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

# Simulation Overview

> Test your AI agents with Netra's simulation framework. Run multi-turn conversations with a simulated user to validate agent behavior before deploying.

AI agents don't just answer questions—they engage in complex, multi-turn conversations to achieve goals. Netra's Simulation framework lets you test these interactions systematically, simulating realistic user behaviors to validate your agent's performance before deployment.

<Card title="Quick Start: Simulation" icon="rocket" href="/quick-start/QuickStart_Simulation">
  New to simulations? Get your first simulation running in minutes.
</Card>

## Why Simulation Matters

Traditional testing falls short for conversational agents. Simulations provide a comprehensive way to test multi-turn interactions with realistic user behaviors:

| Question                                                 | What Netra Simulates                                  |
| -------------------------------------------------------- | ----------------------------------------------------- |
| Does my agent handle multi-turn conversations correctly? | Full conversation flows with simulated user responses |
| Can my agent achieve specific goals?                     | Goal-oriented scenarios with success/failure tracking |
| How does my agent perform with different user personas?  | Frustrated, confused, friendly, or neutral users      |

## Core Building Blocks

The Simulation suite is built on three interconnected pillars:

### Evaluators

[Evaluators](/Simulation/Evaluators) assess the entire conversation after it completes. Netra provides 8 preconfigured library evaluators in two categories:

| Category    | Evaluators                                                                                                                      |
| ----------- | ------------------------------------------------------------------------------------------------------------------------------- |
| **Quality** | Guideline Adherence, Conversation Completeness, Profile Utilization, Conversational Flow, Conversation Memory, Factual Accuracy |
| **Agentic** | Goal Fulfillment, Information Elicitation                                                                                       |

All evaluators use LLM-as-Judge with a default pass threshold of >= 0.6.

### Datasets

[Datasets](/Simulation/Datasets) are collections of simulation scenarios that define multi-turn conversation goals.

| Feature                  | Description                                                                        |
| ------------------------ | ---------------------------------------------------------------------------------- |
| **Multi-Turn Scenarios** | Define conversation goals with simulated user interactions                         |
| **User Personas**        | Choose from neutral, friendly, frustrated, confused, or custom personas            |
| **User Data & Facts**    | Provide context data and facts the agent must communicate correctly                |
| **Variable Mapping**     | Map evaluator inputs to scenario fields, agent responses, or conversation metadata |

### Test Runs

[Test Runs](/Simulation/TestRuns) execute your simulation scenarios, providing detailed conversation transcripts and evaluation results.

| Feature                     | Description                                                                 |
| --------------------------- | --------------------------------------------------------------------------- |
| **Conversation Transcript** | Full multi-turn dialogue between simulated user and agent                   |
| **Scenario Details**        | View goal, persona, user data, and fact checker configuration               |
| **Trace Integration**       | Link directly to execution traces for each turn to debug issues             |
| **Aggregated Metrics**      | View total cost, average latency, and pass/fail rates across the simulation |

## Use Cases

### Goal Achievement Testing

Validate that your agent can successfully complete user objectives:

1. Create scenarios with specific goals (e.g., "Get a refund from customer support")
2. Define what facts the agent must communicate
3. Run simulations and verify goal achievement across different personas
4. Analyze conversation transcripts to understand failure points

### Persona-Based Testing

Test agent performance with different user types:

1. Create datasets with various personas (frustrated, confused, friendly)
2. Run the same scenario across all personas
3. Compare results to identify which personas your agent handles poorly
4. Refine your agent based on insights

## Getting Started

<Steps>
  <Step title="Configure Evaluators">
    Set up [evaluators](/Simulation/Evaluators) to define your scoring criteria — choose from the library or create custom ones.
  </Step>

  <Step title="Create a Dataset">
    Build a [multi-turn dataset](/Simulation/Datasets) with simulation scenarios, user personas, and facts to verify.
  </Step>

  <Step title="Run Simulations">
    Execute your dataset and view conversation transcripts and results in [Test Runs](/Simulation/TestRuns).
  </Step>

  <Step title="Analyze and Improve">
    Use insights from simulations to refine your agent's behavior.
  </Step>
</Steps>

## Related

* [Evaluators](/Simulation/Evaluators) - Configure scoring logic and criteria
* [Datasets](/Simulation/Datasets) - Create multi-turn simulation scenarios
* [Test Runs](/Simulation/TestRuns) - Analyze simulation results and conversation transcripts
* [Traces](/Observability/Traces/overview) - Understand how simulations connect to trace data