How to Build an Approval Gate for AI-Generated Video Before Rendering

If an AI agent can create a video draft, put an approval gate in front of rendering: save the generated VideoJSON, validate the data and assets, let a person approve the preview, and only then enqueue an export. This gives you a useful stop point before a bad product claim, stale price, or missing asset becomes an expensive MP4.

You need a TypeScript service that can store a JSON document, a preview surface, and a rendering path. This guide uses VideoFlow Core as the structured authoring layer and its renderers for preview and export. The same idea applies whether the input comes from an LLM, a product catalog, or a form in your SaaS.

1. Treat the generated video as a draft document

Do not give the agent a render endpoint as its first destination. Ask it for a draft with a stable ID, a source-data revision, and a pending_review status. The important boundary is that the structured video—not the MP4—is the first artifact your system stores.

VideoFlow’s core compiles a video description to portable VideoJSON, which can then be previewed, edited, or rendered. That makes it a better review target than a one-way export. A minimal record might look like this:

type VideoDraft = {
  id: string;
  sourceRevision: string;
  videoJson: unknown;
  status: "pending_review" | "approved" | "rejected";
  approvedBy?: string;
  approvedAt?: string;
};

Keep sourceRevision tied to the catalog, CRM record, or brief that supplied the facts. If the source changes after generation, mark the draft stale rather than silently rendering it. Expected result: every preview has a traceable input and a status your queue can enforce.

2. Validate the draft before anyone reviews it

A human should review meaning and presentation, not discover that the document points to a missing image. Run inexpensive deterministic checks first: schema validity, required brand fields, allowed media domains, duration limits, caption presence when required, and the current source revision.

Use a small policy object rather than embedding business rules in a prompt. For example, a product-video template can require a title, price currency, one approved CTA, and media from your asset library. Reject or return the draft for repair when a rule fails. Do not attempt to infer approval from an agent’s confidence.

This is related to the practice of saving VideoJSON before choosing a renderer: rendering is a later delivery choice, not where the document begins. Expected result: your reviewer sees only complete candidates.

3. Give the reviewer a real preview and an explicit decision

Render a preview from the saved document, then show the source facts next to it: product name, locale, price, CTA, and source revision. VideoFlow’s DOM renderer is intended for live, scrubbable previews, while the optional React Video Editor can provide a multi-track editing surface when reviewers must change copy, timing, or media.

Your approval action should be boring and specific: Approve this revision or Request changes. Record the reviewer, timestamp, and document hash. If editing is allowed, create a new revision and send that revision through validation again. That avoids the common mistake of approving one preview and rendering a later, unreviewed mutation.

For a deeper ownership split, see how to separate video template data from user edits in React. Expected result: a reviewer can tell exactly what they approved and can safely send a specific correction back.

4. Make the render worker accept approved revisions only

The queue worker should receive a draft ID and expected document hash—not arbitrary JSON copied from the browser. Re-read the draft, confirm status === "approved", confirm the hash still matches, and then select the renderer.

Choose the renderer based on the delivery job:

• Use the browser renderer for an interactive, user-initiated export where keeping the source in the browser is valuable. Its cancellation support can be connected to the browser’s standard AbortController.
• Use the server renderer for APIs, scheduled jobs, batch production, or a durable queue.
• Use the DOM renderer for the preview; it is not a replacement for a background export worker.

For batch work, the same guard keeps a queue from running ahead of review—an extension of the approach in building a video rendering queue before the first batch export. Expected result: approved revisions render once, and unapproved drafts cannot enter the queue by accident.

5. Preserve the receipt after delivery

Store the output URL, renderer choice, document hash, template version, and source revision beside the approved draft. This is your release receipt when someone asks which copy or price appeared in a clip. If a source changes, create a new draft; do not overwrite the old approval.

This receipt also keeps automation editable. If a customer needs a correction, you can reopen the stored VideoJSON in an editor rather than recreating the project from a finished MP4. That is the same practical advantage behind letting customers edit AI-generated videos in a SaaS.

Start with one narrow template

Begin with a single low-risk template, such as a 10-second product highlight or monthly account recap. Require an approved source revision, validate the generated VideoJSON, and make the render worker accept approved hashes only. Then measure how often reviewers request changes before adding more templates or autonomous triggers.

The next action is simple: model pending_review and approved in your video-draft record, then connect your export button or queue to the approved state only. With VideoFlow’s JSON-first workflow, that gate can sit between generation, preview, editing, and delivery without forcing you to rebuild the video for each step.