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

# Quickstart

> Run your first evaluation against captured traces or sessions in under 2 minutes

<Note>
  Before you begin, make sure you have:

  * A PandaProbe account. Sign up at [app.pandaprobe.com](https://app.pandaprobe.com).
  * At least one **trace** captured in your project. If you haven't set up tracing yet, follow the [Observability Quickstart](/tracing/get-started/quickstart) first.
  * For agent (session) evaluation: traces grouped under the same `session_id`.
</Note>

<Info>
  PandaProbe Cloud manages the evaluation LLM infrastructure for you. You do not need to bring your own LLM API key to run evaluations in PandaProbe Cloud.
</Info>

## Run your first evaluation

The fastest way to evaluate is directly from the dashboard. You pick a trace (or session), choose a metric, and PandaProbe runs the evaluation in the background.

<Steps>
  <Step title="Open the Traces tab">
    In the PandaProbe dashboard, open the **Traces** tab. You should see the traces that were captured by the SDK.
  </Step>

  <Step title="Select traces to evaluate">
    Pick one or more traces, then click **Evaluate**. You can also open a single trace and click **Evaluate** from the detail view.
  </Step>

  <Step title="Choose a metric">
    Start with `task_completion` — a 2-stage LLM-as-judge metric that scores whether the agent accomplished the user's objective.

    <Tip>
      You can run multiple metrics in the same eval run. Each one produces an independent score attached to the trace.
    </Tip>
  </Step>

  <Step title="Submit the run">
    Click **Submit**. PandaProbe creates an eval run with status `PENDING` and dispatches the work to a background worker. The API responds with `202 Accepted`.
  </Step>

  <Step title="Review the score">
    Open the trace once the run completes. You should see a score with a numeric value, a pass/fail status, a human-readable reason, and structured metadata explaining how the score was produced.
  </Step>
</Steps>

## Try session evaluation

If you have traces grouped under a `session_id`, you can evaluate the entire agent lifecycle:

<Steps>
  <Step title="Open the Sessions tab">
    Open **Sessions** to view grouped agent lifecycles.
  </Step>

  <Step title="Select a session and click Evaluate">
    Choose one or more sessions and click **Evaluate**.
  </Step>

  <Step title="Pick a session metric">
    Start with `agent_reliability` — it surfaces worst-case failure risk across the session by aggregating trace-level signals (`confidence`, `coherence`, `tool_correctness`, `loop_detection`).
  </Step>

  <Step title="Submit and review">
    Submit the run. When it completes, the session detail page shows the aggregated score along with the trace-level signals that produced it.
  </Step>
</Steps>

## What's next?

<CardGroup cols={3}>
  <Card title="Core Concepts" icon="shapes" href="/evaluation/get-started/concepts">
    Learn how eval runs, metrics, scores, signals, and monitors fit together.
  </Card>

  <Card title="Evaluation Approaches" icon="lightbulb" href="/evaluation/get-started/evaluation-approaches">
    Understand when to use trace vs. agent (session) evaluation.
  </Card>

  <Card title="Run via API" icon="terminal" href="/evaluation/setup/run-eval-api">
    Create eval runs programmatically from CI, notebooks, or internal tools.
  </Card>
</CardGroup>