How can spatial computing and AI assistants be combined to create truly immersive experiences?
In 2026, the convergence of spatial computing and AI assistants is redefining how we interact with the digital world. Thanks to protocols like the Model Context Protocol (MCP) and faster, more affordable models like Gemini 3.7 Flash, developers can now create contextual assistants that understand coordinates, meshes, and real-time sensory inputs.
Why is the combination of spatial computing and AI assistants a game-changer?
Recent industry developments highlight three key trends that make this combination so powerful:
2026 Trend: MCP and cost reduction (Okta)
Okta has announced that identity-scoped tool lists based on the Model Context Protocol can significantly reduce token costs for AI agents. Each model call can now include limited spatial context, avoiding redundant requests and optimising billing.
Agentic AI in drug discovery (Novo Nordisk + AWS)
Novo Nordisk is adopting agentic AI on AWS for target identification and therapy development. Agents operate within 3D environments, analysing molecular structures and suggesting new candidate molecules in real time.
Gemini 3.7 Flash: A faster, cheaper model for spatial interfaces
Google has released Gemini 3.7 Flash, a reasoning-optimised model that handles text, images, and 3D data at $0.75 per million input tokens. This affordability makes it ideal for AI assistants that need to process high-resolution spatial streams.
What is Model Context Protocol (MCP) and when should you use it?
The Model Context Protocol (MCP) defines a set of tools that can be invoked by an LLM, keeping context limited to a specific scope (e.g., location, spatial layer). It is particularly useful when:
- The assistant needs to modify or query a 3D scene.
- Low latency is required for real-time interactions.
- You want to limit the amount of tokens sent to the model to control costs.
Below is an example of an MCP definition for a 3D modelling assistant:
// Define an MCP tool for 3D object transformation
const mcpTransform = {
name: "transform_object",
description: "Rotate or move a 3D object within the scene",
parameters: {
type: "object",
properties: {
objectId: { type: "string" },
translation: { type: "array", items: { type: "number" } },
rotation: { type: "array", items: { type: "number" } }
},
required: ["objectId"]
}
};How to create a spatial AI assistant: A step-by-step guide
Step 1: Define the spatial context
Identify the coordinates, coordinate systems, and sensory data that the assistant will use. Use WebXR or native SDKs (Unity, Unreal) to expose this information as device resources.
Step 2: Design the prompt
Prompts for spatial assistants always include explicit references to position, scale, or orientation. Example:
"Place a sphere with a radius of 0.5 metres in front of me (at a distance of 2 metres) and colour it blue."The prompt must include exact numerical values to avoid ambiguity.
Step 3: Integrate with the rendering engine
Connect the MCP tool defined in Step 2 to the rendering engine. When the assistant returns a command, the engine applies it instantly to the scene.
Example prompt for an AR navigation assistant
A user wearing an AR headset asks: "Show me the shortest path to exit this room without crossing the table." The system processes the prompt by including the user's position, the room's topology (extracted via spatial sensors), and generates a 3D path that is displayed as an animated line.
Example prompt for a 3D design assistant
In a collaborative design environment, a designer says: "Add a window of 1 metre x 1.2 metres on the north wall, aligned with the top edge of the desk." The AI assistant uses MCP to modify existing geometry, update materials, and flag any collision conflicts.
Practical use cases and actionable takeaways
- Reduce token costs:Use identity-scoped MCP tool lists to send only the necessary spatial data to the model.
- Accelerate time-to-market:Direct integration with Unity/Unreal via MCP for interactive prototypes in minutes.
- Improve usability:Contextual prompts reduce interpretation errors, making spatial interfaces more intuitive.
- Scalability:Gemini 3.7 Flash handles large meshes and high-resolution sensor streams without exhausting the budget.
- Collaboration:AI agents can co-design with humans, offering real-time suggestions within the same 3D scene.
Conclusion
2026 offers powerful tools for merging spatial computing and AI assistants. By leveraging the Model Context Protocol, optimised models like Gemini 3.7 Flash, and the latest agentic AI platforms, you can create immersive experiences that are both cost-effective and highly responsive. Start with a simple MCP tool, add a contextual prompt, and watch your 3D scene come to life.
Next steps:Set up a basic MCP server, integrate a spatial headset (WebXR or Unity AR), and experiment with prompts that include explicit coordinates. The future of immersive assistants begins with a single line of code.
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 (August 2026): Here are some recent trends and news stories to inspire you: - Okta targets AI agent token costs with MCP scoping: Okta says identity-scoped Model Context Protocol (MCP) tool lists can reduce AI agent token costs. Each model call made by an AI agent can include sch... [2026-08-13] - Google AI Just Released Gemini 3.7 Flash: A Coding and Agent Model at $0.75/1M Input Tokens: Google has released Gemini 3.7 Flash, a refinement of Gemini 3.6 Flash with algorithmic improvements to its reasoning core. It handles text, images, a... [2026-08-13] - Samsung health AI models analyse wearable biosignal data: Samsung Research America’s Digital Health Team has presented two AI foundation models designed to learn from wearable biosignals. The work centres o... [2026-08-14] Use this current information as inspiration to create an original and relevant prompt for 2026.