Auto-Categorize Support Tickets
New Zenes tickets auto-sort instantly into Sales, Engineering, or Customer Support buckets.
What it does
Sort incoming Zenes tickets automatically into three specific buckets: Sales, Engineering, or Customer Support. This saves manual sorting time and ensures every request reaches the correct team immediately upon arrival.
Step by step
- Parse the incoming JSON payload from the Zenes webhook to extract the raw ticket text and metadata.
- Map the extracted fields into a standardized schema expected by your downstream ticketing system API.
- Send the structured ticket data as a synchronous request to the LLM inference endpoint with a fixed prompt template.
Where the LLM does the work
- Instruct the model to analyze the customer's intent and assign one of three specific labels: Sales, Engineering, or Customer Support.
- Ask the LLM to output only the category name in JSON format without any conversational filler or markdown formatting.
- Handle edge cases where the ticket content is ambiguous by having the model request clarification or defaulting to a safe bucket.
Watch out for
Costs will spike if you process every raw character of long tickets, truncate input text to a reasonable token limit before sending.
Ambiguous requests like general inquiries may get misclassified, so implement a human-in-the-loop review step for low-confidence scores.
Tools that fit
Zenes Service ticket ingestion source
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Auto-Categorize Support Tickets: New Zenes tickets auto-sort instantly into Sales, Engineering, or Customer Support buckets. ## Specification - What it does: Sort incoming Zenes tickets automatically into three specific buckets: Sales, Engineering, or Customer Support. This saves manual sorting time and ensures every request reaches the correct team immediately upon arrival. - Trigger: Triggered by an event (Event · on new Zenes ticket) - Autonomy: Fully hands-off - Expected setup effort: under an hour - Tools/services involved: - Zenes: ticket ingestion source - LLM: classify ticket intent into Sales, Engineering, or Support buckets ## Known pitfalls, handle each one explicitly in your implementation 1. Costs will spike if you process every raw character of long tickets, truncate input text to a reasonable token limit before sending. 2. Ambiguous requests like general inquiries may get misclassified, so implement a human-in-the-loop review step for low-confidence scores. ## Reference implementation https://www.youtube.com/watch?v=4fgqkZi8CEY (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/auto-categorize-support-tickets/ via usecasesforagents.com
Frequently asked questions
Can I use a different Search than Zenes?
Yes. Zenes is only the example search in this recipe. The same flow works with GNews, InfraNodus, Jina AI and NewsAPI. Swap the search 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.
Who it's for
Built for support teams drowning in inbound and looking to triage faster, not just harder.
Sales teams can plug this into their own CRM and call cadence just as easily.
Developers can adapt this to their own repo or ticket queue with the same trigger and tools.