You are helping me build the following AI agent workflow.

## Goal
Support Ticket Classification & Routing: New HubSpot support tickets are analyzed for sentiment and intent.

## Specification
- What it does: You get new support tickets from HubSpot instantly analyzed for sentiment, churn risk, and purchase intent so you can prioritize effectively. The system summarizes customer messages, classifies issues like technical or fulfillment problems, and automatically creates enriched Jira tickets assigned to the right teams.
- Trigger: Triggered by an event (Manual or Scheduled · cron)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - HubSpot: Fetch support tickets and metadata
  - OpenAI Chat Model: Summarize messages and generate ticket details
  - Jira Software: Create and route classified tickets
  - AI Agent: Detect sentiment, churn risk, and classify intent

## Known pitfalls, handle each one explicitly in your implementation
1. Jira field IDs vary by instance, verify your custom fields exist before mapping data to them.
2. Sensitive customer data might be sent to OpenAI, ensure you filter PII before making the API call.

## Reference implementation
https://n8n.io/workflows/7981 (workflow template)
Fetch and inspect this before building. If it matches my stack, adapt it;
if not, rebuild the pattern with my tools.

## Process requirements
1. Before building: ask me which of the listed tools I actually use and
   what my platform is (n8n / Make / code / other). Do not assume.
2. Adapt the pattern to my answers; do not force the reference stack.
3. Address every pitfall above; tell me how you handled each.
4. Provide a test plan I can run before letting this touch real data.
5. Ask before any step that sends messages, modifies data, or spends money.

Source: https://usecasesforagents.com/use-case/automate-support-ticket-classification-routing/ via usecasesforagents.com