The Complete Guide to Magentic UI: From Beginner to Hero

Introduction: What is Magentic UI?
Understanding the Core Concepts
Setting Up Your Development Environment
Project 1: AI-Powered Task Manager
Project 2: Smart Content Generator
Project 3: Conversational Data Explorer
Advanced Patterns and Best Practices
Integration with AI Services
Production Deployment
Resources and Further Learning

Introduction: What is Magentic UI?

Magentic UI is an experimental framework from Microsoft Research that represents a paradigm shift in how we build human-AI collaborative interfaces. Instead of traditional UI where humans click buttons and fill forms, Magentic UI creates conversational, adaptive interfaces where AI and humans work together as partners.

The Problem with Traditional UI

Think about traditional web applications:

Static interfaces: Fixed layouts, predefined workflows
Human-driven: Users must learn the interface
Limited flexibility: Can’t adapt to user context or needs
High cognitive load: Users must navigate complex menus and forms

The Magentic UI Solution

Magentic UI flips this model:

Dynamic interfaces: UI components generated on-demand
AI-human partnership: AI suggests, humans approve/modify
Context-aware: Adapts to user intent and situation
Natural interaction: Conversational, intuitive workflows

The Mental Model: Imagine having a smart assistant that not only understands what you want but also creates the perfect interface for you to accomplish it, right when you need it.

Why Magentic UI Matters

Reduced Learning Curve: No complex UIs to learn
Increased Productivity: AI handles repetitive tasks
Better Accessibility: Adapts to different user abilities
Future-Proof: Works with any AI model or service
Developer Efficiency: Less boilerplate UI code

Understanding the Core Concepts

1. Agents and Capabilities

In Magentic UI, agents are AI entities that can perform specific tasks:

// Example agent definition
const taskAgent = {
  name: 'TaskManager',
  capabilities: [
    'create_task',
    'list_tasks',
    'update_task',
    'delete_task',
    'search_tasks'
  ],
  model: 'gpt-4',
  context: 'task_management'
}

Capabilities define what an agent can do:

Actions: Specific functions the agent can execute
Knowledge: Domain expertise and data access
Interface: How the agent presents information

2. UI Generation and Rendering

Magentic UI dynamically generates UI components based on:

Agent capabilities: What actions are available
User context: Current task and preferences
Conversation state: What has been discussed
Data requirements: What information is needed

// Dynamic UI generation
const uiComponents = await magenticUI.generateInterface({
  agent: taskAgent,
  context: userContext,
  intent: 'create_new_task'
})
// Returns: React components, forms, buttons, etc.

3. Conversation Flow

The interaction follows a natural conversation pattern:

User expresses intent (natural language)
AI clarifies requirements (asks questions)
AI generates UI (appropriate components)
User interacts (completes the task)
AI processes results (takes action)

4. State Management

Magentic UI maintains:

Conversation history: Context for ongoing interactions
Application state: Current data and UI state
User preferences: Personalization and settings
Agent memory: What has been accomplished

Setting Up Your Development Environment

Prerequisites

# Node.js 16+ required
node --version  # Should be v16.0.0 or higher

# npm or yarn
npm --version

Installing Magentic UI

# Create new project
npx create-react-app magentic-ui-demo
cd magentic-ui-demo

# Install Magentic UI
npm install @microsoft/magentic-ui
npm install @microsoft/magentic-ui-react

# Install dependencies
npm install openai
npm install @tanstack/react-query
npm install tailwindcss
npm install lucide-react

Basic Configuration

Create src/magenticConfig.js:

import { MagenticUIConfig } from '@microsoft/magentic-ui'

export const magenticConfig = new MagenticUIConfig({
  // AI service configuration
  aiProvider: {
    type: 'openai',
    apiKey: process.env.REACT_APP_OPENAI_API_KEY,
    model: 'gpt-4-turbo-preview',
    temperature: 0.7,
    maxTokens: 2000
  },
  
  // UI generation settings
  uiGeneration: {
    framework: 'react',
    styling: 'tailwind',
    componentLibrary: 'lucide',
    adaptiveLayout: true
  },
  
  // Agent configuration
  agents: {
    defaultModel: 'gpt-4-turbo-preview',
    maxConcurrent: 3,
    timeoutMs: 30000
  },
  
  // Conversation settings
  conversation: {
    maxHistoryLength: 50,
    contextWindow: 10000,
    enableMemory: true
  }
})

Environment Setup

Create .env.local:

# OpenAI API Key
REACT_APP_OPENAI_API_KEY=your_openai_api_key_here

# Optional: Custom backend URL
REACT_APP_MAGENTIC_BACKEND_URL=http://localhost:3001

# Development settings
REACT_APP_DEBUG_MAGENTIC_UI=true
REACT_APP_ENABLE_CONVERSATION_LOG=true

Basic App Structure

Update src/App.js:

import React from 'react'
import { MagenticProvider } from '@microsoft/magentic-ui-react'
import { magenticConfig } from './magenticConfig'
import './index.css'

function App() {
  return (
    <MagenticProvider config={magenticConfig}>
      <div className="min-h-screen bg-gray-50">
        <header className="bg-white shadow-sm border-b">
          <div className="max-w-7xl mx-auto px-4 py-4">
            <h1 className="text-2xl font-bold text-gray-900">
              Magentic UI Demo
            </h1>
          </div>
        </header>
        
        <main className="max-w-7xl mx-auto px-4 py-8">
          {/* Your Magentic UI components will go here */}
        </main>
      </div>
    </MagenticProvider>
  )
}

export default App

Project 1: AI-Powered Task Manager

Let’s build a task manager where users can create, manage, and organize tasks through natural conversation.

Step 1: Define the Task Agent

Create src/agents/TaskAgent.js:

import { Agent } from '@microsoft/magentic-ui'

export const taskAgent = new Agent({
  name: 'TaskManager',
  description: 'Manages tasks, projects, and productivity workflows',
  capabilities: [
    {
      name: 'createTask',
      description: 'Create a new task with title, description, priority, and due date',
      parameters: {
        title: { type: 'string', required: true },
        description: { type: 'string', required: false },
        priority: { type: 'enum', options: ['low', 'medium', 'high'], default: 'medium' },
        dueDate: { type: 'date', required: false },
        category: { type: 'string', required: false }
      }
    },
    {
      name: 'listTasks',
      description: 'List tasks with optional filtering',
      parameters: {
        status: { type: 'enum', options: ['all', 'pending', 'completed'], default: 'all' },
        category: { type: 'string', required: false },
        priority: { type: 'enum', options: ['low', 'medium', 'high'], required: false }
      }
    },
    {
      name: 'updateTask',
      description: 'Update an existing task',
      parameters: {
        taskId: { type: 'string', required: true },
        updates: { type: 'object', required: true }
      }
    },
    {
      name: 'deleteTask',
      description: 'Delete a task',
      parameters: {
        taskId: { type: 'string', required: true }
      }
    },
    {
      name: 'searchTasks',
      description: 'Search tasks by content',
      parameters: {
        query: { type: 'string', required: true },
        limit: { type: 'number', default: 10 }
      }
    }
  ],
  
  // Data source
  dataSource: {
    type: 'localStorage',
    key: 'tasks',
    schema: {
      id: 'string',
      title: 'string',
      description: 'string',
      status: 'enum:pending,completed',
      priority: 'enum:low,medium,high',
      category: 'string',
      createdAt: 'date',
      dueDate: 'date',
      updatedAt: 'date'
    }
  },
  
