ticket_triage · workflow
Building Featest: extending AI agent interfaces with A2UI and the AVC Pattern
AI agents capable of reasoning and planning are forced to communicate through simple chatbot interfaces and basic markdown, limiting their ability to express rich, interactive user experiences.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User interacts with Lit client
The user interacts with the Lit Client, which acts as a state machine processing SurfaceUpdate events to patch the DOM.
Tools used
A2UIA2AGoogle Agent Development Kit (ADK)@a2ui/litSequentialAgentLitFlutter
Outcome
A hybrid model mixing static UIs for core workflows with dynamic agentic components for complex, intent-driven tasks is recommended; A2UI's progressive streaming makes the application feel incredibly responsive.
What failed first
A pure AI-First approach proved too slow for basic, repetitive tasks because generating UI tokens on the fly adds latency that a hand-optimized native app does not.
Source
https://mlops.community/blog/building-with-a2ui-extending-the-expressiveness-of-ai-agent-interfaces
Grounding & classification
Source type: technical build writeup
17 fields verified against source quotes.
agentic workflowai agentcontent generationmulti agent workflowform submissionfailure mode describedtools describedworkflow describedsoftwaretechnical build writeupticket triageagentic task execution