> ## Documentation Index
> Fetch the complete documentation index at: https://docs.egma.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Built-in graders

Open **Graders** in your project. **Active graders** shows the graders used by
that project. **Grader library** contains the graders you can add.

## Expected behaviors

Egma adds **Expected behaviors** to every project. It checks the behavior
statements from the exact test version used by each simulation.

It reads the conversation's transcript, ending outcome, tool names and
arguments, and observed metrics. Tool return payloads are not sent to the LLM.
When agent evidence is available, it uses the agent's account of the turns and
calls. See
[Check which evidence arrived](/guides/start-and-follow-a-run#check-which-evidence-arrived)
if a tool call is missing or the transcript is incomplete.

Each statement gets a decision and supporting evidence. The grader's
score is:

```text theme={null}
passed statements / total statements
```

For example, three passing statements out of four give a score of `0.75`. The
default pass threshold is `1`, so all statements must pass for this grader's
individual result to pass.

Expected behaviors grades chat and voice simulations. Its scope always covers
all simulations and excludes production conversations. You can change the
language model and pass threshold, but you cannot remove it or change its
scope. A custom clone is independent and can have its own scope.

If the evidence does not let the grader decide a statement, the grade has an
error and no overall score. An unknown result is not counted as a failed
statement.

## Response latency

**Response latency** checks the p90 response time across measured conversation
turns. It returns `1` when the observed p90 is at or below your maximum, or `0`
when it exceeds the maximum.

For voice simulations, response time runs from the end of the simulated
caller's played audio to the arrival of the agent's spoken reply. The caller's
audio includes its voice's trailing padding. For chat, it measures the wait
between the caller's message and the agent's reply.

To add it:

1. Open **Grader library** and find **Response latency**.
2. Open its menu and select **Use in project**.
3. Choose the simulations or production sample it should grade.
4. Set **Maximum response time (p90)** in milliseconds. The supplied value is
   `3000`.
5. Keep **Pass threshold** at `1` to require the latency check to pass, then
   select **Use in project**.

The maximum response time and the pass threshold are different settings. The
maximum checks an observed time in milliseconds. The pass threshold compares
the resulting score, `0` or `1`.

The p90 uses nearest rank. With fewer than ten measured turns, it is the
slowest measured turn. A conversation without valid response latency
measurements produces a grading error, not a passing score.

Simulation latency uses the simulated caller's measurements when available.
The agent's measurements stay separate under `otherPov`. Production latency
uses the agent's own evidence. LiveKit closes its speech spans after the
detector's silence wait, and the simulated caller also observes the playback
delay, so the two clocks can report different waits. Egma does not combine
their samples. The grader uses the same primary series shown in the metrics.

Response latency supports chat and voice when the required measurements are
present. You can change its scope and settings or remove it from a project.

## Add your own checks

Use [custom graders](/guides/custom-graders) for a rule that should apply across
multiple tests or production conversations. Keep scenario-specific requirements
in the test's expected behaviors.