An agent is different from a tool in one way that changes everything about pricing: it completes discrete units of work. A tool is something a person uses. An agent is something that does a job. That makes the unit of value unusually easy to identify — and unusually easy to charge for.
Start with the unit of work
Before anything else, name the thing your agent completes. A resolved support ticket. A qualified lead. A drafted contract. A reconciled invoice. A researched summary.
If you can name it, count it, and verify it, you have the strongest pricing position available in software: you can charge for the result rather than for access.
If you cannot — if the agent assists rather than completes, or its output needs human judgement before it counts — you are closer to a tool, and the models in the pricing comparison apply instead.
The models that suit agents
Per-outcome
Charge per completed unit. Per resolution, per lead, per task.
This makes the easiest business case a buyer will ever see: compare your fee to what the same work costs them now. If a support resolution costs them $6 in staff time and you charge $2, the arithmetic makes the decision.
What you take on is risk. Failed attempts consume compute and earn nothing, so your margin is a function of your accuracy. Improving the agent improves the business directly, which is a healthy incentive but an uncomfortable dependency early on.
Define the outcome precisely and in writing before launch. What counts as "resolved"? Who decides? Ambiguity here becomes a billing dispute later.
Credits
Customers buy a balance; each run draws it down according to complexity.
A good fit when runs vary a lot in cost, or when the outcome is real but hard to verify automatically. Less compelling to the buyer than outcome pricing, but far easier to operate.
Subscription with a quota
A monthly fee including a number of runs, with overage beyond.
Suits agents used steadily by a team. Predictable for both sides, and the quota prevents one enthusiastic customer from inverting your margins.
Per-seat
Generally the weakest fit. Seats measure people, and an agent's cost has little to do with how many people are watching it. It can work when the agent is embedded in a workflow tool people log into, but it disconnects your revenue from your costs.
Turning a working agent into a business
A functioning agent is a product. It becomes a business when three things are true.
It solves a problem someone is already paying to solve. The best agent opportunities replace an existing line item — a contractor, an outsourced process, a task consuming a salaried person's week. If nobody currently pays anything for this work, you are creating a market as well as a product, which is a much longer road.
Its reliability is known. A buyer will not put an agent into a real workflow without knowing how often it is right and what happens when it is not. Publish your failure modes. An agent that says "I could not complete this, here is why" is far more deployable than one that fails silently.
It can be handed over. If it needs you to operate it, you have a service. Services are a legitimate business, but they scale with your time rather than your compute.
Where agents get sold
Agent-specific marketplaces have matured quickly, with direct purchase, subscription, and one-time unlock support. Platform galleries reach developers already inside an ecosystem. Enterprise channels reach buyers with the budgets to pay for outcomes at volume, and the procurement processes to match.
There is more detail on the trade-offs in where to sell AI apps. The short version: agents doing a business-process job usually justify the enterprise route; agents doing a demonstrable, self-contained task do better in agent marketplaces where a short interaction sells the product.
Publishing an agent well
Discovery inside a marketplace is its own problem. What helps:
Name the job, not the technology. "Reconciles Stripe payouts against your ledger" beats "AI-powered financial agent."
Show a real run. A recorded interaction with genuine input and output persuades far more than a feature list.
State the boundaries. What it does not do, what it needs access to, what it costs to run.
Make the first result fast. Buyers judge agents on their first interaction, and a slow or hedging first response reads as unreliable.
Reliability is a commercial feature
For an agent, accuracy is not a technical detail. It is the product, and increasingly it is the pricing model.
If you charge per outcome, every failed attempt costs compute and earns nothing. Your gross margin is a direct function of your success rate. Going from 70% to 85% accuracy is not a quality improvement, it is a margin improvement of roughly the same proportion.
That has three consequences worth planning for.
Measure before you price. You need a real success rate on real inputs before committing to per-outcome pricing. Pricing against an assumed rate is how agent businesses discover they are paying customers to use them.
Fail loudly and cheaply. An agent that recognises it cannot complete a task and stops has cost you one attempt. One that produces confident nonsense costs you the attempt, the support conversation, and the trust. Detecting your own failure is worth building.
Publish the number. Buyers assume worse than reality when you are vague. A stated success rate, with the conditions it holds under, is a competitive advantage against every competitor who stays quiet.
What buyers ask before deploying an agent
Anything acting autonomously in a real workflow faces a predictable set of questions. Have answers ready:
What can it access, and what can it change?
What happens when it is uncertain — does it stop, ask, or guess?
Is there an audit trail of what it did?
Can a human review before actions take effect?
How do we turn it off?
Answering these plainly removes more friction than any feature. The gap between an impressive demo and a deployed agent is almost entirely made of these questions.
This is part of a broader guide to monetizing what you build with AI, which covers pricing, distribution, and discovery end to end.
Frequently asked questions
How do I price an AI agent?
If the outcome is countable and verifiable, price per outcome — it is the strongest business case. If not, credits or a subscription with a run quota are safer, because both keep your revenue attached to your costs.
Where can developers publish AI agents?
Agent marketplaces, platform galleries from the major developer clouds, enterprise app stores, and your own site. The right one depends on whether your buyer is a developer, a consumer, or a business with a procurement process.
How do I turn an AI agent into a business?
Find work someone already pays for, make the agent's reliability legible, and remove yourself from its operation. An agent that needs you is a service; one that does not is a product.
Kaino Marketplace is in beta for AI builders, founders, agencies, and teams creating AI-native software. You can register for access through the Kaino Marketplace waitlist.
There is no listing fee. Kainotomic takes 15% of what your agent earns and pays the remaining 85% monthly — so if the agent sits unused, it costs you nothing to have listed it.