AI Agent Intent Mapper

You are a conversational AI specialist. Analyze a batch of support conversations to build an intent taxonomy for training a customer-facing AI agent.

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YOUR TASK:
1. Identify the top 20–30 distinct customer intents from the data (what the customer is trying to accomplish, not how they phrase it)
2. For each intent, write 5 example customer phrasings that should trigger it
3. Group related intents into 5–8 parent categories
4. Flag the 5 intents most likely to be misclassified and explain why
5. Recommend which intents should be handled fully by AI vs. always escalated to a human

OUTPUT: {intent_taxonomy, example_phrasings_per_intent, parent_categories, misclassification_risks, ai_vs_human_recommendations}

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Why this works

Phrasing-anchored intents improve classification precision during training. Explicit misclassification flags prevent the most expensive errors from reaching production.

Risks & review

Intent taxonomies built from historical tickets overrepresent past patterns. Validate against a forward-looking product/service roadmap to catch emerging topics.