  // UI preferences
  uiPreferences: {
    layout: 'kanban-board',
    defaultView: 'board',
    enableDragDrop: true,
    showProgress: true
  }
})

Step 2: Create Task Manager Component

Create src/components/TaskManager.js:

import React, { useState, useEffect } from 'react'
import { useMagenticUI } from '@microsoft/magentic-ui-react'
import { taskAgent } from '../agents/TaskAgent'
import TaskBoard from './TaskBoard'
import ConversationInterface from './ConversationInterface'

function TaskManager() {
  const [tasks, setTasks] = useState([])
  const [currentView, setCurrentView] = useState('board')
  const { 
    startConversation, 
    generateInterface, 
    executeCapability,
    conversation 
  } = useMagenticUI()

  // Load tasks from localStorage on mount
  useEffect(() => {
    const savedTasks = localStorage.getItem('tasks')
    if (savedTasks) {
      setTasks(JSON.parse(savedTasks))
    }
  }, [])

  // Save tasks to localStorage whenever they change
  useEffect(() => {
    localStorage.setItem('tasks', JSON.stringify(tasks))
  }, [tasks])

  // Handle task operations
  const handleTaskOperation = async (operation, params) => {
    try {
      const result = await executeCapability(taskAgent, operation, params)
      
      switch (operation) {
        case 'createTask':
          setTasks(prev => [...prev, { ...result, id: Date.now().toString() }])
          break
          
        case 'updateTask':
          setTasks(prev => prev.map(task => 
            task.id === params.taskId 
              ? { ...task, ...params.updates, updatedAt: new Date() }
              : task
          ))
          break
          
        case 'deleteTask':
          setTasks(prev => prev.filter(task => task.id !== params.taskId))
          break
          
        case 'listTasks':
          let filteredTasks = result
          if (params.status !== 'all') {
            filteredTasks = filteredTasks.filter(task => task.status === params.status)
          }
          setTasks(filteredTasks)
          break
          
        default:
          console.log('Unknown operation:', operation)
      }
      
      return result
    } catch (error) {
      console.error('Task operation failed:', error)
      throw error
    }
  }

  // Start conversation with task agent
  const startTaskConversation = async () => {
    await startConversation(taskAgent, {
      context: {
        currentTasks: tasks,
        taskCount: tasks.length,
        pendingCount: tasks.filter(t => t.status === 'pending').length
      },
      greeting: "Hi! I'm your task management assistant. What would you like to accomplish today?",
      suggestions: [
        "Create a new task",
        "Show me my pending tasks",
        "Find high-priority tasks",
        "Help me organize my week"
      ]
    })
  }

  return (
    <div className="flex h-screen">
      {/* Main Task Interface */}
      <div className="flex-1 p-6">
        <div className="mb-6 flex justify-between items-center">
          <h2 className="text-2xl font-bold text-gray-900">Task Manager</h2>
          
          <div className="flex gap-2">
            <button
              onClick={() => setCurrentView('board')}
              className={`px-4 py-2 rounded-lg font-medium ${
                currentView === 'board' 
                  ? 'bg-blue-600 text-white' 
                  : 'bg-gray-200 text-gray-700'
              }`}
            >
              Board View
            </button>
            <button
              onClick={() => setCurrentView('list')}
              className={`px-4 py-2 rounded-lg font-medium ${
                currentView === 'list' 
                  ? 'bg-blue-600 text-white' 
                  : 'bg-gray-200 text-gray-700'
              }`}
            >
              List View
            </button>
            <button
              onClick={startTaskConversation}
              className="px-4 py-2 bg-green-600 text-white rounded-lg font-medium hover:bg-green-700"
            >
              💬 AI Assistant
            </button>
          </div>
        </div>

        {/* Dynamic UI based on current view */}
        {currentView === 'board' ? (
          <TaskBoard tasks={tasks} onTaskUpdate={handleTaskOperation} />
        ) : (
          <TaskList tasks={tasks} onTaskUpdate={handleTaskOperation} />
        )}
      </div>

      {/* Conversation Interface */}
      {conversation.isActive && (
        <div className="w-96 border-l bg-gray-50">
          <ConversationInterface 
            agent={taskAgent}
            onExecuteAction={handleTaskOperation}
            tasks={tasks}
          />
        </div>
      )}
    </div>
  )
}

export default TaskManager

Step 3: Create Conversation Interface

Create src/components/ConversationInterface.js:

import React, { useState, useRef, useEffect } from 'react'
import { useMagenticUI } from '@microsoft/magentic-ui-react'
import { Send, Bot, User, Sparkles } from 'lucide-react'

function ConversationInterface({ agent, onExecuteAction, tasks }) {
  const [input, setInput] = useState('')
  const [isTyping, setIsTyping] = useState(false)
  const messagesEndRef = useRef(null)
  
  const { 
    conversation, 
    sendMessage, 
    generatedUI,
    executeCapability 
  } = useMagenticUI()

  // Auto-scroll to bottom
  useEffect(() => {
    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
  }, [conversation.messages])

  const handleSendMessage = async () => {
    if (!input.trim()) return

    const userMessage = {
      type: 'user',
      content: input,
      timestamp: new Date()
    }

    setInput('')
    setIsTyping(true)

    try {
      // Send message and get AI response
      await sendMessage(input)
      
      // Check if AI wants to execute a capability
      if (conversation.lastMessage?.intent?.action) {
        const { action, parameters } = conversation.lastMessage.intent
        
        // Execute the action
        await executeCapability(agent, action, parameters)
          .then(result => {
            // Send confirmation back to conversation
            sendMessage(`I've ${action.replace(/([A-Z])/g, ' $1').toLowerCase()} the task successfully.`, 'system')
          })
          .catch(error => {
            sendMessage(`Sorry, I encountered an error: ${error.message}`, 'error')
          })
      }
    } catch (error) {
      console.error('Conversation error:', error)
    } finally {
      setIsTyping(false)
    }
  }

  const handleKeyPress = (e) => {
    if (e.key === 'Enter' && !e.shiftKey) {
      e.preventDefault()
      handleSendMessage()
    }
  }

  const handleSuggestionClick = (suggestion) => {
    setInput(suggestion)
  }

  return (
    <div className="flex flex-col h-full">
      {/* Header */}
      <div className="p-4 border-b bg-white">
        <div className="flex items-center gap-2">
          <Bot className="w-5 h-5 text-blue-600" />
          <h3 className="font-semibold text-gray-900">Task Assistant</h3>
        </div>
        <p className="text-sm text-gray-600 mt-1">
          {agent.description}
        </p>
      </div>

      {/* Messages */}
      <div className="flex-1 overflow-y-auto p-4 space-y-4">
        {conversation.messages.map((message, index) => (
          <div key={index} className={`flex gap-3 ${
            message.type === 'user' ? 'flex-row-reverse' : 'flex-row'
          }`}>
            <div className={`w-8 h-8 rounded-full flex items-center justify-center ${
              message.type === 'user' 
                ? 'bg-blue-600 text-white' 
                : 'bg-gray-200 text-gray-700'
            }`}>
              {message.type === 'user' ? (
                <User className="w-4 h-4" />
              ) : (
                <Bot className="w-4 h-4" />
              )}
            </div>
            
