Triage support tickets with an n8n AI Agent and confidence-based routing
An n8n workflow cleans and validates incoming tickets with plain code nodes, uses an AI Agent only to classify urgency and type, and routes each ticket based on how confident the classification is.
The problem
Sending every step of ticket handling through an LLM is slow, costs more and is hard to trust. Using the model only for judgment calls, with fixed rules around it, makes triage cheaper and safer.
Steps
- Start the workflow with a Webhook node that receives incoming tickets.
- Normalize the input with a Code node and check required fields with IF nodes.
- Add Guardrails nodes to screen inputs and outputs.
- Use an AI Agent with a structured output parser to label each ticket's urgency and category.
- Route with Switch nodes. High-confidence results go straight through, medium ones are flagged and low ones go to a person.
- Or import the article's ready-made version of the workflow (Exercise 5).
Results
- The suggested routing: above 0.85 processes autonomously, 0.6-0.85 is processed but flagged for review, and below 0.6 goes to a human.
- Urgent billing issues go to finance, technical problems to support and general questions to the right owners.
As reported by the source (n8n Blog); AgentGid did not measure these figures.
Suits a small support or ops team that is comfortable with webhooks, JSON and a self-hosted n8n instance, or has the budget for n8n Cloud. Watch the 0.85 and 0.6 thresholds: they are suggested values, so check them against your own tickets before trusting autonomous routing. n8n's downloads are slowing (-20% in 30 days); open-source Dify (Free) is a comparable alternative.
The agent used here
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