How to Build Spatial AI Assistants with Code-as-World and EnvHarness

Introduction: Why Spatial Computing is Transforming AI Assistants

This article showshowCode-as-World,EnvHarness, and theMIT

How the Latest Tools Empower Spatial Assistants

Code-as-World: Turning Videos into Editable Physical Code

Code-as-World retrieves editable MuJoCo scene code from a real video and uses it for physics-based reasoning training. For an AI assistant, this means being able toimport real-world actionsand make them immediately executable in a simulation.

Example workflow

• Capture a short video of a robot walking through a maze.
• Send the footage to Code-as-World via a structured prompt.
• Receive a MuJoCo XML file that defines bodies, joints, and geometry.

The generated code can be used directly by an AI assistant to test behaviors or to generate additional 3D assets.

<mujoco>
  <worldbody>
    <geom name="floor" type="plane" size="10 10 0.1"/>
    <body name="robot" pos="0 0 0.5">
      <joint name="joint0" type="ball"/>
      <geom type="box" size="0.5 0.5 1"/>
    </body>
  </worldbody>
</mujoco>

Assistant prompt:

Prompt: "Convert this video of a walking robot into an editable MuJoCo model using Code-as-World. Provide the XML code and specify masses and joints."

EnvHarness: Making Static Environments Adaptive

Google AI Research released EnvHarness, a programmable layer that turns static worlds into dynamic training environments. For an AI assistant, this means being able tomodify parameters(lighting, physics, obstacles) in real time without regenerating the entire scene.

Example Python usage:

# Initialize an environment with EnvHarness
env = EnvHarness('my_env')
env.add_sensor('depth_camera')
env.set_reward_fn(lambda obs: np.sum(obs['height']))
# Run an episode
obs = env.reset()
for t in range(200):
    action = agent.act(obs)
    obs, reward, done, info = env.step(action)
    if done:
        break

MIT AI: Weather Forecasting Without Historical Data

MIT engineers have created a tool that predicts extreme weather events without ever seeing historical data. When integrated into a spatial AI assistant, it canrender 3D risk mapsover a real-world model, helping rescuers plan interventions in AR environments.

When to Use a Spatial AI Assistant: Real-World Use Cases

Designing AR Interfaces for Industrial Training

A manufacturer wants an assistant that can show complex maintenance steps directly over a production line. Using a prompt that includes spatial coordinates and 3D annotations, the assistant generates an AR overlay that follows the operator’s movements.

Simulation and Robotics

A robotics team uses Code-as-World to capture a real robot grasping objects and convert it into a MuJoCo simulation. The AI assistant can then iterate quickly on controller parameters without needing to recapture new videos.

Weather Forecasting and Emergency Response

During a storm, a spatial AI assistant displays a 3D risk map in an AR visor, highlighting safe routes and assembly points. The MIT forecasting model provides real-time predictions without relying on historical data.

Best Practices for Prompts: Building Spatial Context

• Define the coordinate system:specify origin, units, and orientation (e.g., planar XZ vs. global 3D).
• Specify layers:indicate whether you need background geometry, interactive objects, or UI elements in the foreground.
• Add constraints:mass, inertia, or speed limits for physics simulations.
• Request editability:ask the assistant to provide code that can be easily tweaked later.
• Use structured tags:``, ``, `` help the assistant understand the desired hierarchy.

Conclusion: The Future of AI Assistants in the Spatial World

Spatial computing and AI assistants are becoming a dynamic duo. With tools like Code-as-World, EnvHarness, and the MIT forecasting model, developers can now create experiences that not only answer questions butact in the physical world. Whether you’re designing AR training, training robots in simulation, or providing immersive risk maps, mastering prompts for these spatial ecosystems will give you a competitive edge in the tech landscape of 2026.

Start today: capture a short video, craft a clear prompt, and let a spatial AI assistant transform the footage into a working physics simulation. The future is three-dimensional, and the tools are already here.