Introduction: Why integrating AI assistants with spatial computing is crucial in 2026
Spatial computing is transforming how we interact with digital environments, bringing 3D interfaces, augmented reality (AR), and virtual reality (VR) into everyday workflows. By 2026, AI assistants have emerged as the bridge that turns this immersive technology into actionable results. Tools like Infini-AI-Lab’sHermit, Google DeepMind’sGemini 4 Carbonmodel, and theAssemblyAI-TTS-1API are making it easier than ever for developers and businesses to create intelligent, conversational, and voice-enabled spatial assistants.
This practical guide demonstrateshowto combine these technologies, offering real code examples and prompt engineering tips to build AI assistants that understand, act, and communicate in the language of space.
Fundamentals of spatial computing and AI
What makes spatial environments unique
- Depth and coordinates:Every object has a position (x, y, z) and often a surface normal.
- Persistent context:The scene remains intact even after interface resets, maintaining state across sessions.
- Multimodal input:Head movements, gestures, eye-tracking, and voice commands are all valid inputs.
The role of LLMs in spatial contexts
ALarge Language Model (LLM)like Gemini 4 Carbon excels at:
- Interpreting ambiguous textual instructions into concrete spatial actions.
- Generating dynamic descriptions for 3D objects in real time.
- Maintaining long-term memory of user goals within a scene.
When combined with aspatial sensor(e.g., a depth camera or mapping API), an LLM can power an assistant that says"Show me the office shelf on the left"...> and then points precisely in that direction.
Tools and frameworks for integration in 2026
Hermit: The adapter harness for spatial agents
Infini-AI-Lab’s Hermit is anopen-source adapter harness (Apache-2.0)that enables exposing capabilities, memory, and tools to an AI agent. In spatial computing, Hermit can:
- Register custom plugins (e.g., calculate 3D intersections, query databases).
- Manage astate machinethat maps spatial events (detected entities, orientation changes) to agent actions.
- Facilitatetool-callingfrom an LLM to rendering engines like Unity, Unreal, or WebGL.
Example of defining a spatial agent with Hermit:
import hermit
# Define the spatial agent
agent = hermit.Agent(
name="spatial_assistant",
llm="gemini-4-carbon",
tools=[
hermit.Tool("query_scene", "Returns the coordinates of an object by name"),
hermit.Tool("render_annotation", "Draws a 3D annotation in the scene")
],
memory=hermit.Memory(store="vector_db")
)
# Hook for spatial sensor updates
agent.on_event("object_detected", handle_object_detection)This code creates an agent that can call external functions based on sensor data while maintaining a vector context for future recall.
Gemini 4 Carbon: Advanced model for spatial understanding
Gemini 4 Carbon achieves a score of59 on the Artificial Analysis Intelligence Indexand, making it ideal for tasks requiring precise spatial reasoning. When designing prompts for a spatial assistant, always include:
- Explicit coordinate references(e.g., “at the northeast corner”).
- Desired units of measurement(meters, feet, degrees).
- Contextual constraints(e.g., “inside room 3A, no obstacles”).
Example prompt for Gemini 4 Carbon:
prompt = (
"You have access to a 3D map of the current room. "
"The user asks: 'Show me the monitor at eye level.' "
"Return the coordinates (x, y, z) and orientation of the object closest to that location."
)Conclusion:Use these steps as a foundation, adapting tools, policies, and controls to your organization’s real-world context.
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: - Block Unveils Dessa, a Full-Duplex Speech Model Trained Only on Speech: Block’s AI lab released Dessa, a full-duplex speech-to-speech model trained exclusively on human conversation audio. It runs locally on a MacBook Pro M4 Ma... [2026-10-29] - AssemblyAI Ships AssemblyAI-TTS-1: A $15 per 1M Character Text-to-Speech API for Voice Agents: AssemblyAI has released AssemblyAI-TTS-1, its first text-to-speech model for voice agents. It costs $15 per 1M characters and offers unlimited concurrency... [2026-10-29] - Infini-AI-Lab Releases Hermit, an Open-Source Harness Adapter Framework for AI Agents: Infini-AI-Lab released Hermit, an Apache-2.0 harness adapter framework that trains an external layer of instructions, skills, memory, and tools from the base model... [2026-10-29] Use this current information as inspiration to create an original and relevant 2026 prompt.