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Webinar Feedback Response Agent

Source reviewed Updated 2026-07-07

Captures webinar feedback via webhook, analyzes sentiment with AI.

What it does

You automatically collect webinar feedback and receive personalized thank-you emails based on the attendee's sentiment. The system logs every interaction in your Google Sheets so you can track responses without manual effort.

Step by step

  1. Set up a webhook endpoint to receive POST requests containing JSON feedback data from the webinar platform.
  2. Parse the incoming payload and extract fields such as attendee email, rating score, and text comments using standard string manipulation or JSON parsing libraries.
  3. Construct an HTTP request to the Gmail API with the appropriate headers for authentication and the formatted HTML body for the email content.
  4. Append a new row to the Google Sheets spreadsheet containing the timestamp, original feedback data, sentiment result, and email status code.

Where the LLM does the work

  • Send the raw attendee comments to OpenAI with a system prompt instructing it to return JSON output containing a sentiment label (positive, neutral, negative) and a brief summary of key themes.
  • Generate personalized thank-you email copy by injecting the attendee's name and specific references from their feedback into a dynamic template using LLM-generated text variations.

Watch out for

Webhook payloads may contain special characters or HTML entities in comments which can break email formatting if not properly escaped before constructing the HTML body.
Google Sheets API quotas are strict for bulk operations, so batch insert rows using the sheets.batchUpdate method instead of appending one by one in a loop.

Tools that fit

OpenAI LLM Analyze sentiment and generate response text
Gmail Service Send personalized thank you emails
Google Sheets Service Store feedback data and log outcomes
Code API Normalize input and build HTML email

The agent brief

Everything your agent needs, including the gotchas. Copy it and go.
agent-brief.md
You are helping me build the following AI agent workflow.

## Goal
Webinar Feedback Response Agent: Captures webinar feedback via webhook, analyzes sentiment with AI.

## Specification
- What it does: You automatically collect webinar feedback and receive personalized thank-you emails based on the attendee's sentiment. The system logs every interaction in your Google Sheets so you can track responses without manual effort.
- Trigger: Triggered by an event (Event · on new feedback submission)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - OpenAI: Analyze sentiment and generate response text
  - Gmail: Send personalized thank you emails
  - Google Sheets: Store feedback data and log outcomes
  - Code: Normalize input and build HTML email

## Known pitfalls, handle each one explicitly in your implementation
1. Webhook payloads may contain special characters or HTML entities in comments which can break email formatting if not properly escaped before constructing the HTML body.
2. Google Sheets API quotas are strict for bulk operations, so batch insert rows using the sheets.batchUpdate method instead of appending one by one in a loop.

## Reference implementation
https://n8n.io/workflows/11889 (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/automated-webinar-feedback-response-agent/ via usecasesforagents.com

Frequently asked questions

Can I use a different LLM than OpenAI?

Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.

Want this running in your business?

This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Get in touch →

Who it's for

Built for marketers who'd rather brief an agent than start from a blank doc.

Seen in the wild

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