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Documentation Index

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Create user prompts to guide how a simulated user interacts with your agent, then evaluate the results using defined metrics. Useful for quality assurance and catching regressions before deployment.

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Setup

1

Create a new test case

Click AI Simulated Chat to create a new test case.
2

Define the user prompt

Write a prompt describing the simulated user’s identity, goal, and personality. Recommended format:
## Identity

Your name is Mike.
Your date of birth is June 10, 1999.
Your order number is 7891273.

## Goal

Your primary objective is to return the package you received and get a refund.

## Personality

You are a patient customer. However, if the conversation becomes too long or
complicated, you will show signs of impatience. If the issue remains unresolved,
you may become frustrated and angry.
3

Select LLM model

Choose which LLM model to use to generate the simulated user conversation.
4

Run the simulation

Click Test to start the conversation.
5

Review the results

Manually review the conversation to identify any issues.
6

Save as test case

Click Save to preserve the test case for future regression runs.
7

Define evaluation metrics

Add metrics to automatically score the conversation. Recommended format:
1. Verify that the customer successfully returned the package and received a refund.
2. Confirm that the end_call function was called at the end of the conversation.
3. Ensure the agent's responses are conversational and contain 5 sentences or fewer.
8

Configure variables and function mocks (optional)

Specify dynamic variables to use during testing. Set up mocks for custom functions to prevent real calls during testing and ensure consistent results across runs.
Save your best test cases — they become your regression suite. Run them after every prompt or function change to catch breakage early.