Core Concepts

Understanding these foundational concepts will help you think like Meta-OS and leverage its full potential — whether you're a user or developer.

🎯

Intent-Based Architecture

The foundation of how Meta-OS works

Traditional operating systems are app-centric: you launch an app, navigate its UI, and manually complete tasks. Meta-OS is intent-centric: you express what you want, and the OS figures out how to do it.

Traditional OS

1. Open calendar app

2. Tap "New Event"

3. Enter event details

4. Open contacts

5. Find person

6. Send invite

Meta-OS

You: "Schedule lunch with Sarah tomorrow at noon"

✓ Done in one step

How Intent Resolution Works:

1. Parse: AI understands natural language → Extract entities (Sarah, tomorrow, noon, lunch)

2. Plan: Determine required capabilities → Need Calendar + Contacts

3. Orchestrate: Invoke agents in sequence → Lookup Sarah → Create event → Send invite

4. Confirm: Report results → "Added to calendar. Invite sent to Sarah."

⚡

Agents vs Apps

Capabilities, not applications

Apps are monolithic programs with their own UIs. Agents are lightweight capability providers that extend the OS without visible interfaces.

Characteristic	Traditional Apps	Meta-OS Agents
Size	50-500MB	1-10MB
Interface	Custom UI required	API-only, OS provides UI
Discovery	Browse app store	Voice + visual store
Interoperability	Limited, manual sharing	Built-in orchestration
Updates	Manual, user-initiated	Auto-update background

Example: Spotify Agent vs Spotify App

The Spotify agent (4.1MB) provides playback control via voice. When you say "Play my Discover Weekly," the agent handles authentication and playback — but the OS provides the interface (now playing card, controls).

The full Spotify app (200MB+) includes UI frameworks, images, animations — all unnecessary when the OS handles presentation.

🤖

The AI Orchestrator

Meta-OS's brain

The AI Orchestrator is the core intelligence layer that coordinates agents, maintains context, and ensures secure execution.

🧠 Context Management

Maintains conversational context across interactions. Knows "the movie" from earlier conversation, who "Sarah" is from contacts, what "tomorrow" means based on current time.

🔗 Agent Coordination

Determines which agents to invoke, in what order, and how to pass data between them. Handles failures gracefully (if restaurant booking fails, suggests alternatives).

🔐 Security Enforcement

Verifies each agent has permission before allowing access to data or capabilities. Sandboxes agent execution to prevent unauthorized access.

⚡ Performance Optimization

Balances on-device vs cloud AI, caches results, pre-loads likely-needed agents based on user patterns.

📋

Capability Declarations

Transparent permissions

Every agent must declare exactly what it can do in structured, human-readable format. No hidden permissions.

// Example: Weather Agent capability declaration

{

"agentId": "com.weatherco.weather",

"version": "1.2.0",

"capabilities": [

{

"type": "location.read",

"purpose": "Fetch weather for your current location",

"frequency": "on_demand"

},,

{

"type": "network.fetch",

"purpose": "Download weather data from api.weather.com",

"domains": ["api.weather.com"]

},,

{

"type": "notifications.send",

"purpose": "Alert you to severe weather",

"maxPerDay": 5

}

]

}

Users see this as:

This agent can:

✓ Access your current location (to fetch local weather)
✓ Download data from api.weather.com
✓ Send up to 5 notifications per day (severe weather alerts)

🔄

Agent Lifecycle

From discovery to uninstall

Discovery

Voice search ("I need a photo editor") or visual browse in Agent Store

Installation

Review capabilities → One tap to install → Agent registers with orchestrator

Invocation

Orchestrator calls agent when user intent matches capabilities

Execution

Agent runs in sandbox, accesses only declared capabilities, returns results

Updates

Auto-update in background (like Chrome extensions). New capabilities require re-approval.

Removal

Say "Remove [agent]" or long-press in store. All permissions instantly revoked.

📱

On-Device AI

Privacy through local processing

Meta-OS runs a powerful on-device AI model that handles most tasks locally, without sending data to the cloud.

On-Device (Private)

✓ Intent understanding

✓ Contact lookup

✓ Calendar operations

✓ Message drafting

✓ Basic Q&A

Cloud (When Needed)

• Complex reasoning

• Real-time data (weather, stocks)

• External API calls

• Agent downloads

Technical: Meta-OS uses quantized LLMs optimized for mobile hardware (similar to Apple's on-device models). Typical inference: 50-200ms for voice commands on device.

🔒

Next: Security Model

Now that you understand how Meta-OS works, let's dive into how it keeps your data private and secure.

Continue to Security Model →