            <div className={`max-w-[80%] rounded-lg p-3 ${
              message.type === 'user'
                ? 'bg-blue-600 text-white'
                : message.type === 'error'
                ? 'bg-red-100 text-red-800'
                : 'bg-gray-100 text-gray-900'
            }`}>
              <p className="text-sm">{message.content}</p>
              <span className="text-xs opacity-70 mt-1 block">
                {new Date(message.timestamp).toLocaleTimeString()}
              </span>
            </div>
          </div>
        ))}

        {/* Generated UI Components */}
        {generatedUI && (
          <div className="bg-blue-50 border border-blue-200 rounded-lg p-4">
            <div className="flex items-center gap-2 mb-3">
              <Sparkles className="w-4 h-4 text-blue-600" />
              <span className="text-sm font-medium text-blue-900">
                Suggested Action
              </span>
            </div>
            {generatedUI}
          </div>
        )}

        {/* Typing indicator */}
        {isTyping && (
          <div className="flex gap-3">
            <div className="w-8 h-8 rounded-full bg-gray-200 flex items-center justify-center">
              <Bot className="w-4 h-4" />
            </div>
            <div className="bg-gray-100 rounded-lg p-3">
              <div className="flex gap-1">
                <div className="w-2 h-2 bg-gray-400 rounded-full animate-bounce" />
                <div className="w-2 h-2 bg-gray-400 rounded-full animate-bounce" style={{ animationDelay: '0.1s' }} />
                <div className="w-2 h-2 bg-gray-400 rounded-full animate-bounce" style={{ animationDelay: '0.2s' }} />
              </div>
            </div>
          </div>
        </div>
        )}

        <div ref={messagesEndRef} />
      </div>

      {/* Suggestions */}
      {conversation.suggestions && conversation.suggestions.length > 0 && (
        <div className="p-4 border-t bg-gray-50">
          <p className="text-sm text-gray-600 mb-2">Suggested actions:</p>
          <div className="flex flex-wrap gap-2">
            {conversation.suggestions.map((suggestion, index) => (
              <button
                key={index}
                onClick={() => handleSuggestionClick(suggestion)}
                className="px-3 py-1 text-sm bg-white border border-gray-300 rounded-full hover:bg-gray-50"
              >
                {suggestion}
              </button>
            ))}
          </div>
        </div>
      )}

      {/* Input */}
      <div className="p-4 border-t bg-white">
        <div className="flex gap-2">
          <input
            type="text"
            value={input}
            onChange={(e) => setInput(e.target.value)}
            onKeyPress={handleKeyPress}
            placeholder="Ask me anything about your tasks..."
            className="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500"
            disabled={isTyping}
          />
          <button
            onClick={handleSendMessage}
            disabled={!input.trim() || isTyping}
            className="px-4 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700 disabled:opacity-50 disabled:cursor-not-allowed"
          >
            <Send className="w-4 h-4" />
          </button>
        </div>
      </div>
    </div>
  )
}

export default ConversationInterface

Step 4: Create Task Board Component

Create src/components/TaskBoard.js:

import React, { useState } from 'react'
import { Card, CardContent, CardHeader } from './ui/Card'
import { Badge } from './ui/Badge'
import { Calendar, Clock, Flag } from 'lucide-react'

function TaskBoard({ tasks, onTaskUpdate }) {
  const [draggedTask, setDraggedTask] = useState(null)

  const columns = [
    { id: 'pending', title: 'To Do', color: 'border-gray-300' },
    { id: 'in-progress', title: 'In Progress', color: 'border-blue-300' },
    { id: 'completed', title: 'Done', color: 'border-green-300' }
  ]

  const getTasksByStatus = (status) => {
    return tasks.filter(task => task.status === status)
  }

  const handleDragStart = (task) => {
    setDraggedTask(task)
  }

  const handleDragOver = (e) => {
    e.preventDefault()
  }

  const handleDrop = async (e, status) => {
    e.preventDefault()
    if (draggedTask && draggedTask.status !== status) {
      await onTaskUpdate('updateTask', {
        taskId: draggedTask.id,
        updates: { status, updatedAt: new Date() }
      })
    }
    setDraggedTask(null)
  }

  const getPriorityColor = (priority) => {
    switch (priority) {
      case 'high': return 'bg-red-100 text-red-800'
      case 'medium': return 'bg-yellow-100 text-yellow-800'
      case 'low': return 'bg-green-100 text-green-800'
      default: return 'bg-gray-100 text-gray-800'
    }
  }

  const TaskCard = ({ task }) => (
    <Card 
      className="mb-3 cursor-move hover:shadow-md transition-shadow"
      draggable
      onDragStart={() => handleDragStart(task)}
    >
      <CardHeader className="pb-2">
        <div className="flex justify-between items-start">
          <h4 className="font-medium text-gray-900">{task.title}</h4>
          <Badge className={getPriorityColor(task.priority)}>
            {task.priority}
          </Badge>
        </div>
      </CardHeader>
      <CardContent>
        {task.description && (
          <p className="text-sm text-gray-600 mb-3">{task.description}</p>
        )}
        
        <div className="flex items-center gap-4 text-xs text-gray-500">
          {task.dueDate && (
            <div className="flex items-center gap-1">
              <Calendar className="w-3 h-3" />
              {new Date(task.dueDate).toLocaleDateString()}
            </div>
          )}
          
          <div className="flex items-center gap-1">
            <Clock className="w-3 h-3" />
            {new Date(task.createdAt).toLocaleDateString()}
          </div>
          
          {task.category && (
            <Badge variant="outline" className="text-xs">
              {task.category}
            </Badge>
          )}
        </div>
      </CardContent>
    </Card>
  )

  return (
    <div className="grid grid-cols-3 gap-6">
      {columns.map(column => (
        <div key={column.id} className="flex flex-col">
          <div className={`p-4 rounded-t-lg border-2 border-b-0 ${column.color}`}>
            <h3 className="font-semibold text-gray-900">
              {column.title}
              <span className="ml-2 text-sm text-gray-600">
                ({getTasksByStatus(column.id).length})
              </span>
            </h3>
          </div>
          
          <div 
            className={`flex-1 p-4 rounded-b-lg border-2 border-t-0 min-h-[400px] ${column.color}`}
            onDragOver={handleDragOver}
            onDrop={(e) => handleDrop(e, column.id)}
          >
            {getTasksByStatus(column.id).map(task => (
              <TaskCard key={task.id} task={task} />
            ))}
          </div>
        </div>
      ))}
    </div>
  )
}

export default TaskBoard

Project 2: Smart Content Generator

Let’s build a content generation tool where users can create blog posts, social media content, and marketing materials through AI collaboration.

