user's context. This enables rendering interactive charts, forms, or cards instead of static screens. The approach tackles the challenge of interface variability, where pre-built states can't cover every scenario.

Key Use Cases

Generative UIs (GenUI) shine when interfaces are too flexible for hardcoding. Main scenarios:

• Live Artifacts: The agent creates an editable object—like a chart or table. Users tweak parameters, and the AI responds in real time with suggestions.
• Data Collection: Dynamic forms customized to the task, such as booking parameters.
• Data Visualization: Charts or cards replace text descriptions to cut cognitive load.
• Option Navigation: Filters and comparisons for handling multiple results.

Approach 1: Direct HTML Generation

The model outputs HTML code straight to the browser for rendering. The flow is straightforward: prompt → HTML → render.

Advantages:

• Quick to implement.
• Model has full freedom in picking elements.

Disadvantages:

• Breaks design system consistency: buttons and colors vary wildly.
• Security risks: Executable code from the model.
• Hallucinated attributes or browser glitches.

Best for prototypes or tools without strict UI rules. Avoid in production due to instability.

Approach 2: Declarative Descriptions

The agent generates JSON describing the structure: component types, order, and props. The client renders using a catalog of atomic elements (strings, buttons, inputs). Built on Google's A2UI protocol.

Advantages:

• Consistent styling aligned with your design system.
• Flexible composition without custom components.

Disadvantages:

• Structural variations based on prompts.
• Relies on model quality.

Perfect for highly variable visualizations while keeping brand integrity.

Approach 3: Component Library Selection

The agent picks a ready-made component from a catalog and fills its props. Maximum control: no code or structure generation.

The Tambo framework implements this via the AG-UI protocol: components are registered with Zod schemas. Supports two types:

• Generative: Rendered in-chat (charts, cards).
• Interactive: React components anywhere in the UI, with two-way binding (agent monitors state).

Advantages:

• Total control over visuals and UX.
• Rock-solid security.

Disadvantages:

• Must anticipate all needed components upfront.

Comparison of Approaches

| Criterion | HTML | Declarative | Static (Tambo) |

|----------|------|-------------|----------------|

| Visual Control | Low | Medium | High |

| Agent Freedom | Maximum | High | Limited |

| Complexity | Low (+tuning hassle) | Medium | Medium |

| Security | Risky | Safe | Safe |

Implementing in an Analytics Product

In an AI assistant for marketing analytics, we split the UI:

• Workspace: Interactive components (tables). Agent updates data without touching structure.
• Chat: Generative components (insight cards, mini-charts).

Registration: Components with descriptions and schemas. Agent streams props in real time.

Key Takeaways

• GenUI reduces cognitive load but needs a balance of freedom and control.
• Pick your approach based on UI variability and need for predictability.
• Tambo and A2UI are mature tools for React projects (Tambo v1.0 drops February 2026).
• Test rigorously for security and cross-browser compatibility.
• Focus on users: Let AI enhance, not disrupt.

— Editorial Team