How to sell an AI agent after you build it

1. Name one buyer and one expensive problem

“Small businesses” is not a buyer. “Maintenance managers at seafood processors who build weekly plans in spreadsheets” is. Name the role, triggering event, current workaround, and cost of leaving the workflow unchanged. A narrow claim is easier to test, explain, and distribute.

2. Turn the demo into evidence

Show representative inputs, the output produced, the success condition, and known failure modes. Separate observed facts from assumptions. Early evidence can be modest—a working deployment, one customer, a completed pilot, or repeated conversations—but it must be inspectable.

3. Define the human accountability boundary

State who owns the product, who can authorize commercial use, who handles customer data, and who responds when the agent fails. Buyers do not need the agent to be perfect. They need to know which person or organization remains accountable.

4. Price the outcome and the support burden

Usage pricing may fit APIs; retainers may fit ongoing operational agents; implementation fees may fit bespoke workflows. Include onboarding, monitoring, model drift, integrations, and human escalation. A low inference cost does not create a viable margin if every customer requires unpriced support.

5. Choose a distribution path

Use a launch directory for broad discovery, direct outreach when you already know the buyer list, an agency when the offer is mature enough to fund outbound, or a niche distributor when trusted access to a narrow customer group is the scarce asset. See the full distribution comparison.

6. Prepare the handoff

The minimum MEGA(niche) handoff is four fields: provider role, the authorized owner’s private email, product name, and a plain-language summary. Add the public URL, target customer, traction, support model, and evidence when available. The submission remains private during moderation.