AI Agent Examples for
Every Use Case

// Customer Support Bot Handler
export default async function handler(request: Request) {
  const { message, userId } = await request.json()
  
  // Analyze customer intent
  const intent = await analyzeIntent(message)
  
  // Route to appropriate response
  switch (intent.type) {
    case 'billing':
      return handleBillingInquiry(message, userId)
    case 'technical':
      return handleTechnicalSupport(message, userId)
    case 'general':
      return handleGeneralInquiry(message, userId)
    default:
      return fallbackToHuman(message, userId)
  }
}

# E-commerce Assistant Handler
import openai
from typing import Dict, List

async def handler(request: Dict) -> Dict:
    query = request.get('query', '')
    user_id = request.get('user_id')
    
    # Analyze user intent
    intent = await analyze_shopping_intent(query)
    
    if intent['type'] == 'search':
        products = await search_products(intent['keywords'])
        return format_product_results(products)
    elif intent['type'] == 'recommend':
        recommendations = await get_recommendations(user_id)
        return format_recommendations(recommendations)
    elif intent['type'] == 'track':
        order_status = await get_order_status(intent['order_id'])
        return format_order_status(order_status)

// Content Generation Bot Handler
export default async function handler(request: Request) {
  const { prompt, contentType, brandGuidelines } = await request.json()
  
  // Generate content based on type
  const content = await generateContent({
    prompt,
    type: contentType,
    guidelines: brandGuidelines,
    tone: 'professional',
    length: 'medium'
  })
  
  // Optimize for SEO if blog post
  if (contentType === 'blog') {
    content.seoOptimized = await optimizeForSEO(content.text)
  }
  
  return {
    content: content.text,
    metadata: {
      wordCount: content.wordCount,
      readingTime: content.readingTime,
      seoScore: content.seoOptimized?.score
    }
  }
}

# Data Analysis Assistant Handler
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from typing import Dict, Any

async def handler(request: Dict) -> Dict:
    query = request.get('query', '')
    data_url = request.get('data_url')
    
    # Load and process data
    df = pd.read_csv(data_url)
    
    # Analyze query intent
    analysis_type = await determine_analysis_type(query)
    
    if analysis_type == 'summary':
        return generate_data_summary(df)
    elif analysis_type == 'correlation':
        return find_correlations(df, query)
    elif analysis_type == 'trend':
        return analyze_trends(df, query)
    elif analysis_type == 'visualization':
        return create_visualization(df, query)

// Code Review Assistant Handler
export default async function handler(request: Request) {
  const { pullRequest, repository, rules } = await request.json()
  
  // Analyze code changes
  const analysis = await analyzeCodeChanges(pullRequest.diff)
  
  // Check for issues
  const issues = await checkForIssues(analysis, {
    security: true,
    performance: true,
    style: true,
    bestPractices: true
  })
  
  // Generate suggestions
  const suggestions = await generateSuggestions(issues, rules)
  
  return {
    summary: {
      totalIssues: issues.length,
      criticalIssues: issues.filter(i => i.severity === 'critical').length,
      suggestions: suggestions.length
    },
    issues,
    suggestions,
    approval: issues.filter(i => i.severity === 'critical').length === 0
  }
}

# Meeting Assistant Handler
import whisper
from datetime import datetime
from typing import List, Dict

async def handler(request: Dict) -> Dict:
    audio_file = request.get('audio_file')
    meeting_id = request.get('meeting_id')
    
    # Transcribe audio
    model = whisper.load_model("base")
    result = model.transcribe(audio_file)
    transcript = result["text"]
    
    # Extract key information
    action_items = await extract_action_items(transcript)
    decisions = await extract_decisions(transcript)
    summary = await generate_summary(transcript)
    
    # Store results
    await store_meeting_data(meeting_id, {
        'transcript': transcript,
        'action_items': action_items,
        'decisions': decisions,
        'summary': summary,
        'timestamp': datetime.now()
    })
    
    return {
        'transcript': transcript,
        'action_items': action_items,
        'decisions': decisions,
        'summary': summary
    }