Step 1: Define Content Generation Agent

Create src/agents/ContentAgent.js:

import { Agent } from '@microsoft/magentic-ui'

export const contentAgent = new Agent({
  name: 'ContentGenerator',
  description: 'AI-powered content creation for blogs, social media, and marketing',
  capabilities: [
    {
      name: 'generateBlogPost',
      description: 'Generate a complete blog post with outline and content',
      parameters: {
        topic: { type: 'string', required: true },
        tone: { type: 'enum', options: ['professional', 'casual', 'friendly', 'formal'], default: 'professional' },
        length: { type: 'enum', options: ['short', 'medium', 'long'], default: 'medium' },
        keywords: { type: 'array', required: false },
        targetAudience: { type: 'string', required: false }
      }
    },
    {
      name: 'generateSocialMedia',
      description: 'Create social media posts for different platforms',
      parameters: {
        content: { type: 'string', required: true },
        platforms: { type: 'array', options: ['twitter', 'linkedin', 'facebook', 'instagram'], required: true },
        tone: { type: 'enum', options: ['professional', 'casual', 'engaging', 'humorous'], default: 'casual' },
        includeHashtags: { type: 'boolean', default: true },
        includeEmojis: { type: 'boolean', default: true }
      }
    },
    {
      name: 'generateEmailNewsletter',
      description: 'Create an email newsletter campaign',
      parameters: {
        purpose: { type: 'string', required: true },
        audience: { type: 'string', required: true },
        keyPoints: { type: 'array', required: true },
        callToAction: { type: 'string', required: false },
        tone: { type: 'enum', options: ['professional', 'friendly', 'persuasive'], default: 'professional' }
      }
    },
    {
      name: 'optimizeSEO',
      description: 'Optimize content for SEO with meta tags and keywords',
      parameters: {
        content: { type: 'string', required: true },
        targetKeywords: { type: 'array', required: false }
      }
    },
    {
      name: 'generateOutline',
      description: 'Create a structured outline for long-form content',
      parameters: {
        topic: { type: 'string', required: true },
        type: { type: 'enum', options: ['blog', 'article', 'video', 'podcast'], default: 'blog' },
        depth: { type: 'enum', options: ['basic', 'detailed', 'comprehensive'], default: 'detailed' }
      }
    }
  ],
  
  // Templates and styles
  templates: {
    blogPost: {
      structure: ['introduction', 'mainPoints', 'conclusion'],
      minWords: 500,
      maxWords: 3000
    },
    socialMedia: {
      twitter: { maxLength: 280, includeImages: true },
      linkedin: { maxLength: 3000, professionalTone: true },
      facebook: { maxLength: 500, mediaRich: true },
      instagram: { maxLength: 2200, visualFocus: true }
    }
  },
  
  // UI preferences
  uiPreferences: {
    layout: 'split-view',
    showPreview: true,
    enableRealTimeGeneration: true,
    showWordCount: true,
    enableExport: ['pdf', 'docx', 'html', 'markdown']
  }
})

Step 2: Create Content Generator Interface

Create src/components/ContentGenerator.js:

import React, { useState, useEffect } from 'react'
import { useMagenticUI } from '@microsoft/magentic-ui-react'
import { contentAgent } from '../agents/ContentAgent'
import ContentEditor from './ContentEditor'
import ContentPreview from './ContentPreview'
import TemplateGallery from './TemplateGallery'
import { FileText, Share2, Download, Settings, Sparkles } from 'lucide-react'

function ContentGenerator() {
  const [activeTab, setActiveTab] = useState('generator')
  const [generatedContent, setGeneratedContent] = useState(null)
  const [isGenerating, setIsGenerating] = useState(false)
  const [selectedTemplate, setSelectedTemplate] = useState(null)
  
  const { 
    startConversation, 
    generateInterface, 
    executeCapability,
    conversation 
  } = useMagenticUI()

  const handleContentGeneration = async (contentType, params) => {
    setIsGenerating(true)
    
    try {
      const result = await executeCapability(contentAgent, contentType, params)
      setGeneratedContent(result)
      
      // Start conversation to refine content
      await startConversation(contentAgent, {
        context: {
          generatedContent: result,
          contentType: contentType,
          parameters: params
        },
        greeting: "I've generated your content! Would you like me to refine it or make any adjustments?",
        suggestions: [
          "Make it more engaging",
          "Add more details",
          "Change the tone",
          "Optimize for SEO",
          "Generate variations"
        ]
      })
    } catch (error) {
      console.error('Content generation failed:', error)
    } finally {
      setIsGenerating(false)
    }
  }

  const handleContentRefinement = async (refinement) => {
    if (!generatedContent) return
    
    setIsGenerating(true)
    
    try {
      const refinedContent = await executeCapability(contentAgent, 'refineContent', {
        originalContent: generatedContent,
        refinement: refinement
      })
      
      setGeneratedContent(refinedContent)
    } catch (error) {
      console.error('Content refinement failed:', error)
    } finally {
      setIsGenerating(false)
    }
  }

  return (
    <div className="flex h-screen bg-gray-50">
      {/* Sidebar */}
      <div className="w-80 bg-white border-r flex flex-col">
        <div className="p-6 border-b">
          <h2 className="text-xl font-bold text-gray-900 mb-2">
            Content Generator
          </h2>
          <p className="text-sm text-gray-600">
            AI-powered content creation for all your needs
          </p>
        </div>
        
        {/* Navigation */}
        <div className="flex-1">
          <nav className="p-4 space-y-2">
            {[
              { id: 'generator', label: 'Generate', icon: Sparkles },
              { id: 'templates', label: 'Templates', icon: FileText },
              { id: 'history', label: 'History', icon: Settings },
              { id: 'export', label: 'Export', icon: Download }
            ].map(tab => (
              <button
                key={tab.id}
                onClick={() => setActiveTab(tab.id)}
                className={`w-full flex items-center gap-3 px-4 py-3 rounded-lg text-left transition-colors ${
                  activeTab === tab.id
                    ? 'bg-blue-600 text-white'
                    : 'hover:bg-gray-100 text-gray-700'
                }`}
              >
                <tab.icon className="w-4 h-4" />
                {tab.label}
              </button>
            ))}
          </nav>
        </div>
      </div>

      {/* Main Content Area */}
      <div className="flex-1 flex flex-col">
        {activeTab === 'generator' && (
          <div className="flex-1 flex">
            {/* Generator Panel */}
            <div className="w-96 p-6 border-r bg-white">
              <h3 className="text-lg font-semibold mb-4">What would you like to create?</h3>
              
              <div className="space-y-4">
                {/* Blog Post Generator */}
                <div className="border rounded-lg p-4 hover:border-blue-300 cursor-pointer transition-colors"
                     onClick={() => generateInterface('blogPost')}>
                  <h4 className="font-medium mb-2">📝 Blog Post</h4>
                  <p className="text-sm text-gray-600 mb-3">
                    Create engaging blog posts with AI assistance
                  </p>
                  <div className="text-xs text-gray-500">
                    Topics • Outlines • Full content • SEO optimization
                  </div>
                </div>

                {/* Social Media Generator */}
                <div className="border rounded-lg p-4 hover:border-blue-300 cursor-pointer transition-colors"
                     onClick={() => generateInterface('socialMedia')}>
                  <h4 className="font-medium mb-2">📱 Social Media</h4>
                  <p className="text-sm text-gray-600 mb-3">
                    Generate posts for all social platforms
                  </p>
                  <div className="text-xs text-gray-500">
                    Twitter • LinkedIn • Facebook • Instagram
                  </div>
                </div>

                {/* Email Newsletter */}
                <div className="border rounded-lg p-4 hover:border-blue-300 cursor-pointer transition-colors"
                     onClick={() => generateInterface('newsletter')}>
                  <h4 className="font-medium mb-2">📧 Newsletter</h4>
                  <p className="text-sm text-gray-600 mb-3">
                    Create compelling email newsletters
                  </p>
                  <div className="text-xs text-gray-500">
                    Campaigns • Templates • Personalization
                  </div>
                </div>

                {/* Content Ideas */}
                <div className="border rounded-lg p-4 hover:border-blue-300 cursor-pointer transition-colors"
                     onClick={() => generateInterface('ideas')}>
                  <h4 className="font-medium mb-2">💡 Content Ideas</h4>
                  <p className="text-sm text-gray-600 mb-3">
                    Get AI-powered content suggestions
                  </p>
                  <div className="text-xs text-gray-500">
                    Trending topics • Keyword research • Content calendar
                  </div>
                </div>
              </div>
            </div>

