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

# How does Agent Simulation Help Evaluate AI Agents?

> Maxim AI's Agent Simulation allows you to automate the testing of conversational AI by generating realistic, multi-turn interactions. Instead of relying solely on static datasets or manual testing, simulations create dynamic dialogues between your agent and a simulated user to stress-test behavior, context retention, and robustness.

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## Why Would I Use Simulations Instead of Just Manual Testing?

Manual testing is time-consuming and often fails to uncover edge cases. Simulations automate this process, allowing you to:

* **Test Context Retention:** Verify how your AI maintains context across multiple exchanges.
* **Evaluate Emotional Intelligence:** See how your agent responds to different user emotions (e.g., frustrated vs. happy) and behaviors.
* **Verify Business Logic:** Ensure the agent adheres to policies and uses business context correctly.
* **Identify Dead-Ends:** Spot conversation flows where the agent gets stuck or fails to resolve the user's issue.

## What Is the Difference Between Agent Simulation and Prompt Simulation?

Maxim offers two distinct simulation modes depending on what you are testing:

| Simulation Type       | Purpose                                                             | Best For                                                            |
| --------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------- |
| **Agent Simulation**  | Tests complete agent workflows with AI-generated user interactions. | Multi-agent systems, complex workflows, and full application logic. |
| **Prompt Simulation** | Tests individual prompts or prompt chains with AI-generated inputs. | Optimizing prompt performance and logic before integration.         |

## How Do I Create a Realistic Test Scenario?

To ensure your simulation mirrors real-world usage, you must define three key components:

<Steps>
  <Step title="The Scenario">
    Be specific about the situation you want to test. Vague scenarios lead to generic tests.
    <Info>***Example*** <br />
    "Customer requesting a refund for a defective laptop" or "New user needs help configuring account security settings."</Info>
  </Step>

  <Step title="The Agent Description">
    Provide a detailed description of your agent's purpose, capabilities, and tone. This helps the simulation generator understand what the agent *should* be doing.
    <Info>***Example*** <br />
    "Financial Advisor Agent: Helps with investment decisions, analyzes portfolios, and maintains a professional tone. Always recommends consulting a licensed advisor for major decisions."</Info>
  </Step>

  <Step title="The User Persona">
    Define the simulated user's emotional state and expertise level to test how your agent adapts.
    <Info>***Example*** <br />
    "Frustrated customer," "Non-technical user," or "Expert user with specific terminology."</Info>
  </Step>
</Steps>

## Can I Control the Length and Complexity of the Simulation?

Yes. In the **Advanced settings**, you can configure constraints to tailor the simulation to your needs:

* **Maximum Number of Turns:** Set a limit for the conversation length. If unset, the simulation runs until the conversation naturally concludes.
* **Reference Tools:** Attach specific [Prompt Tools](https://www.getmaxim.ai/docs/library/prompt-tools) you want to test within the simulation (e.g., an API for checking order status).
* **Reference Context:** Add [Context Sources](https://www.getmaxim.ai/docs/library/context-sources) (documents or knowledge bases) to guide the conversation.