Architecture, Implementation, and Open Questions
We are witnessing a structural shift in how interactive systems are built.
Backend systems are increasingly agentic — probabilistic reasoning engines capable of planning, tool use, and dynamic workflow construction. Their capabilities evolve continuously as tools and knowledge expand.
Frontend systems, however, remain largely deterministic — composed of static routes, predefined flows, and compile-time component trees that assume we know in advance what users will need to see.
This mismatch introduces friction:
- Each new agent capability requires UI changes
- Unexpected conversational branches lack visual representation
- Frontend release cycles become workflow bottlenecks
This pressure has led to interest in Generative UI (GenUI) — moving interface construction from compile-time to runtime.
This article explores that shift through Agent-to-UI interaction models (A2UI) — a protocol-oriented approach where UI becomes a negotiated surface between reasoning systems and renderers.
We will examine both high-level architecture and practical implementation mechanics.
Historical Context — This Isn’t a Blank Slate
Generative UI extends prior work in schema-driven rendering:
- Adaptive Cards
- JSONForms
- Headless CMS layout systems
- Low-code UI builders
- Remote-config driven interfaces
The difference today: Layout producers are now probabilistic and stateful rather than deterministic. Understanding this lineage prevents overstating novelty and grounds architectural discussion.
High-Level Architecture
flowchart TD
Subgraph Client
User Renderer[A2UI Renderer] Registry[Component Registry] User --> Renderer Renderer --> Registry end
subgraph Protocol Events[User Events JSON] Stream[Schema Stream] end
subgraph Backend Gateway[Gateway / Sanitizer] Agent[Agent Runtime] Tools[Enterprise Tools] end
Renderer --> Events Events --> Gateway Gateway --> Agent Agent --> Tools Tools --> Agent Agent --> Gateway Gateway --> Stream Stream --> Renderer
Responsibilities
Renderer
- Builds UI from schema
- Emits user interaction events
Registry
- Trusted component mapping
- Execution boundary
Gateway
- Validates schema
- Normalizes structure
- Enforces security
Agent Runtime
- Maintains state
- Plans actions
- Produces layout intent
Protocol Schema Design
Rather than streaming executable UI code, systems exchange structured schema.
Example Layout Intent
{
"interaction_id": "evt_99821_a",
"operation": "surfaceUpdate",
"components": [
{
"id": "card_main",
"type": "adaptive_card",
"props": {
"title": "Conflicting Meeting Detected",
"severity": "warning"
},
"children": ["desc", "actions"]
},
{
"id": "desc",
"type": "text_block",
"props": {
"content": "Your proposed time overlaps."
}
}
]
}
Why Flat Adjacency?
- Easier streaming
- Reduced generation failure
- Efficient patching
- Cleaner incremental rendering
Renderer Implementation (Client Side)
Below is a simplified React-style renderer.
const REGISTRY = {
adaptive_card: props => <WarningCard {...props} />,
text_block: props => <p>{props.content}</p>,
button: props => (
<button onClick={() => emitEvent(props.action)}>
{props.label}
</button>
)
};
Tree Construction
function buildTree(components) {
const map = Object.fromEntries(
components.map(c => [c.id, {...c, children: []}])
);
components.forEach(c => {
if (c.children) {
map[c.id].children = c.children.map(id => map[id]);
}
});
return map;
}
Rendering
function renderNode(node) {
const Comp = REGISTRY[node.type];
if (!Comp) return null;
return (
<Comp {...node.props}>
{node.children?.map(renderNode)}
</Comp>
);
}
This demonstrates:
- Schema → component mapping
- No execution from stream
- Registry-enforced safety
Gateway Implementation Pattern
The gateway protects the renderer from malformed intent.
Validation Steps
- Schema structure verification
- Component whitelist enforcement
- Depth/size limits
- Property sanitization
- Version normalization
Example Outline
function sanitizeSchema(input) {
validateStructure(input);
enforceAllowedTypes(input.components);
limitTreeDepth(input);
return normalize(input);
}
This layer is critical in enterprise deployments.
Interaction Loop
sequenceDiagram participant User participant Renderer participant Gateway participant Agent User->>Renderer: Click Renderer->>Gateway: Event JSON Gateway->>Agent: Sanitized Event Agent->>Agent: Reason + Tools Agent-->>Gateway: Schema Gateway-->>Renderer: Stream Updates
Optimistic Rendering
Renderers often:
- show placeholders
- animate loading surfaces
- patch incremental updates
This maintains responsiveness during reasoning latency.
State Hydration
Restoring UI state after refresh is non-trivial.
flowchart TD Refresh --> SessionID SessionID --> Agent Agent --> Snapshot Snapshot --> Renderer
Typical implementation:
- Renderer sends session ID
- Agent returns last schema snapshot
- Renderer rebuilds tree
Requires persistent schema storage.
Security Boundary Model
flowchart LR
Agent –> Gateway –> Renderer –> Registry –> UI
Key enforcement points:
- Gateway sanitization
- Registry type enforcement
- No dynamic code execution
Operational Considerations
Real deployments must address:
- Schema versioning
- Audit logging
- Observability tracing
- Latency budgeting
- Access controls
- Accessibility validation
These often dominate production readiness discussions.
Limitations and Tradeoffs
UX Coherence Drift
Generated layouts may lack visual consistency.
Debugging Difficulty
Reproducing stateful layouts requires trace capture.
Accessibility Risk
Runtime generation complicates guarantees.
Latency Sensitivity
Reasoning loops affect UX.
Observability Complexity
Tracking intent evolution is nontrivial.
Alternative Architectures
Not all systems adopt full generative rendering:
- Deterministic UI per tool
- Hybrid AI-selected screens
- Schema-constrained form generation
Choice depends on workflow variability.
Open Questions
Areas still evolving:
- Registry vocabulary sizing
- Static schema validation
- Streaming cache models
- Accessibility enforcement
- Cost-efficient diff encoding
These questions will shape production maturity.
Conclusion
Protocol-driven UI generation repositions the interface as a dynamic boundary between reasoning systems and rendering environments.
It does not replace traditional UI architecture, but expands design options where workflow evolution outpaces interface iteration cycles.
The future likely includes hybrid models — deterministic where stability matters, generative where adaptability provides leverage.
What remains clear is that UI is increasingly becoming a negotiated runtime surface, and protocols may shape that transition as much as frameworks.
