Airlines FAQ Bot

Airlines FAQ Bot Setup Guide

What This Agent Does

The Airlines FAQ Bot is an intelligent, AI-powered customer service automation that instantly answers passenger questions about flights, policies, bookings, and travel procedures. This workflow combines natural language processing with a knowledge base system to deliver accurate, contextual responses 24/7 without human intervention.

Key benefits include:

• Instant response times - Passengers get answers in seconds instead of waiting for support staff
• Reduced support costs - Automate 70-80% of routine inquiries, freeing your team for complex issues
• Consistent quality - Every response follows the same high standards with AI-powered accuracy
• Scalability - Handle unlimited concurrent questions without additional staffing
• Customer satisfaction tracking - Built-in satisfaction monitoring helps you continuously improve

Ideal use cases:

• Baggage policies and allowances
• Flight booking and modification questions
• Cancellation and refund procedures
• Seat selection and upgrade inquiries
• Frequent flyer program details
• Check-in and boarding requirements

Who Is It For

This workflow is perfect for:

• Airlines and travel companies managing high-volume customer inquiries
• Customer support teams looking to reduce response times and operational costs
• Organizations needing 24/7 availability without expanding headcount
• Travel platforms integrating intelligent FAQ capabilities into their applications
• Multi-channel support systems requiring consistent answers across touchpoints

Required Integrations

OpenAI

Why it's needed: OpenAI powers the intelligent core of your FAQ bot. The workflow uses two distinct OpenAI models: GPT-3.5-turbo for fast question classification and GPT-4 for generating detailed, contextual answers. This dual-model approach balances speed with accuracy.

Setup steps:

1. Create an OpenAI account at platform.openai.com
2. Navigate to API keys in your account settings (Account → API keys)
3. Generate a new secret key by clicking "Create new secret key"
4. Copy the key immediately - you won't be able to view it again
5. In TaskAGI, go to Integrations → Add Integration
6. Select OpenAI from the available options
7. Paste your API key in the authentication field
8. Name your integration (e.g., "Airlines-OpenAI-Production")
9. Test the connection by clicking "Verify Integration"
10. Save and enable the integration for your workflow

How to obtain API keys:

• Visit your OpenAI account dashboard
• Ensure you have billing set up (pay-as-you-go or prepaid credits)
• Create organization-level keys for production use
• Consider creating separate keys for development and production environments

Configuration in TaskAGI:

• Store your API key securely in TaskAGI's encrypted credential vault
• Set rate limits if needed to control costs (recommended: 100 requests/minute for testing)
• Enable usage monitoring to track API calls and expenses
• Create backup keys for failover scenarios

Configuration Steps

Step 1: Trigger Setup - Webhook (Node 6074)

The webhook acts as your entry point, receiving customer questions from your website, app, or support platform.

Configuration:

1. In TaskAGI, open the "Webhook - User Question" node
2. Copy the webhook URL provided by TaskAGI
3. Configure your frontend to POST requests to this URL with the following JSON structure:

{
  "userQuestion": "Can I change my flight date?",
  "userId": "user_12345",
  "sessionId": "session_abc123"
}

4. Test the webhook using Postman or curl:

curl -X POST https://your-webhook-url \
  -H "Content-Type: application/json" \
  -d '{"userQuestion":"What is your baggage policy?","userId":"test_user"}'

Expected output: The workflow triggers and processes the question through subsequent nodes.

Step 2: Data Cleaning - Extract & Clean Question (Node 6076)

This node standardizes the incoming question, removing extra whitespace, special characters, and normalizing text.

Configuration:

1. Open the node and review the function code
2. Ensure it handles:
   • Lowercase conversion for consistency
   • Removal of extra spaces and punctuation
   • Trimming of leading/trailing whitespace
3. Example transformation:
   • Input: " WHY IS MY FLIGHT DELAYED??? "
   • Output: "why is my flight delayed"

Data flow: Cleaned question passes to Node 6078 (Classify Question Category)

Step 3: Classification - Classify Question Category (Node 6078)

This OpenAI node categorizes the question to route it appropriately.