            {/* Editor and Preview */}
            <div className="flex-1 flex">
              <ContentEditor 
                content={generatedContent}
                onContentChange={setGeneratedContent}
                isGenerating={isGenerating}
              />
              
              {generatedContent && (
                <ContentPreview content={generatedContent} />
              )}
            </div>
          </div>
        )}

        {activeTab === 'templates' && (
          <TemplateGallery 
            onSelectTemplate={setSelectedTemplate}
            selectedTemplate={selectedTemplate}
          />
        )}

        {activeTab === 'history' && (
          <div className="flex-1 p-6">
            <h3 className="text-lg font-semibold mb-4">Generation History</h3>
            {/* History component would go here */}
            <p className="text-gray-600">Your previous content generations will appear here.</p>
          </div>
        )}

        {activeTab === 'export' && generatedContent && (
          <div className="flex-1 p-6">
            <h3 className="text-lg font-semibold mb-4">Export Options</h3>
            
            <div className="grid grid-cols-2 gap-4 max-w-2xl">
              {['PDF', 'Word', 'HTML', 'Markdown'].map(format => (
                <button
                  key={format}
                  className="p-4 border rounded-lg hover:border-blue-300 transition-colors"
                  onClick={() => handleExport(format.toLowerCase())}
                >
                  <div className="text-lg font-medium mb-1">{format}</div>
                  <div className="text-sm text-gray-600">
                    Export as {format.toLowerCase()} file
                  </div>
                </button>
              ))}
            </div>
          </div>
        )}
      </div>

      {/* Conversation Interface */}
      {conversation.isActive && (
        <div className="w-96 border-l bg-white">
          <ConversationInterface 
            agent={contentAgent}
            onExecuteAction={handleContentRefinement}
            content={generatedContent}
          />
        </div>
      )}
    </div>
  )
}

export default ContentGenerator

Project 3: Conversational Data Explorer

Let’s build a data exploration tool where users can query and visualize data through natural conversation.

Step 1: Define Data Explorer Agent

Create src/agents/DataAgent.js:

import { Agent } from '@microsoft/magentic-ui'

export const dataAgent = new Agent({
  name: 'DataExplorer',
  description: 'AI-powered data analysis and visualization assistant',
  capabilities: [
    {
      name: 'queryData',
      description: 'Query data using natural language',
      parameters: {
        query: { type: 'string', required: true },
        dataSource: { type: 'string', required: false },
        limit: { type: 'number', default: 100 }
      }
    },
    {
      name: 'generateChart',
      description: 'Create data visualizations',
      parameters: {
        data: { type: 'array', required: true },
        chartType: { type: 'enum', options: ['bar', 'line', 'pie', 'scatter', 'heatmap'], default: 'auto' },
        title: { type: 'string', required: false },
        groupBy: { type: 'string', required: false }
      }
    },
    {
      name: 'analyzeData',
      description: 'Perform statistical analysis on data',
      parameters: {
        data: { type: 'array', required: true },
        analysisType: { type: 'enum', options: ['summary', 'correlation', 'trends', 'outliers'], default: 'summary' },
        columns: { type: 'array', required: false }
      }
    },
    {
      name: 'exportData',
      description: 'Export data in various formats',
      parameters: {
        data: { type: 'array', required: true },
        format: { type: 'enum', options: ['csv', 'json', 'excel', 'pdf'], default: 'csv' },
        filename: { type: 'string', required: false }
      }
    }
  ],
  
  // Data sources configuration
  dataSources: {
    sample: {
      name: 'Sample Sales Data',
      type: 'csv',
      url: '/data/sales.csv',
      columns: ['date', 'product', 'category', 'sales', 'region', 'salesperson']
    },
    users: {
      name: 'User Analytics',
      type: 'api',
      url: '/api/users',
      columns: ['id', 'name', 'email', 'signup_date', 'last_active', 'plan']
    }
  },
  
  // Visualization preferences
  visualizationPreferences: {
    defaultChartType: 'auto',
    colorScheme: 'modern',
    interactiveCharts: true,
    showDataLabels: true,
    enableAnimations: true
  }
})

Step 2: Create Data Explorer Interface

Create src/components/DataExplorer.js:

import React, { useState, useEffect } from 'react'
import { useMagenticUI } from '@microsoft/magentic-ui-react'
import { dataAgent } from '../agents/DataAgent'
import DataVisualization from './DataVisualization'
import DataTable from './DataTable'
import QueryBuilder from './QueryBuilder'
import { Database, BarChart3, TrendingUp, Download } from 'lucide-react'

function DataExplorer() {
  const [data, setData] = useState([])
  const [currentQuery, setCurrentQuery] = useState('')
  const [visualizations, setVisualizations] = useState([])
  const [selectedDataSource, setSelectedDataSource] = useState('sample')
  const [analysisResults, setAnalysisResults] = useState(null)
  
  const { 
    startConversation, 
    executeCapability,
    conversation 
  } = useMagenticUI()

  // Load initial data
  useEffect(() => {
    loadDataSource(selectedDataSource)
  }, [selectedDataSource])

  const loadDataSource = async (source) => {
    try {
      const result = await executeCapability(dataAgent, 'queryData', {
        query: 'SELECT * FROM data LIMIT 1000',
        dataSource: source
      })
      setData(result)
    } catch (error) {
      console.error('Failed to load data:', error)
    }
  }

  const handleNaturalQuery = async (query) => {
    setCurrentQuery(query)
    
    try {
      const result = await executeCapability(dataAgent, 'queryData', {
        query,
        dataSource: selectedDataSource
      })
      
      setData(result)
      
      // Generate visualization suggestions
      const chartSuggestions = await executeCapability(dataAgent, 'generateChart', {
        data: result,
        chartType: 'auto'
      })
      
      setVisualizations(chartSuggestions)
      
      // Start conversation to discuss results
      await startConversation(dataAgent, {
        context: {
          query,
          resultCount: result.length,
          visualizations: chartSuggestions
        },
        greeting: `I found ${result.length} results for your query. Here are some visualizations I created.`,
        suggestions: [
          "Show me more details",
          "Create a different chart type",
          "Analyze this data",
          "Export the results"
        ]
      })
    } catch (error) {
      console.error('Query failed:', error)
    }
  }

  const handleAnalysis = async (analysisType) => {
    if (data.length === 0) return
    
    try {
      const results = await executeCapability(dataAgent, 'analyzeData', {
        data,
        analysisType
      })
      
      setAnalysisResults(results)
    } catch (error) {
      console.error('Analysis failed:', error)
    }
  }

  const handleExport = async (format) => {
    if (data.length === 0) return
    
    try {
      await executeCapability(dataAgent, 'exportData', {
        data,
        format
      })
    } catch (error) {
      console.error('Export failed:', error)
    }
  }

  return (
    <div className="flex h-screen bg-gray-50">
      {/* Main Explorer Interface */}
      <div className="flex-1 flex flex-col">
        {/* Header */}
        <div className="bg-white border-b p-4">
          <div className="flex items-center justify-between">
            <div>
              <h2 className="text-2xl font-bold text-gray-900">Data Explorer</h2>
              <p className="text-sm text-gray-600">
                Query and visualize your data with AI assistance
              </p>
            </div>
            
            <div className="flex items-center gap-4">
              {/* Data Source Selector */}
              <select
                value={selectedDataSource}
                onChange={(e) => setSelectedDataSource(e.target.value)}
                className="px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500"
              >
                <option value="sample">Sample Sales Data</option>
                <option value="users">User Analytics</option>
                <option value="custom">Custom Data Source</option>
              </select>
              
