How to Sell AI Agents: The Creator Side of the AI Store

TL;DR: Build an agent in Perspective AI, publish it to the AI Store, and it ranks on completed paid jobs rather than ratings. Buyers preview before cloning. Building costs nothing beyond the $14.99/mo subscription you already run your models on, because the agent draws on the same credits as everything else in one subscription. The creator payout mechanisms are documented but carry no published numbers yet.

The AI Store has two sides. Most coverage is about the buying side, cloning an agent someone else built and putting it to work. This page is about the other one: what actually happens when you publish. Perspective AI runs $14.99/mo on Starter with 250 credits, or $49.99/mo on Pro with 700, across every major model family in one subscription, and building an agent to publish uses the same account you already work in, so the models it calls are the ones you have already consolidated rather than a second vendor to sign up with. The important caveat goes first rather than last: the creator payout mechanisms are documented but carry no published numbers yet, so this page describes mechanisms and deliberately quotes no earnings.

What follows is the sequence in the order you would actually do it, with the unfinished parts marked as unfinished. Compare that with the standalone agent platforms, which charge their own subscription on top of whatever model access you already pay for. Related use cases sit in our use cases hub, and the credit arithmetic that governs the buying side is in how credits are priced across plans.

How Do You Sell an AI Agent on Perspective AI?

Build the agent in your own account, publish it to the AI Store, and let buyers preview it running before they clone it. Ranking comes from completed paid jobs. The payout mechanisms exist in design and are not yet numbered.

• Starter: best for building and testing an agent against your own real work, $14.99/mo. 250 credits a month across the whole catalog, which is the plan most creators will build on.
• Pro: best for running an agent hard enough to find where it breaks, $49.99/mo. 700 credits a month, and the tier a heavy tester or a small team ends up on.
• Enterprise: best for organisations publishing internally at scale, $499/mo. The tier to ask about if agents are going to a team rather than to a store.

Step One: Build the Agent Where a Buyer Can Watch It Run

The design decision that most affects how you should build is the preview. A prospective buyer can watch your agent behave before deciding to clone it, rather than paying and finding out.

That inverts the usual marketplace incentive. In a store where the description is all anyone sees before paying, the winning strategy is a persuasive description. In a store where behaviour is visible first, the description stops being the product. The practical consequences for a builder are specific:

• Design for a short, legible run. If the agent takes twenty turns to do anything visible, the preview is working against you. Make the first thing it does the thing that demonstrates it works.
• Narrow beats broad. An agent that does one job well is easy to evaluate in a preview. A general assistant is not, because there is nothing specific for the viewer to check.
• Give it the memory it needs, not the memory you can. Agents carry memory and tools, and every tool is a thing that can behave badly in front of a buyer.
• Test it against work you actually have. The failure mode is an agent that performs on the examples its builder invented and collapses on a real task.

None of that is unusual product advice. It is worth spelling out because most agent marketplaces reward the opposite behaviour, and habits carried in from those stores will underperform here. The mechanics of what an agent is, and how memory and tools attach to one, are covered in the agent lifecycle from preview to deployment.

Step Two: Publish It, and Understand What Ranks It

Publishing is permissionless. There is no approval queue and no curation gate, which means the store's quality problem has to be solved by ranking rather than by admission.

The ranking signal is paid, completed, undisputed jobs. Not votes, not stars, not review counts. The reasoning is straightforward: ratings are cheap to manufacture, and a job that someone paid for and that finished without a dispute is not. Real usage costs real money to fake, so real usage is the signal that survives contact with people trying to game it.

For a creator this changes the optimisation target in a way worth internalising. Under a ratings system, the goal is a large number of first impressions, because every trial is a chance at a five-star review. Under a completed-jobs system, the goal is repeat use. An agent that fifty people try once ranks below an agent that five people run every week. That points at narrow, boring, repeatable jobs rather than impressive demos, which is an unusual thing for a marketplace to reward and the main reason to build here differently from elsewhere.

