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

# Anatomy of an Agent

export const AgentOptionalMethodsTable = () => {
  const rows = [{
    method: "display_icon",
    returns: "str",
    purpose: "Path to a 32x32 pixel icon."
  }, {
    method: "get_inputs()",
    returns: "List[str]",
    purpose: "Input devices to activate (for example: [\"micro\"])."
  }, {
    method: "uses_gaze()",
    returns: "bool",
    purpose: "Enable person-tracking eye movement."
  }];
  return <div className="interface-methods-table-wrap">
      <table className="interface-methods-table">
        <thead>
          <tr>
            <th>Method</th>
            <th>Returns</th>
            <th>Purpose</th>
          </tr>
        </thead>
        <tbody>
          {rows.map(row => <tr key={row.method}>
              <td>
                <span className="interface-method-pill">{row.method}</span>
              </td>
              <td>
                <span className="interface-param-badge">
                  {row.returns}
                </span>
              </td>
              <td>{row.purpose}</td>
            </tr>)}
        </tbody>
      </table>
    </div>;
};

export const AgentCoreMethodsTable = () => {
  const rows = [{
    method: "id",
    returns: "str",
    purpose: "Unique identifier (snake_case)."
  }, {
    method: "display_name",
    returns: "str",
    purpose: "Human-readable name shown in the app."
  }, {
    method: "get_skills()",
    returns: "List[str]",
    purpose: "Skills this agent can use."
  }, {
    method: "get_prompt()",
    returns: "str",
    purpose: "Personality and behavioral instructions."
  }];
  return <div className="interface-methods-table-wrap">
      <table className="interface-methods-table">
        <thead>
          <tr>
            <th>Method</th>
            <th>Returns</th>
            <th>Purpose</th>
          </tr>
        </thead>
        <tbody>
          {rows.map(row => <tr key={row.method}>
              <td>
                <span className="interface-method-pill">{row.method}</span>
              </td>
              <td>
                <span className="interface-param-badge">
                  {row.returns}
                </span>
              </td>
              <td>{row.purpose}</td>
            </tr>)}
        </tbody>
      </table>
    </div>;
};

Every agent follows the same structure. Once you understand it, you can create any agent you need.

## File Location

Your agents are stored in `~/innate-os/workspace/custom_agents/` on the robot (shipped agents live next door in `workspace/innate_agents/`). The system automatically discovers Python files in these directories—no registration or configuration required.

The directory is watched while the robot runs: new files and edits hot-reload automatically within a second or two — no restart required. (`innate service restart` remains the fallback if something doesn't get picked up.)

## Core Interface

Every agent implements four methods:

<AgentCoreMethodsTable />

Optional methods:

<AgentOptionalMethodsTable />

## Minimal Example

The simplest possible agent:

```python theme={null}
from typing import List
from brain_client.agent_types import Agent

class MyAgent(Agent):
    @property
    def id(self) -> str:
        return "my_agent"

    @property
    def display_name(self) -> str:
        return "My Agent"

    def get_skills(self) -> List[str]:
        return []

    def get_prompt(self) -> str:
        return "You are a robot."
```

This agent loads and runs, but does very little—it has no skills and a minimal prompt.

## Complete Example

A functional agent with skills, inputs, and a detailed prompt:

```python theme={null}
from typing import List
from brain_client.agent_types import Agent

class HelloWorld(Agent):
    """Greets visitors with a friendly wave."""

    @property
    def id(self) -> str:
        return "hello_world"

    @property
    def display_name(self) -> str:
        return "Hello World"

    @property
    def display_icon(self) -> str:
        return "assets/hello_world.png"

    def get_skills(self) -> List[str]:
        return ["innate-os/navigate_to_position", "innate-os/wave"]

    def get_inputs(self) -> List[str]:
        return ["micro"]  # enables the microphone — see below

    def get_prompt(self) -> str:
        return """
You are a friendly robot who greets people.

- Speak in a casual, warm tone
- If you don't see anyone, turn around to look for them
- When you see a person, wave and say hello
- Respond to what people say to you
"""

    def uses_gaze(self) -> bool:
        return True
```

This agent combines navigation and waving skills, microphone input, a friendly prompt, and gaze tracking for more natural interaction.

## Enabling the Microphone

If you want to **talk to your agent**, you must enable the microphone input — it is not on by default. Add `"micro"` to the list returned by `get_inputs()`:

```python theme={null}
def get_inputs(self) -> List[str]:
    return ["micro"]
```

That's all: when the agent starts, the runtime opens MARS's built-in microphone and everything you say is streamed into the agent's context as chat input. Without this, the agent only reacts to what it sees and to messages typed in the app.

The same mechanism works for any [input device](/software/inputs) (added sensors, network events, ...) — `"micro"` is just the built-in one. See [Example: Microphone](/software/inputs/example-microphone) for how it works under the hood.

## Writing Effective Prompts

The prompt determines how the robot behaves. Skills define what's *possible*; the prompt defines what *actually happens*.

A good prompt defines personality, goals, constraints, and strategy in plain language. Be specific. The AI interprets your prompt literally, and vague instructions produce inconsistent behavior.

## Template

Copy and modify this template for new agents:

```python theme={null}
from typing import List
from brain_client.agent_types import Agent

class MyCustomAgent(Agent):
    """One-line description of what this agent does."""

    @property
    def id(self) -> str:
        return "my_custom_agent"

    @property
    def display_name(self) -> str:
        return "My Custom Agent"

    @property
    def display_icon(self) -> str:
        return "assets/my_icon.png"  # Optional

    def get_skills(self) -> List[str]:
        return [
            "innate-os/navigate_to_position",
            "innate-os/wave",
            "innate-os/turn_and_move",
        ]

    def get_inputs(self) -> List[str]:
        return ["micro"]

    def get_prompt(self) -> str:
        return """
Describe who the robot is and what it should do.
Be explicit about personality, goals, strategy, and constraints.
"""

    def uses_gaze(self) -> bool:
        return True
```

## Deploy your agent

<Steps>
  <Step title="Save the file in the right place">
    Save your agent as `my_agent.py` in **`~/innate-os/workspace/custom_agents/`** on the robot. This is the one directory the system scans for your agents — anywhere else and it won't load.
  </Step>

  <Step title="Let hot reload pick it up">
    The runtime watches `custom_agents/` — your new agent loads automatically within a second or two of saving, and so does every later edit. If it ever fails to appear, restart the runtime as a fallback:

    ```bash theme={null}
    innate service restart
    ```
  </Step>

  <Step title="Check it appears in the app">
    Your agent shows up as a card on the app **Home** screen — pull down to refresh if needed. From there, tap to start it.
  </Step>
</Steps>