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Metrics define how to evaluate whether a conversation was successful in agent simulations.

How metrics work

After each simulated conversation completes, an LLM judge reviews the full conversation transcript and evaluates it against each metric you have defined for the simulation. For every metric, the judge assigns a binary pass or fail result based on your evaluation instructions. This means your evaluation instructions should be written clearly enough for an LLM to make a definitive yes/no decision about whether the criteria were met.

Quickstart

Create a new metric

From the sidebar, click Metrics to view your existing metrics. Click Add metric to create a new metric.
Metrics

Add the name and evaluation instructions

Give a meaningful name to the metric (e.g. Data collection success) and define how to evaluate success.
Add metric dialog

Guidelines for evaluation instructions

Define clear, measurable criteria that an LLM can evaluate with a yes/no answer:
  1. Success conditions: what must happen for the metric to pass?
  2. Failure conditions: what indicates failure?
  3. Edge cases: any special considerations?
Example evaluation instructions:

Save the metric

Click Add metric to create the metric.

Best practices

Metrics vs personas vs scenarios

Example metrics

Data collection success

Issue resolution

Appointment booking

Next steps

Create personas

Define who your simulated users are

Create scenarios

Define what your simulated users should do