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Customer Support Agent

Build an AI-powered customer support agent that provides instant, accurate responses to customer questions while escalating complex issues to human agents.

What You'll Build​

An intelligent support agent that:

  • ✅ Answers questions using your knowledge base
  • ✅ Handles multiple customers simultaneously
  • ✅ Escalates complex issues automatically
  • ✅ Logs all interactions for quality review
  • ✅ Works 24/7 without breaks

Time to build: 30 minutes
Difficulty: Beginner
Best for: SaaS companies, e-commerce, service businesses

Use Case Scenarios​

Scenario 1: Product Questions​

Customer: "How do I reset my password?"
Agent: Searches knowledge base → Provides step-by-step instructions → Asks if more help is needed

Scenario 2: Billing Inquiry​

Customer: "Why was I charged twice?"
Agent: Recognizes billing issue → Escalates to human agent → Notifies customer of escalation

Scenario 3: Feature Request​

Customer: "Can you add dark mode?"
Agent: Categorizes as feedback → Logs to feature request system → Thanks customer

Implementation​

Step 1: Set Up Your Workflow​

  1. Create a new workflow named "Customer Support Agent"
  2. Add a Starter Block (already included by default)
  3. Configure trigger:
    • Trigger type: Webhook or Chat
    • This allows customers to interact via chat interface or API

Step 2: Build the Knowledge Base​

  1. Add a Knowledge Base tool
  2. Upload your support documentation:
    • FAQs
    • Product guides
    • Troubleshooting steps
    • Policy documents

Step 3: Create the Agent​

  1. Add an Agent Block

  2. Configure:

    Model: Claude 3.7 Sonnet (good at following instructions)

    System Prompt:
    "You are a friendly customer support agent for [Company Name]. 

    Your responsibilities:
    - Answer customer questions using the knowledge base
    - Be concise and helpful
    - If you don't know the answer, say so honestly
    - For billing, refund, or account issues, escalate to a human agent
    - Always maintain a professional, friendly tone

    Important rules:
    - Never make up information
    - Never promise refunds or account changes
    - Always search the knowledge base before responding"

    User Prompt: <start.input>

  3. Add Tools to the agent:

    • Knowledge Base Search - to find relevant information
    • Thinking - for complex reasoning

Step 4: Add Escalation Logic​

  1. Add a Condition Block after the agent
  2. Configure conditions: 
    // Check if escalation is needed
    <agent.content>.toLowerCase().includes('escalate') || 
    <agent.content>.toLowerCase().includes('billing') ||
    <agent.content>.toLowerCase().includes('refund')

Step 5: Set Up Human Handoff​

  1. For TRUE path (escalation needed):

    • Add Slack tool
    • Send message to support channel: 
      Customer needs help:
      Question: <start.input>
      Agent Response: <agent.content>
      Priority: High

  2. Add Response Block:

    "I've notified our support team about your issue. 
    Someone will reach out to you within 30 minutes."

  3. For FALSE path (handled by agent):

    • Add Response Block with <agent.content>

Step 6: Log All Interactions​

  1. Add PostgreSQL or Supabase tool
  2. Insert interaction log: 
    {
      timestamp: <execution.timestamp>,
      customer_query: <start.input>,
      agent_response: <agent.content>,
      escalated: <condition.result>,
      resolution_time: <execution.duration>
    }

Testing Your Agent​

Test Case 1: Simple Question​

Input: "What are your business hours?"
Expected: Agent finds answer in knowledge base and responds

Test Case 2: Billing Issue​

Input: "I was charged twice for my subscription"
Expected: Agent escalates to human, sends Slack notification

Test Case 3: Unknown Question​

Input: "Do you offer enterprise plans?"
Expected: Agent admits uncertainty if not in knowledge base

Best Practices​

1. Knowledge Base Quality​

  • Keep documentation up-to-date
  • Use clear, simple language
  • Include examples and screenshots
  • Organize by category (billing, features, troubleshooting)

2. Escalation Rules​

Clear escalation criteria:

  • Always escalate: Billing disputes, refunds, account closures
  • Consider escalating: Technical issues after 2 failed attempts
  • Never escalate: General questions, feature inquiries

3. Response Quality​

  • Keep responses under 200 words
  • Use bullet points for steps
  • Include relevant links
  • End with "Is there anything else I can help you with?"

4. Monitoring​

Track these metrics:

  • Response time (target: < 3 seconds)
  • Resolution rate (target: > 70% without escalation)
  • Customer satisfaction (add feedback buttons)
  • Escalation rate (target: < 30%)

Enhancements​

Add Sentiment Analysis​

Use an Evaluator Block to detect frustrated customers and prioritize their tickets.

Multi-Language Support​

Add Translate tool to detect language and respond in customer's language.

Proactive Follow-Up​

Add Schedule trigger to check in with customers 24 hours after their issue.

Integration with CRM​

Connect to Salesforce or HubSpot to log interactions and update customer profiles.

Cost Optimization​

Estimated costs per 1,000 interactions:

  • Claude 3.7 Sonnet: ~$3-5
  • Knowledge base searches: ~$0.50
  • Total: $3.50-$5.50 per 1,000 customers

Tips to reduce costs:

  • Use GPT-4o-mini for simple questions (check if question is in FAQ first)
  • Cache common responses (use Memory tool)
  • Limit knowledge base search results to top 3

Troubleshooting​

Issue: Agent gives incorrect information
Solution: Review knowledge base quality, add more examples, use Claude 3.7 Sonnet for better reasoning

Issue: Too many escalations
Solution: Improve knowledge base coverage, adjust escalation conditions, add more FAQ entries

Issue: Slow response times
Solution: Use faster model (GPT-4o-mini), reduce knowledge base size, optimize search queries

Next Steps​

  1. Add to Slack - Deploy as a Slack bot
  2. Set up webhooks - Integrate with your website chat
  3. Monitor performance - Track metrics and improve
Want more advanced features?

Check out our Multi-Step Agent pattern to build agents that can perform actions like creating tickets, updating orders, or scheduling appointments.