Configuration:

1. Select your OpenAI integration from the dropdown
2. Model selection: gpt-3.5-turbo (fast and cost-effective for classification)
3. Temperature: Set to 0.3 (low creativity, consistent classifications)
4. Max tokens: 100 (classification requires minimal output)
5. Review the prompt - it should classify into categories like:
   • Baggage & Allowances
   • Booking & Modifications
   • Cancellations & Refunds
   • Seat Selection
   • Check-in & Boarding
   • Frequent Flyer
   • Other

Example prompt:

You are a travel question classifier. Classify the following 
airline customer question into one of these categories: 
Baggage, Booking, Cancellation, Seating, Check-in, Loyalty, Other.

Question: [cleaned_question]

Respond with ONLY the category name.

Data flow: Classification result passes to Node 6080 (Parse Category Result)

Step 4: Parsing - Parse Category Result (Node 6080)

This function extracts the category from OpenAI's response and formats it for downstream processing.

Configuration:

1. Extract the category from the OpenAI response
2. Create a standardized output object:

{
  "category": "Baggage",
  "confidence": 0.95,
  "originalQuestion": "[from previous node]"
}

3. Add error handling for unrecognized categories (default to "Other")

Data flow: Parsed category passes to Node 6082 (Fetch Knowledge Base Context)

Step 5: Knowledge Retrieval - Fetch Knowledge Base Context (Node 6082)

This node retrieves relevant information from your knowledge base based on the question category.

Configuration:

1. Connect to your knowledge base (database, API, or document store)
2. Query structure - search by category and keywords:

{
  "category": "[from Node 6080]",
  "keywords": "[extracted from cleaned question]",
  "limit": 5
}

3. Example knowledge base entry:

{
  "category": "Baggage",
  "topic": "Checked Baggage Allowance",
  "content": "Economy passengers receive 1 free checked bag up to 50 lbs..."
}

4. Fallback mechanism: If no results found, return general policy information

Data flow: Knowledge base context passes to Node 6084 (Generate AI Answer)

Step 6: Answer Generation - Generate AI Answer (Node 6084)

This is the primary response generator using GPT-4 for high-quality, contextual answers.

Configuration:

1. Select your OpenAI integration
2. Model selection: gpt-4 (superior reasoning and accuracy)
3. Temperature: 0.7 (balanced creativity and consistency)
4. Max tokens: 500 (allows detailed responses)
5. System prompt example:

You are a helpful and friendly airlines travel assistant. 
Use the provided knowledge base information to answer 
customer questions accurately and courteously. 
If information isn't available, suggest contacting support.

Knowledge Base: [from Node 6082]
Customer Question: [from Node 6076]

Provide a clear, friendly response.

Data flow: Generated answer passes to Node 6086 (Format Final Response)

Step 7: Formatting - Format Final Response (Node 6086)

This node structures the answer into a customer-friendly format.

Configuration:

1. Create a response object:

{
  "answer": "[from Node 6084]",
  "category": "[from Node 6080]",
  "timestamp": "[current time]",
  "followUpOptions": [
    "Need more help?",
    "Speak with an agent"
  ]
}

2. Add formatting:
   • Bullet points for lists
   • Clear paragraphs for readability
   • Friendly closing statement

Data flow: Formatted response passes to Node 6088 (Check User Satisfaction)

Step 8: Satisfaction Check - Check User Satisfaction (Node 6088)

This conditional node determines if the answer satisfied the user.