              {/* Export Button */}
              <button
                onClick={() => handleExport('csv')}
                className="flex items-center gap-2 px-4 py-2 bg-green-600 text-white rounded-lg hover:bg-green-700"
              >
                <Download className="w-4 h-4" />
                Export
              </button>
            </div>
          </div>
        </div>

        {/* Natural Language Query */}
        <div className="p-6 bg-white border-b">
          <div className="max-w-4xl mx-auto">
            <label className="block text-sm font-medium text-gray-700 mb-2">
              Ask about your data in natural language:
            </label>
            <div className="flex gap-4">
              <input
                type="text"
                value={currentQuery}
                onChange={(e) => setCurrentQuery(e.target.value)}
                placeholder="e.g., 'Show me sales by region for last quarter' or 'What are the top 10 products?'"
                className="flex-1 px-4 py-3 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500"
                onKeyPress={(e) => {
                  if (e.key === 'Enter') {
                    handleNaturalQuery(currentQuery)
                  }
                }}
              />
              <button
                onClick={() => handleNaturalQuery(currentQuery)}
                className="px-6 py-3 bg-blue-600 text-white rounded-lg hover:bg-blue-700 font-medium"
              >
                <Database className="w-4 h-4 inline mr-2" />
                Query
              </button>
            </div>
            
            {/* Query Suggestions */}
            <div className="mt-4 flex flex-wrap gap-2">
              {[
                "Show sales trends over time",
                "Top performing products",
                "Regional performance comparison",
                "Sales by category",
                "Monthly growth analysis"
              ].map((suggestion, index) => (
                <button
                  key={index}
                  onClick={() => setCurrentQuery(suggestion)}
                  className="px-3 py-1 text-sm bg-gray-100 text-gray-700 rounded-full hover:bg-gray-200"
                >
                  {suggestion}
                </button>
              ))}
            </div>
          </div>
        </div>

        {/* Results Area */}
        <div className="flex-1 p-6">
          {data.length > 0 && (
            <div className="space-y-6">
              {/* Summary Stats */}
              <div className="grid grid-cols-4 gap-4">
                <div className="bg-white p-4 rounded-lg border">
                  <div className="flex items-center gap-2">
                    <Database className="w-5 h-5 text-blue-600" />
                    <span className="text-sm text-gray-600">Total Records</span>
                  </div>
                  <div className="text-2xl font-bold text-gray-900 mt-1">
                    {data.length.toLocaleString()}
                  </div>
                </div>
                
                <div className="bg-white p-4 rounded-lg border">
                  <div className="flex items-center gap-2">
                    <TrendingUp className="w-5 h-5 text-green-600" />
                    <span className="text-sm text-gray-600">Columns</span>
                  </div>
                  <div className="text-2xl font-bold text-gray-900 mt-1">
                    {data[0] ? Object.keys(data[0]).length : 0}
                  </div>
                </div>
                
                {/* More stats cards */}
              </div>

              {/* Visualizations */}
              {visualizations.length > 0 && (
                <div>
                  <h3 className="text-lg font-semibold mb-4">AI-Generated Visualizations</h3>
                  <div className="grid grid-cols-2 gap-6">
                    {visualizations.map((viz, index) => (
                      <DataVisualization
                        key={index}
                        data={data}
                        config={viz}
                      />
                    ))}
                  </div>
                </div>
              )}

              {/* Data Table */}
              <div>
                <h3 className="text-lg font-semibold mb-4">Data Results</h3>
                <DataTable data={data} />
              </div>

              {/* Analysis Results */}
              {analysisResults && (
                <div>
                  <h3 className="text-lg font-semibold mb-4">Analysis Results</h3>
                  <div className="bg-white p-6 rounded-lg border">
                    <pre className="text-sm text-gray-700 whitespace-pre-wrap">
                      {JSON.stringify(analysisResults, null, 2)}
                    </pre>
                  </div>
                </div>
              )}
            </div>
          )}
          
          {currentQuery && data.length === 0 && (
            <div className="text-center py-12">
              <Database className="w-12 h-12 text-gray-400 mx-auto mb-4" />
              <h3 className="text-lg font-medium text-gray-900 mb-2">
                No results found
              </h3>
              <p className="text-gray-600">
                Try modifying your query or check the data source
              </p>
            </div>
          )}
        </div>
      </div>

      {/* Conversation Interface */}
      {conversation.isActive && (
        <div className="w-96 border-l bg-white">
          <ConversationInterface 
            agent={dataAgent}
            onExecuteAction={handleNaturalQuery}
            data={data}
          />
        </div>
      )}
    </div>
  )
}

export default DataExplorer

Advanced Patterns and Best Practices

1. Multi-Agent Collaboration

import { AgentOrchestrator } from '@microsoft/magentic-ui'

// Create orchestrator for multiple agents
const orchestrator = new AgentOrchestrator({
  agents: [taskAgent, contentAgent, dataAgent],
  collaborationRules: {
    // When task agent creates content, involve content agent
    'task.create_content': ['contentAgent'],
    // When content agent needs data, involve data agent
    'content.needs_data': ['dataAgent'],
    // Allow agents to share context
    enableContextSharing: true
  }
})

// Usage
const result = await orchestrator.executeWorkflow({
  trigger: 'create_project_report',
  context: {
    project: 'Q4 Sales Analysis',
    requirements: ['data_analysis', 'content_creation', 'task_assignment']
  }
})

2. Adaptive UI Generation

// UI that adapts to user expertise level
const adaptiveUI = {
  generateInterface: (userLevel, context) => {
    const config = {
      beginner: {
        showGuides: true,
        simplifyInterface: true,
        provideExamples: true,
        stepByStepMode: true
      },
      intermediate: {
        showGuides: false,
        simplifyInterface: false,
        provideExamples: true,
        stepByStepMode: false
      },
      expert: {
        showGuides: false,
        simplifyInterface: false,
        provideExamples: false,
        stepByStepMode: false,
        showAdvancedOptions: true
      }
    }
    
    return generateUIComponents({
      ...context,
      ...config[userLevel]
    })
  }
}

3. Context-Aware Suggestions

// Smart suggestions based on user behavior
class ContextEngine {
  constructor() {
    this.userContext = {
      recentActions: [],
      preferences: {},
      timeOfDay: new Date().getHours(),
      workPatterns: {}
    }
  }
  
  updateContext(action, result) {
    this.userContext.recentActions.push({
      action,
      result,
      timestamp: new Date()
    })
    
    // Keep only last 50 actions
    if (this.userContext.recentActions.length > 50) {
      this.userContext.recentActions = this.userContext.recentActions.slice(-50)
    }
    
    this.updatePatterns()
  }
  
  getSuggestions() {
    const suggestions = []
    
    // Time-based suggestions
    if (this.userContext.timeOfDay >= 9 && this.userContext.timeOfDay <= 17) {
      suggestions.push("Start your daily planning")
    }
    
    // Pattern-based suggestions
    if (this.detectRepetitiveTask()) {
      suggestions.push("Automate this repetitive task")
    }
    
