The GenUI Approval Checklist: Five Artifacts for Shipping Generative UI

The generative UI demo is rarely the problem. Everyone in the room wants the upside: faster experiences, more personalized interfaces, and new ways to connect users to products and information. The project stalls in the meeting after the demo, when product, engineering, and compliance stakeholders ask what would need to be true for this to be safe enough to ship. That is the right question, and a new checklist from Very Good Ventures answers it with artifacts instead of assurances.

Approval gets easier the moment teams stop describing GenUI as magic and start showing how it is constrained, grounded, and checked. Generative UI is a way for a language model to emit an interface in terms of components an app already knows how to render, often through the A2UI protocol. The stakes are higher than chat because the output is an interface, and users act on interfaces.

Three Failure Modes Reviewers Care About

Collapsing GenUI risk into the single word "hallucinations" is too vague to act on. VGV's Brian Egan splits the problem into three categories, and a single scenario shows why the distinction matters: a customer asking their bank's GenUI app for the fastest way to pay down a credit card.

Content hallucinations make up facts, products, policies, or answers, like a well-formed offer card for a balance-transfer product with a 0% intro rate the bank does not offer. Structural hallucinations generate UI that does not match the approved catalog, returning plain text the client cannot render, inventing components, or picking a valid widget that fails the job.

Intent hallucinations respond outside the job: the customer types "waive my late fees for the year," the model complies, and the app renders a confirmation card for a waiver that was never approved. No single defense catches all three. A team that only addresses accuracy while ignoring structure, permissions, and fallbacks has de-risked only one third of it.

Five Artifacts Instead of Assurances

Product leaders should walk into review with artifacts, not a vision deck. The checklist starts with a constrained component catalog: the model composes only from approved building blocks with a clear registry, defined allowed properties, and examples of valid compositions. This is the main defense against structural hallucinations, and it makes output auditable.

Second is grounding to verifiable IDs and source systems. The model references a product ID and the application fetches the real record from the system of record. Tools handle data the model must fetch during generation, while context injection covers facts the server already knows at request time, with one guardrail: never inject secrets or unscreened client input.

• Constrained catalog: the model composes only from approved components
• Grounding: facts come from verifiable IDs and source systems, not model memory
• Validation: every output is checked against the catalog and schema before render
• Access control: every tool call and data fetch runs through authentication and RBAC
• Fallbacks: safe refusal, fallback UI states, and escalation paths are part of the architecture

Every output gets validated against the catalog and schema before render, with a response validator that rejects a miss and retries once as a safety net. The conversation changes when the answer is not "we prompt it carefully" but "we validate every output before render," and teams should also show what happens when validation fails.

Access control sits on every data and tool path. A major failure mode is the model retrieving information it should never have touched: if a user cannot see a record, the model cannot retrieve it on that user's behalf, and every access is auditable. When the interface is dynamically generated from model-linked tools, permissions are part of the safety model.

The Questions an Approval-Ready Team Can Answer

The last artifact, and the one teams neglect most, is fallbacks for uncertainty, invalid output, and off-intent requests. This is where intent hallucinations get caught, at both edges of the request: screening the prompt coming in and the response going out. The check adds latency, so the tradeoff must be weighed per product, but approval-ready systems always have explicit failure design.

• What is the model allowed to generate, and what is it not allowed to generate?
• Where do facts come from, and how are inputs and outputs sanitized?
• How is UI output validated before render, and what happens when validation fails?
• What data can the model access, and how are permissions enforced?
• What is the fallback when output is uncertain, invalid, or off-intent?

GenUI does not become safer because a team says it is controlled; it becomes safer when the controls are explicit, reviewable, and enforced. Each artifact answers a question no other can: the catalog sets what the model may generate, grounding sets where facts come from, validation stands between a bad generation and the screen, access control limits what the model touches, and fallbacks cover everything else. A team missing one artifact walks into review with an open question, and open questions are where approvals stall.