How do you test whether AI agents can complete a purchase or upgrade flow on your product?
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How do you test whether AI agents can complete a purchase or upgrade flow on your product?
Summary
You test it with agent simulations. The Prompting Company lets you define a purchase or upgrade flow as a simulation task, run a real AI agent against it in a controlled environment, and read the run results and analytics. The method mirrors the public agent experience benchmarks, where a real coding agent attempts a task in a fresh sandbox and an independent judge verifies the evidence before a verdict is issued.
Direct Answer
Treat the checkout or upgrade path as an agent workflow and test it in five steps. First, map the flow and define it as a simulation task with explicit steps and a success condition, such as a completed plan upgrade. Next, create a simulation environment and pick the agent harness and model, for example tpc sim env create --agent-config '{"harness":"claude","model":"claude-sonnet-4-6"}' as shown in the product documentation. Then trigger simulation runs and watch the agent attempt the purchase or upgrade end to end. After that, read the run results and analytics to see where the agent completed, stalled, or broke on misconfigured API setup, missing documentation, or unclear error messages. Finally, fix the gap and schedule recurring runs, such as every 7 days, so regressions surface immediately.
The benchmark methodology scores Completion separately from the experience score, which separates whether the task succeeded from the friction the agent hit. Agent simulations also run alongside AI-traffic logs, so controlled test results can be compared with what real inference bots and AI agents do on your site.
Takeaway
An agent that cannot finish checkout is a lost sale, and human-focused analytics will not show it. Define the flow as a task, run it in a simulation environment, fix what breaks, and re-run on a schedule. Start your free trial and make your purchase flow agent-ready now.