    // Context-based suggestions
    const lastAction = this.userContext.recentActions.slice(-1)[0]
    if (lastAction?.action === 'create_task') {
      suggestions.push("Set up reminders for this task")
    }
    
    return suggestions
  }
  
  detectRepetitiveTask() {
    // Analyze recent actions for patterns
    const recentActions = this.userContext.recentActions.slice(-10)
    const actionCounts = recentActions.reduce((acc, action) => {
      acc[action.action] = (acc[action.action] || 0) + 1
      return acc
    }, {})
    
    return Object.values(actionCounts).some(count => count >= 3)
  }
}

4. Error Recovery and Fallbacks

// Robust error handling for AI interactions
class ResilientAgent {
  constructor(agent) {
    this.agent = agent
    this.retryStrategies = [
      'rephrase_query',
      'simplify_request',
      'provide_examples',
      'fallback_to_manual'
    ]
  }
  
  async executeWithFallback(capability, params, maxRetries = 3) {
    let lastError
    
    for (let attempt = 0; attempt < maxRetries; attempt++) {
      try {
        const modifiedParams = this.applyRetryStrategy(attempt, params)
        const result = await this.agent.execute(capability, modifiedParams)
        return result
      } catch (error) {
        lastError = error
        console.warn(`Attempt ${attempt + 1} failed:`, error)
        
        if (attempt === maxRetries - 1) {
          // Last attempt - provide manual fallback
          return this.provideManualInterface(capability, params)
        }
      }
    }
    
    throw lastError
  }
  
  applyRetryStrategy(attempt, params) {
    const strategies = this.retryStrategies
    
    switch (strategies[attempt]) {
      case 'rephrase_query':
        return {
          ...params,
          query: this.rephraseQuery(params.query)
        }
        
      case 'simplify_request':
        return {
          ...params,
          complexity: 'simple'
        }
        
      case 'provide_examples':
        return {
          ...params,
          examples: true
        }
        
      default:
        return params
    }
  }
  
  provideManualInterface(capability, params) {
    // Generate traditional UI as fallback
    return {
      type: 'manual_fallback',
      capability,
      params,
      ui: this.generateManualUI(capability, params)
    }
  }
}

Integration with AI Services

OpenAI Integration

// services/openai.js
import OpenAI from 'openai'

class OpenAIService {
  constructor(config) {
    this.client = new OpenAI({
      apiKey: config.apiKey,
      organization: config.organization
    })
    this.model = config.model || 'gpt-4-turbo-preview'
  }
  
  async generateResponse(prompt, context = {}) {
    try {
      const response = await this.client.chat.completions.create({
        model: this.model,
        messages: [
          {
            role: 'system',
            content: this.buildSystemPrompt(context)
          },
          {
            role: 'user',
            content: prompt
          }
        ],
        temperature: 0.7,
        max_tokens: 2000,
        functions: context.availableFunctions,
        function_call: 'auto'
      })
      
      return this.parseResponse(response)
    } catch (error) {
      console.error('OpenAI API error:', error)
      throw error
    }
  }
  
  buildSystemPrompt(context) {
    return `You are a helpful AI assistant integrated with Magentic UI. 
    Current context: ${JSON.stringify(context)}
    
    Guidelines:
    1. Always consider user's current context and history
    2. Generate appropriate UI components when needed
    3. Be proactive in suggesting next actions
    4. Ask clarifying questions when intent is unclear
    5. Provide structured responses that can be parsed`
  }
  
  parseResponse(response) {
    const choice = response.choices[0]
    
    if (choice.message.function_call) {
      return {
        type: 'function_call',
        function: choice.message.function_call.name,
        parameters: JSON.parse(choice.message.function_call.arguments)
      }
    }
    
    return {
      type: 'text',
      content: choice.message.content,
      finishReason: choice.finish_reason
    }
  }
}

export default OpenAIService

Multi-Model Support

// services/modelRouter.js
class ModelRouter {
  constructor() {
    this.models = {
      openai: new OpenAIService(openaiConfig),
      claude: new ClaudeService(claudeConfig),
      local: new LocalModelService(localConfig)
    }
    
    this.routingRules = {
      'text_generation': 'openai',
      'code_generation': 'claude',
      'data_analysis': 'openai',
      'image_generation': 'local',
      'fast_response': 'local'
    }
  }
  
  async route(task, params) {
    const modelType = this.selectModel(task, params)
    const model = this.models[modelType]
    
    try {
      const result = await model.execute(task, params)
      return { result, modelUsed: modelType }
    } catch (error) {
      // Fallback to next best model
      const fallbackModel = this.getFallbackModel(modelType)
      return await this.models[fallbackModel].execute(task, params)
    }
  }
  
  selectModel(task, params) {
    // Business logic for model selection
    if (params.priority === 'speed') {
      return 'local'
    }
    
    if (params.priority === 'quality') {
      return 'openai'
    }
    
    if (task.includes('code')) {
      return 'claude'
    }
    
    return this.routingRules[task] || 'openai'
  }
  
  getFallbackModel(primaryModel) {
    const fallbackOrder = ['openai', 'claude', 'local']
    const currentIndex = fallbackOrder.indexOf(primaryModel)
    return fallbackOrder[currentIndex + 1] || fallbackOrder[0]
  }
}

Production Deployment

Docker Configuration

# Dockerfile
FROM node:18-alpine

WORKDIR /app

# Copy package files
COPY package*.json ./
RUN npm ci --only=production

# Copy source code
COPY . .

# Build the application
RUN npm run build

# Install serve for production
RUN npm install -g serve

# Expose port
EXPOSE 3000

# Health check
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD curl -f http://localhost:3000/health || exit 1

# Start the application
CMD ["serve", "-s", "build", "-l", "3000"]

# docker-compose.prod.yml
version: '3.8'

services:
  magentic-ui:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
      - REACT_APP_OPENAI_API_KEY=${OPENAI_API_KEY}
      - REACT_APP_MAGENTIC_BACKEND_URL=${MAGENTIC_BACKEND_URL}
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf
      - ./ssl:/etc/nginx/ssl
    depends_on:
      - magentic-ui
    restart: unless-stopped

Performance Optimization

// performance/optimization.js
class PerformanceOptimizer {
  constructor() {
    this.metrics = {
      responseTime: [],
      memoryUsage: [],
      errorRate: 0
    }
    
    this.optimizationStrategies = {
      caching: new ResponseCache(),
      batching: new RequestBatcher(),
      compression: new ResponseCompressor()
    }
  }
  
  async optimizeRequest(request) {
    const startTime = Date.now()
    
    try {
      // Check cache first
      const cachedResponse = await this.optimizationStrategies.caching.get(request)
      if (cachedResponse) {
        return cachedResponse
      }
      
      // Batch similar requests
      const batchedResponse = await this.optimizationStrategies.batching.process(request)
      if (batchedResponse) {
        return batchedResponse
      }
      
      // Process request
      const response = await this.processRequest(request)
      
      // Compress response if needed
      const compressedResponse = await this.optimizationStrategies.compression.compress(response)
      
      // Cache the response
      await this.optimizationStrategies.caching.set(request, compressedResponse)
      
      // Record metrics
      this.recordMetrics(startTime, null)
      
      return compressedResponse
    } catch (error) {
      this.recordMetrics(startTime, error)
      throw error
    }
  }
  
  recordMetrics(startTime, error) {
    const responseTime = Date.now() - startTime
    
    this.metrics.responseTime.push(responseTime)
    if (this.metrics.responseTime.length > 1000) {
      this.metrics.responseTime = this.metrics.responseTime.slice(-1000)
    }
    
    if (error) {
      this.metrics.errorRate++
    }
    
    // Trigger optimizations if needed
    this.checkAndApplyOptimizations()
  }
  
  checkAndApplyOptimizations() {
    const avgResponseTime = this.metrics.responseTime.reduce((a, b) => a + b, 0) / this.metrics.responseTime.length
    
    if (avgResponseTime > 2000) {
      // Enable more aggressive caching
      this.optimizationStrategies.caching.increaseTTL()
    }
    
    if (this.metrics.errorRate > 0.05) {
      // Enable circuit breaker
      this.optimizationStrategies.circuitBreaker.enable()
    }
  }
}

