Outverse’s AI agents were resolving thousands of tickets across channels but the conversation data behind those resolutions was going underutilized.
Dashboards are great for high level metrics, but are rigid - conversational interfaces are great for queries that require flexibility in answers, but tiring to consume. Generative UI sits between the two : A richer experience that's easier to scan and angaging and flexible enough to assemble around whatever the user asks.
Prototype
I built the prototype in our internal design playground — a repo I created that mirrors our product surfaces using Next.js, ShadCN, and Tailwind. I used Chris Tate’s JSON Render as the foundation, which let the agent return structured UI (charts, metrics, drilldown tables) rather than just paragraphs. The frontend assembled those components dynamically from the agent’s JSON spec, making it feel like a product surface rather than a chatbot.
FIG 1 / WORKING PROTOTYPE USING OPENAI RESPONSES API & JSON RENDER
For data, I generated a realistic synthetic dataset - support conversations, CSAT scores, intents, channels, outcomes - so I could test whether the responses felt genuinely useful to a support operator.
MCP as an additional entrypoint
When it comes to being able to query your data, Its quickly becoming table stakes to allow your product to be queryable from other AI tools, think claude, co work, chatgpt, codex.
FIG 2 / PROTOTYPE MCP SERVER INSIDE OF CLAUDE
Agent native
Whilst I initially prototyped the VOC agent in our existing interface, I think soon most tools will look like users talking with agents that drive user interface. This was some exploration into how we could shift our interface to enable that!
FIG 3 / AGENT NATIVE UI ENTRYPOINT