Use Coding Agents to Build an Interactive Product Demo

Use a coding agent to build an interactive product demo around one decision the visitor can make. Give the agent a clear starting state, a meaningful interaction and an observable result. Specify which parts run real application logic, which use sample data and which simulate a service that does not yet exist.

A polished demo is most useful when its boundaries are visible. A visitor should be able to explore the product idea without mistaking a canned result for a completed transaction or a working integration. The method below is a proposed build workflow, using a fictional product configurator as its example.

Instead of asking the agent to “make an impressive demo,” describe who is visiting, what they are deciding and what evidence the interface should give them. For a fictional desk configurator, the task might be choosing a size and finish, seeing the combination and saving a sample specification.

Specify the minimum complete path before asking for visual polish. The visitor should understand the starting choice, see the consequence of changing it and reach an outcome that can be inspected. A clickable button that opens another decorative panel may add interaction without helping the visitor decide anything.

OpenAI’s architectural visualization example describes iterating on an editable Blender scene with preview inspection. It also identifies an early part of the house that was represented only by an exterior volume. That is a useful distinction between a convincing visual and a fully developed product. It is a vendor-authored example, not a performance result reproduced here.

For every visible control, define the input, the change and the evidence that it worked. Specify empty, invalid and unavailable states alongside the happy path. A brief that defines only the successful screenshot leaves the agent to invent what happens when a visitor changes direction.

Give the agent a bounded implementation prompt. For the configurator, ask for a locally runnable demo, sample products, real selection logic, a visible sample-data label and a reset control. Require the saved specification to reflect the current selection. State that no purchase, stock reservation or message should be sent.

Acceptance should describe behavior rather than require one implementation. Anthropic’s tool evaluation guidance recommends verifiable outcomes and warns against overfitting to a particular valid path. The same principle helps here: the visitor’s task is the test.

Real behavior means the software performs the operation shown, such as recalculating a layout or generating a file from the current choices. Sample data means the inputs are fictional or fixed. Simulated behavior means the interface represents an outcome without executing the underlying service.

These categories can overlap. A real calculation can use sample prices. A working download can contain a fictional specification. The label should describe the consequence that matters to the user, such as “Sample estimate” or “Demo reservation; nothing is booked.”

Place the label at the relevant decision point, not only in a footer. If the interface shows success, its wording should reflect the actual behavior. A simulated payment should not leave a reviewer believing money moved.

Ask the agent to implement the selection and output path first. Review it in a browser, then add the visual treatment that makes the product understandable. This sequence makes it easier to detect whether visual changes break the working behavior.

Use the design materials you actually have: product images, dimensions, permitted fonts, interaction references and brand rules. Mark missing assets as placeholders. Do not let the agent invent a product specification merely because a layout needs text.

Keep the demo’s external dependencies proportionate to the question it needs to answer. If a live service is unnecessary, a local fixture can make the experience more repeatable. If a real integration is the point of the demo, test that boundary explicitly and explain its access requirements.

Our AI-built app ownership reference covers the later operational handover. The demo stage should establish which dependencies are already real and which remain implementation work.

Give a reviewer the visitor task and observe where they hesitate, choose the wrong control or misunderstand the result. Record the behavior before explaining the intended path. A demonstration led by its builder can hide missing instructions or a control that makes sense only to someone who knows the brief.

Check keyboard access, visible focus, readable labels and small-screen layout. Change a selection after reaching the result and confirm that the output updates. Reset, repeat and inspect the downloaded file. A screenshot is useful visual evidence but cannot establish those behaviors.

The screenshot evaluation guide explains the limits of image-only inspection. The game playtest guide covers a different kind of interactive loop. Here the test is whether a visitor understands a product choice and its represented consequence.

Deliver the runnable files, start instructions, sample-data definition and a concise behavior inventory. Separate checks actually performed from proposed future checks. If only the desktop path was exercised, say so; do not label the demo fully tested.

List the work required to turn the demo into a product: real data, authentication, billing, service integration or an operational owner, as applicable. A successful demo may answer a design question while leaving those requirements unresolved. That is a useful result when the decision is stated honestly.

For help turning the brief into a bounded build, see our AI transformation service. Begin the next phase from the accepted visitor journey and its evidence, rather than assuming the agent’s finished screen means the business is ready to operate.