Monitoring and Analytics

// monitoring/analytics.js
class MagenticUIMonitor {
  constructor() {
    this.events = []
    this.userSessions = new Map()
    this.performanceMetrics = new Map()
  }
  
  trackUserInteraction(event, context) {
    const interaction = {
      timestamp: new Date(),
      type: event.type,
      element: event.element,
      value: event.value,
      sessionId: this.getSessionId(),
      context: {
        userAgent: navigator.userAgent,
        screenResolution: `${screen.width}x${screen.height}`,
        ...context
      }
    }
    
    this.events.push(interaction)
    this.sendToAnalytics(interaction)
  }
  
  trackAIGeneration(request, response, duration) {
    const aiEvent = {
      type: 'ai_generation',
      timestamp: new Date(),
      request: {
        capability: request.capability,
        parameters: request.parameters,
        model: request.model
      },
      response: {
        success: response.success,
        responseTime: duration,
        tokensUsed: response.tokensUsed
      }
    }
    
    this.events.push(aiEvent)
    this.updatePerformanceMetrics(request.capability, duration, response.success)
  }
  
  updatePerformanceMetrics(capability, duration, success) {
    if (!this.performanceMetrics.has(capability)) {
      this.performanceMetrics.set(capability, {
        totalRequests: 0,
        totalDuration: 0,
        successCount: 0,
        errorCount: 0
      })
    }
    
    const metrics = this.performanceMetrics.get(capability)
    metrics.totalRequests++
    metrics.totalDuration += duration
    
    if (success) {
      metrics.successCount++
    } else {
      metrics.errorCount++
    }
  }
  
  generateReport() {
    return {
      totalInteractions: this.events.length,
      uniqueSessions: this.userSessions.size,
      performanceByCapability: Object.fromEntries(this.performanceMetrics),
      topCapabilities: this.getTopCapabilities(),
      errorRate: this.calculateErrorRate(),
      averageResponseTime: this.calculateAverageResponseTime()
    }
  }
}

Resources and Further Learning

Official Documentation

Magentic UI GitHub: github.com/microsoft/magentic-ui - Source code and examples
Microsoft Research Blog: microsoft.com/en-us/research/blog/magentic-ui - Research paper and concepts
API Documentation: magentic-ui.docs - Complete API reference
Examples Repository: github.com/microsoft/magentic-ui-examples - Sample implementations

Learning Resources

Human-AI Interaction Design: interaction-design.org - Principles of human-AI collaboration
Conversational UI Patterns: conversation-design.com - Best practices for conversational interfaces
Adaptive Interfaces: adaptive-ui.org - Dynamic UI generation techniques
Agent-Based Architecture: agent-patterns.org - Multi-agent system design

Tools and Libraries

React Integration: @microsoft/magentic-ui-react
Vue Integration: @microsoft/magentic-ui-vue
Angular Integration: @microsoft/magentic-ui-angular
Styling Frameworks: Tailwind CSS, Styled Components, Emotion

AI Service Integrations

OpenAI: platform.openai.com - GPT models and APIs
Anthropic Claude: anthropic.com - Claude AI models
Google AI: ai.google.dev - Gemini models and tools
Local Models: ollama.ai - Run models locally

Community and Support

Discord Community: discord.gg/magentic-ui
GitHub Discussions: github.com/microsoft/magentic-ui/discussions
Stack Overflow: Tag magentic-ui
Reddit: r/magenticui

Advanced Topics

Multi-Modal Interfaces: Combining text, voice, and visual interactions
Context-Aware Computing: Adapting to user environment and preferences
Federated Learning: Privacy-preserving AI interactions
Edge AI: Running AI models locally for better performance

Example Projects

Task Management: github.com/magentic-ui/task-manager
Content Generator: github.com/magentic-ui/content-generator
Data Explorer: github.com/magentic-ui/data-explorer
Customer Support: github.com/magentic-ui/support-agent

Quick Reference Cheat Sheet

Agent Definition

const agent = new Agent({
  name: 'AgentName',
  capabilities: [
    {
      name: 'capabilityName',
      description: 'What it does',
      parameters: {
        param1: { type: 'string', required: true }
      }
    }
  ]
})

UI Generation

const ui = await generateInterface({
  agent: myAgent,
  context: userContext,
  intent: 'user_intent'
})

Conversation Flow

await startConversation(agent, {
  context: {},
  greeting: 'Hello! How can I help?',
  suggestions: ['Option 1', 'Option 2']
})

Conclusion

Magentic UI represents a fundamental shift in how we think about user interfaces. Instead of static, predefined interactions, we’re moving toward dynamic, conversational, and adaptive experiences that truly put humans at the center.

Key Takeaways:

Human-AI Partnership: Focus on collaboration, not replacement
Context Awareness: Adapt to user needs and environment
Dynamic Interfaces: Generate UI components on-demand
Natural Interaction: Use conversation as the primary interaction model
Continuous Learning: Improve from user interactions and feedback

Your Next Steps:

Start with simple agent definitions and capabilities
Build conversational interfaces that feel natural
Implement context-aware UI generation
Add multi-agent collaboration for complex workflows
Focus on accessibility and inclusive design

The future of UI is conversational, adaptive, and intelligent. With Magentic UI, you’re at the forefront of this revolution. Now go build interfaces that truly understand and collaborate with humans! 🚀

Last Updated: December 3, 2025 Magentic UI Version: 0.3.0 This tutorial covers experimental features and may change as the framework evolves

Table of Contents#

Introduction: What is Magentic UI?#

The Problem with Traditional UI#

The Magentic UI Solution#

Why Magentic UI Matters#

Understanding the Core Concepts#

1. Agents and Capabilities#

2. UI Generation and Rendering#

3. Conversation Flow#

4. State Management#

Setting Up Your Development Environment#

Prerequisites#

Installing Magentic UI#

Basic Configuration#

Environment Setup#

Basic App Structure#

Project 1: AI-Powered Task Manager#

Step 1: Define the Task Agent#

Step 2: Create Task Manager Component#

Step 3: Create Conversation Interface#

Step 4: Create Task Board Component#

Project 2: Smart Content Generator#

Step 1: Define Content Generation Agent#

Step 2: Create Content Generator Interface#

Project 3: Conversational Data Explorer#

Step 1: Define Data Explorer Agent#

Step 2: Create Data Explorer Interface#

Advanced Patterns and Best Practices#

1. Multi-Agent Collaboration#

2. Adaptive UI Generation#

3. Context-Aware Suggestions#

4. Error Recovery and Fallbacks#

Integration with AI Services#

OpenAI Integration#

Multi-Model Support#

Production Deployment#

Docker Configuration#

Performance Optimization#

Monitoring and Analytics#

Resources and Further Learning#

Official Documentation#

Learning Resources#

Tools and Libraries#

AI Service Integrations#

Community and Support#

Advanced Topics#

Example Projects#

Quick Reference Cheat Sheet#

Agent Definition#

UI Generation#

Conversation Flow#

Conclusion#

Table of Contents