You are helping me build the following AI agent workflow.

## Goal
Support QA Reviews: On closed Intercom tickets, an agent scores clarity, tone, and resolution via GPT and logs results to Google Sheets.

## Specification
- What it does: You get instant quality checks on every closed Intercom ticket as the agent scores your support conversations for clarity, tone, and resolution using OpenAI. It automatically logs these structured results to Google Sheets so you can track performance and provide targeted coaching feedback without manual review.
- Trigger: Triggered by an event (Event · on conversation closed)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - Intercom: Trigger and fetch conversation data
  - OpenAI Chat Model: Evaluate response quality and tone
  - Google Sheets: Log structured QA scores

## Known pitfalls, handle each one explicitly in your implementation
1. Long transcripts may exceed token limits, so truncate or summarize history before sending to the model.
2. Ambiguous ticket statuses might trigger false positives, so filter webhooks strictly by closed status and valid IDs.

## Reference implementation
https://n8n.io/workflows/5831 (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-qa-reviews/ via usecasesforagents.com