Configuration:

1. Set the condition: Does the response adequately answer the question?
2. True path (Node 6090): User satisfied → Log positive interaction
3. False path (Node 6092): User unsatisfied → Offer human support
4. Decision logic:
   • If answer length > 100 characters AND contains relevant keywords: TRUE
   • Otherwise: FALSE

Data flow: Splits into two paths based on satisfaction

Step 9A: Satisfied Path - Log Satisfied User (Node 6090)

Configuration:

1. Record successful interaction:

{
  "userId": "[from webhook]",
  "status": "satisfied",
  "category": "[from Node 6080]",
  "timestamp": "[current time]"
}

2. Merge with unsatisfied path at Node 6094

Step 9B: Unsatisfied Path - Offer Human Support (Node 6092)

Configuration:

1. Generate support offer message:

{
  "message": "I couldn't fully answer your question. 
             Would you like to connect with a support agent?",
  "supportLink": "https://your-support-portal.com",
  "estimatedWaitTime": "2-3 minutes"
}

2. Merge with satisfied path at Node 6094

Step 10: Merge Paths - Merge Satisfaction Paths (Node 6094)

This node combines both satisfaction paths into a single data stream.

Configuration:

1. Merge strategy: Combine all fields from both paths
2. Output structure:

{
  "userId": "[from webhook]",
  "answer": "[from Node 6086]",
  "status": "[satisfied/unsatisfied]",
  "supportOffered": "[boolean]",
  "timestamp": "[current time]"
}

Data flow: Merged data passes to Node 6096 (Log Interaction to Database)

Step 11: Database Logging - Log Interaction to Database (Node 6096)

This HTTP request node stores the interaction for analytics and compliance.

Configuration:

1. HTTP Method: POST
2. Endpoint: Your database API endpoint
3. Headers:

{
  "Content-Type": "application/json",
  "Authorization": "Bearer [your-api-key]"
}

4. Request body:

{
  "userId": "[[nodes.6094.merged_data.userId]]",
  "question": "[[nodes.6076.cleaned_question]]",
  "category": "[[nodes.6080.category]]",
  "answer": "[[nodes.6086.formatted_answer]]",
  "satisfied": "[[nodes.6088.result]]",
  "timestamp": "[[current_timestamp]]"
}

5. Error handling: Retry failed requests up to 3 times

Data flow: Confirmation passes to Node 6098 (Send Response to User)

Step 12: Response Delivery - Send Response to User (Node 6098)

This final node returns the answer to the user through the webhook.

Configuration:

1. Response body:

{
  "status": "success",
  "answer": "[[nodes.6086.formatted_answer]]",
  "category": "[[nodes.6080.category]]",
  "supportAvailable": "[[nodes.6092.support_offered]]",
  "timestamp": "[[current_timestamp]]"
}

2. HTTP Status Code: 200 (success)
3. Content-Type: application/json

Testing Your Agent

Test Execution Steps

1. Prepare test data: Create 5-10 sample questions covering different categories:

• "What's your baggage allowance?"
• "Can I change my flight date?"
• "How do I cancel my booking?"
• "What are seat selection fees?"
• "How do I check in online?"

2. Run test execution:

1. In TaskAGI, open your workflow
2. Click "Test Workflow"
3. Paste your first test question in the webhook payload
4. Execute and monitor the workflow run

3. Verify at each step:

4. Expected results:

✅ Success indicators:

• All nodes execute without errors
• Response time under 5 seconds
• Generated answers are relevant and helpful
• Database logging completes successfully
• Webhook returns 200 status code

❌ Common issues and fixes:

• OpenAI API errors: Verify API key and billing status
• Slow responses: Check OpenAI rate limits; reduce max_tokens
• Irrelevant answers: Review knowledge base content and prompts
• Database connection fails: Verify endpoint URL and authentication

5. Performance testing:

• Test with 10 concurrent requests
• Monitor API costs and token usage
• Verify response times remain under 5 seconds
• Check error rates (target: <1%)

Congratulations! Your Airlines FAQ Bot is now ready to deliver instant, intelligent customer support. Monitor performance metrics regularly and refine your knowledge base based on user interactions for continuous improvement.