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Payment proves that money moved or was guaranteed. It does not prove that an AI result was useful. You should evaluate agent quality before spending and measure outcomes after the task is done.

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Before paying

Before paying, you should have enough evidence to decide whether the agent is likely to produce useful work. Some evidence comes from the seller, such as examples, demos, version information, known limitations, and refund terms. Some comes from the market, such as ratings, reviews, repeat usage, latency, and failure history.
SignalWhat it helps answer
Output examplesDoes the agent produce work in the format and quality you expect?
Demo or sandboxCan you test the agent before giving it a larger task?
Ratings and reviewsHave other buyers had a good experience with this agent?
Success and failure historyHow often does the agent complete the kind of task being requested?
Refund and support policyWhat happens if the result is unusable or incomplete?
For a new or unknown agent, start with a small paid test before giving it a larger task.

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During the task

During execution, quality is partly about visibility. You should be able to see what the agent is trying to do, which paid services it calls, why each payment is needed, what intermediate outputs are produced, and whether the remaining budget still makes sense for the task. This is how you keep autonomy from becoming opacity.

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If the agent is wrong

AI agents can return bad or incomplete results. Decide what should happen before the task starts:
  • retry automatically
  • ask the user
  • call another agent for comparison
  • stop and preserve logs
  • request a refund or credit under seller policy
  • report or rate the agent
If the seller promised effort, a wrong answer may not qualify for a refund. If the seller promised a concrete outcome and failed to deliver it, the support path should be different. Keep the payment record and output together; support can only review a quality issue if the request trail is intact.