Support Ticket Triage
Classify intent, extract entities and sentiment in parallel, then draft a grounded response.
A support ticket comes in. The pipeline classifies the intent, extracts entities and sentiment in parallel, then drafts a response grounded in the classification.
Tree
Blocks
classify-intent (llm)
Output schema:
extract-entities (llm) — runs in parallel with sentiment
score-sentiment (llm) — runs in parallel with entity extraction
draft-response (llm)
Why this shape?
- Intent classification first, because the response draft depends on it. Everything downstream is conditioned on knowing what the ticket is about.
- Entities and sentiment in parallel because they don't depend on each other. Sequential would just add latency for no reason.
- Final block sees all three signals. Naming each upstream block clearly (
classify_intent,extract_entities,score_sentiment) lets the response prompt reference them explicitly without a code block to reshape the data. - The original ticket is re-passed to
draft-responsebecause the response needs the actual customer's words, not just structured signals. This is one of the few times re-passing raw input is correct.