How to Use AI Assistants in Spatial Computing: Practical Examples and Effective Prompts

How AI assistants are transforming spatial computing in 2026

AI assistants are rapidly becoming the bridge between the digital and physical worlds, thanks to the rise of spatial computing. Today, models like Microsoft-Decision-1 and Drex 1.5 enable assistants to understand spatial contexts, make decisions based on options, and operate in advanced security sandboxes like ExploitGym. This article demonstrates how to leverage these technologies with effective prompts and practical workflows.

What is spatial computing and why is it important for AI assistants?

Spatial computing goes beyond 2D interfaces, leveraging depth, movement, and positioning to create more immersive experiences. For AI assistants, this means being able to interpret visual, tactile, and spatial signals, providing contextual responses that work in real-world environments such as factories, hospitals, or retail. In 2026, this integration has become a standard for any assistant that wants to operate reliably in the physical world.

• Spatial perception: cameras, LiDAR, and inertial sensors provide 3D coordinates.
• Natural interface: gestures, voice, and gaze replace mouse and keyboard.
• Contextual decision-making: decision models evaluate options in real time.

Prompt engineering techniques for spatial environments

Designing prompts for assistants in spatial contexts requires explicit language that includes coordinates, directional references, and physical constraints. Here’s a practical template you can copy and adapt.

Example prompt for an AI assistant in spatial computing

Use this code as a starting point for building prompts that work with Microsoft-Decision-1 or Drex 1.5:

# Prompt for an assistant that places an object in a room
prompt = """
Goal: Place a container 3 meters northeast of the entrance, at a height of 1.2 meters.
Context:
- Entrance location: (0, 0, 0)
- Coordinate system: X east, Y north, Z up
- Landmarks: south wall, east window
- Constraints: minimum distance 2.5 m from furniture, maximum height 1.5 m.
Generate a motion trajectory for a mobile robot that respects the constraints.
"""
print(prompt)

Current trends: decision models like Drex 1.5 and Microsoft-Decision-1

Two key releases have defined the AI assistant landscape in spatial computing in 2026:

• Microsoft-Decision-1: a Qwen3.5-9B model optimized for routing, classification, and agent control. Ideal for planning movements in dynamic environments.
• Drex 1.5: a 9B open-source model that returns a probability for each option in a single forward pass, excellent for scenarios with many alternatives.

Both are integrated into major spatial computing SDKs, allowing developers to add decision-making capabilities without building custom pipelines.

Integration into agent workflows

When an AI agent receives a spatial prompt, it typically follows this pipeline:

1. Prompt parsing
2. Option generation
3. Evaluation
4. Selection and execution

This chain reduces latency to milliseconds, critical for real-time applications such as collaborative robotics or AR navigation.

Practical use cases and workflows

1. Warehouse logistics assistance

2. Healthcare inspection with augmented reality

An AR technician points the camera at a machine, the AI assistant recognizes components, identifies anomalies, and suggests maintenance steps based on precise coordinates.

3. Safety in the ExploitGym sandbox

In July 2026, AI agents tested in ExploitGym discovered unexpected network paths and automatically triggered countermeasures. The decision model evaluated dozens of possible responses, choosing the safest one based on predefined policies.

Key takeaways and next steps

• Always define explicit, coordinate-based prompts for AI assistants in spatial computing.
• Leverage decision models like Microsoft-Decision-1 or Drex 1.5 for rapid option evaluation.
• Integrate safety logic into prompt design; sandboxes like ExploitGym test your agents in realistic conditions.
• Stay updated on the latest open-source releases; the community is contributing new spatial datasets that improve model accuracy.

With these practices, you can build AI assistants that not only see the world in 3D, but act intelligently within it, ready for the challenges of logistics, healthcare, and security in 2026 and beyond.