You are helping me build the following AI agent workflow.

## Goal
E-commerce Refund Risk Scoring Agent: New e-commerce orders are automatically scored for refund risk by an AI agent that logs results to Sheets and alerts teams via Discord or...

## Specification
- What it does: You instantly identify refund and chargeback risks for new orders so your team can act before losses occur. This agent analyzes incoming transactions, assigns clear risk scores to Google Sheets, and sends immediate alerts via Discord or Gmail whenever high-risk cases are detected.
- Trigger: Triggered by an event (Event · on new order webhook)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - Webhook: Ingest incoming order payloads
  - OpenAI: AI risk scoring and classification
  - Google Sheets: Log results and deduplication check
  - Discord: Send real-time high-risk alerts
  - Gmail: Email finance stakeholders with risk context

## Known pitfalls, handle each one explicitly in your implementation
1. Webhook payloads may contain sensitive PII, so strip or hash personally identifiable information before sending data to the OpenAI API.

## Reference implementation
https://n8n.io/workflows/12576 (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/e-commerce-refund-risk-scoring-agent/ via usecasesforagents.com