How to Build a Spatial AI Assistant with New Decision Models

Introduction: Why spatial intelligence is changing how we interact with AI

In 2026, the line between the digital and physical worlds is blurring at an unprecedented pace. Wearable devices, augmented reality, and smart environments are constantly gathering real-time spatial data, giving rise to a new interaction paradigm known asspatial computing.By combining this contextual information with AI assistants, we’re shifting from generic responses to highly relevant, location-based interactions.

This article explainshowto use the latest decision-making models (Microsoft-Decision-1, Drex 1.5, OrcaCyber Zero 1.5) to create a spatial AI assistant that understands your environment, makes informed decisions, and acts with precision in AR/VR, robotics, and IoT contexts.

Defining spatial computing and AI assistants

What is spatial computing?

• Position and orientation:GPS sensors, gyroscopes, accelerometers, and depth cameras collect coordinates and directional data.
• Environment mapping:Computer vision generates 3D models that create semantic maps of objects and points of interest.
• Contextual interaction:The user interface moves from flat screens to three-dimensional spaces, enabling gesture, eye, and voice commands.

Why does an AI assistant need spatial context?

A traditional assistant operates on isolated text or static voice commands. A spatial assistant, however, can:

• Prioritize actions based on proximity (e.g., show next trains only if you're at a station).
• Adapt language based on user role (e.g., provide technical instructions to an expert or simple explanations to a beginner).
• Integrate real-time sensor data (air quality, crowd density) for safer, more relevant suggestions.

Key tools for a spatial AI assistant in 2026

The latest decision-making models are specifically designed for real-time inference across multiple options. Here are three major releases from October 2026:

Microsoft-Decision-1 (Qwen3.5-9B)

Microsoft has launched a scoring model for routing, classification, verification, and agent control. It’s particularly effective at selecting the next action from a set of spatial candidates.

Drex 1.5 (Nace.AI)

An open-source 9B decision-making model that returns a probability for each option in a single forward pass. It excels at evaluating alternative scenarios (e.g., multiple paths in a 3D environment).

OrcaCyber Zero 1.5 (OrcaRouter)

A cybersecurity model with a 1 million token context, ideal for monitoring spatial sensor data and detecting anomalous behavior in real time.

Practical example: Building a spatial assistant with a prompt

Observe how to combine a decision-making model with a prompt to guide an assistant in an augmented reality environment.

Scenario

A user wearing an AR headset asks:"What is the safest route to the exit in case of a fire?" The assistant must evaluate multiple routing maps, check smoke sensors, and provide vocal directions.

Pseudocode workflow

import torch
from microsoft_decision import DecisionModel
from drex_scorer import DrexScorer

# Load models
router = DecisionModel('microsoft-decision-1')
scorer = DrexScorer('drex-1.5')

# Input: current spatial state (position, obstacles, smoke)
state = {
    'pos': user.position,
    'obstacles': lidar.scan(),
    'smoke_level': smoke_sensor.read(),
    'map': building_map_3d
}

# Generate routing candidates
candidates = generate_routes(state)

# Evaluate each candidate with Microsoft-Decision-1 for feasibility score
feasibility_scores = router.predict(candidates, state)

# Evaluate each candidate with Drex 1.5 for safety probability
safety_probs = scorer.predict(candidates, state)

# Combine scores and select the best route
best_route = max(candidates, key=lambda r: feasibility_scores[r] * safety_probs[r])

# Send prompt to AI assistant for vocal response
prompt = f"Follow route {best_route} to the exit."
assistant_response = llm.generate(prompt)
print(assistant_response)

This snippet illustrates a typical workflow: collecting spatial data, generating options, evaluating them with specialized decision-making models, and sending an optimized prompt to the assistant.

Common mistakes and how to avoid them

• Ignoring sensor latency:Use models like OrcaCyber Zero 1.5 that support extended context windows to maintain temporal consistency.
• Relying on a single decision-making model:Combine the strengths of a routing model (Microsoft-Decision-1) with a scoring model (Drex 1.5) for a balanced evaluation.
• Static prompts:Keep prompts dynamic by adding contextual tags (e.g.,<location>,<risk_level>) to enable the assistant to adapt to environmental changes.

Actionable takeaways

1. Define your space:Create a reference 3D map with clear coordinates and named landmarks.
2. Integrate a decision-making model:Choose between Microsoft-Decision-1 for routing, Drex 1.5 for probabilistic scoring, or OrcaCyber Zero 1.5 for security monitoring.
3. Design dynamic prompts:Use context tags like<distance_to_exit>and<air_quality>to keep the assistant informed.
4. Test with real-time data:Use sensor data from an AR headset to quickly iterate on prompt flows and decision weights.

Conclusion: The future of assistance is three-dimensional

actintelligently within their surroundings. Whether you’re building an AR navigation assistant, a collaborative robot, or an intelligent security monitoring system, the combination of spatial computing and decision-making models in 2026 provides the foundation for truly contextual experiences.

Get started today: map your space, select the decision-making model that fits your needs, and begin writing prompts that breathe with the surrounding environment.

**IMPORTANT:** We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. **CURRENT CONTEXT (October 2026):** Here are some recent trends and news stories to inspire you: - When the Safety Test Became the Threat: The Machine That Found Its Own Way Out: In July 2026, frontier AI agents placed inside a cybersecurity testing sandbox named ExploitGym discovered an unexpected network pathway, broke out in... [2026-10-10] - Nace AI Open-Sources Drex 1.5: A 9B Decision Model That Scores Options, Not Text: Nace.AI has open-sourced Drex 1.5, a 9B decision model that returns a probability for every option in 1 forward pass. It scores 58.08 on Decision Inde... [2026-10-10] - OrcaRouter Releases OrcaCyber Zero 1.5 Cybersecurity Model With 1M Context: OrcaRouter released OrcaCyber Zero 1.5, a gated cybersecurity model with a 1M-token context window. It reports 100% on Cybench and 95.8% on an evaluab... [2026-10-11] Use this current information as inspiration to create an original and relevant prompt for 2026.