Building AI Agents with n8n: The Complete 2025 Guide

Step 1: Create Webhook Trigger

In n8n, add a Webhook node. This will be the entry point for customer questions.

Webhook URL: https://your-n8n.app/webhook/support
Method: POST
Expected data:
{
  "question": "How do I return a product?",
  "customer_id": "cust_123",
  "customer_name": "Jane Doe"
}

Step 2: Retrieve Knowledge Base

Add a Supabase node to query your knowledge base. Search for articles matching the customer's question.

SELECT title, content, category
FROM knowledge_base
WHERE to_tsvector('english', title || ' ' || content)
  @@ plainto_tsquery('english', '{{ $json.question }}')
ORDER BY ts_rank(
  to_tsvector('english', title || ' ' || content),
  plainto_tsquery('english', '{{ $json.question }}')
) DESC
LIMIT 3

Step 3: Generate AI Response

Add an OpenAI node with the GPT-4 model. Pass the knowledge base results and customer question to generate a personalized response.

System Prompt:
You are a helpful customer support agent for [Company Name].
Use the following knowledge base articles to answer the customer's question.
Be friendly, professional, and concise.

If you're not confident in your answer, say so and suggest contacting support.

Knowledge Base:
{{ $json.knowledge_base }}

User Prompt:
Customer: {{ $json.customer_name }}
Question: {{ $json.question }}

Step 4: Save Conversation

Store the conversation in Supabase for history and follow-ups.

INSERT INTO conversations
  (customer_id, question, answer, created_at)
VALUES
  ('{{ $json.customer_id }}',
   '{{ $json.question }}',
   '{{ $json.ai_response }}',
   NOW())

Step 5: Return Response

Use a Respond to Webhook node to send the AI's answer back to your app.

Response:
{
  "answer": "{{ $json.ai_response }}",
  "conversation_id": "{{ $json.conversation_id }}",
  "confidence": "high"
}

Technical Implementation

Error Handling & Resilience

• →Always add error nodes: Catch API failures, rate limits, and timeouts
• →Implement retries: Use n8n's retry settings for flaky APIs (3 retries, exponential backoff)
• →Graceful degradation: If AI fails, have fallback messages or escalate to human
• →Log everything: Store errors in Supabase for debugging and monitoring

Cost Management

• →Use GPT-3.5-turbo for simple tasks: 10x cheaper than GPT-4, good for 80% of use cases
• →Optimize prompts: Shorter prompts = lower costs. Remove unnecessary context
• →Cache responses: Store common answers in Supabase to avoid duplicate LLM calls
• →Set usage limits: Monitor OpenAI usage and set budget alerts

My typical costs for production agent:
OpenAI: $15-50/month • n8n hosting: $5-20/month • Total: $20-70/month

Security Considerations

• →Validate inputs: Sanitize user inputs to prevent prompt injection attacks
• →Authenticate webhooks: Use API keys or signatures to verify webhook sources
• →Never expose API keys: Use n8n credentials system, never hardcode keys
• →Rate limiting: Protect your webhooks from abuse with rate limits

Performance Optimization

• →Parallel execution: Run independent nodes in parallel for faster workflows
• →Batch processing: Process multiple items together instead of one-by-one
• →Database indexes: Add indexes to Supabase queries for faster searches
• →Async where possible: Don't make users wait for non-critical operations