Ownership sits alongside this. Clones are private to the people who made them, and a published agent is not something the platform can quietly reprice, restrict or take away. That is framed as a constraint on the platform rather than a benefit it hands out, which is the more durable form of the promise.

Step Three: Size the Demand Before You Size the Agent

Here is the arithmetic worth doing before you build anything, because it is the part nobody publishes and it is knowable today.

Every buyer in this store arrives with a credit budget, and those budgets are public. A Starter subscriber at $14.99/mo carries 250 credits. A Pro subscriber at $49.99/mo carries 700. So a Pro seat holds 2.8 times the monthly credit pool of a Starter seat while paying 3.3 times as much.

Two things follow for a creator. The first is that the addressable spend per buyer is a bounded number rather than an open wallet, and an agent whose job consumes a meaningful slice of a Starter's 250 credits is competing against everything else that subscriber wants to do that month. The second is that the ratio tells you where the heavier agents belong: Pro seats do not scale linearly with price, so the subscribers who can afford to run a credit-hungry agent regularly are a narrower group than the price difference suggests.

The design implication is unglamorous and useful. Agents that do a small job cheaply and often fit the shape of the demand. Agents that do one enormous job a month fit almost nobody, because the buyer has to give up most of their allowance to run it once. How the credit unit itself works, and why it is denominated in dollar terms at each model's real provider cost, is covered in the breakdown of what a credit actually is.

What Is Not Built Yet, Stated Plainly

This is the section most pages about creator economies leave out, and it is the one that matters most if you are deciding whether to spend a weekend on this.

The payout design is two-layer. When someone clones your agent, part of the one-time clone cost is routed to you as its creator. Beyond that, the creator keeps an ongoing share of the credits that every clone of the agent burns. Both of those are described in the product's own documentation as mechanisms still being built, set out as design rather than as finished parameters.

Which means there are no percentages. Not withheld, not commercially sensitive, not available on request. They do not exist as published numbers yet, and this page will not invent them. Anyone quoting you a royalty rate for this store is quoting something they made up.

The honest way to hold that: the agents themselves are the finished product and you can build, publish and run them today. The economics on top are a stated intention with a real design behind it and no numbers attached. Those are two different levels of certainty and they should drive two different amounts of investment. Anyone weighing this against running an open agent runtime themselves should price that side properly too, because self-hosting carries GPU bills and patching duty that a store listing does not, and our comparison of managed OpenClaw hosting lays out what that route costs a month.

Which Builder Should Start Now, and Which Should Wait

Start now if you were going to build the agent anyway. If you have a repetitive task in your own work that an agent would handle, building it pays for itself in your own time regardless of what happens on the creator side. Publishing it costs nothing extra, and the completed-jobs ranking means an agent that is genuinely useful accumulates the one signal that will matter whenever the economics do land. This reader has no downside case.

Start now if you want position in a ranking that rewards patience. A store that ranks on repeat paid usage rewards agents that have been quietly working for months over agents that launch loudly. That is a structural advantage available to early builders and it is not available later by definition.

Wait if the payout is the reason you would build. If the entire case for spending the weekend is a revenue number, there is no revenue number, and building against an unpublished rate is speculation rather than a plan. Nothing about the design is discouraging, but a design is not a rate card. Come back when the percentages are published, and treat that publication as the actual start date.

Wait if you were planning to publish at volume. Twenty thin agents was the winning strategy in ratings-based stores and is close to the worst strategy in a completed-jobs one, because none of them accumulates the repeat usage that ranks. One agent that gets used weekly is worth more here than twenty that get tried once, and that is a different production plan, not a smaller one.

If you are in the first two groups, the next thing to read is how agents handle memory, tools and channel deployment, since those constraints shape what is buildable. If you are evaluating the category more broadly first, the agent platforms built for business automation is the comparison to run, and what Perspective AI is covers the product the store sits inside.

Plan prices and credit allowances on this page were read from the live Perspective AI pricing page on 18 August 2026. The creator-economy mechanisms are as described in the product documentation on the same date and carry no published rates.

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