IntentRefiner: AI-Powered Issue Refinement for Faster Support Automation

Source: Dev.to

What I Built

I built IntentRefiner, an AI agent that automatically converts vague, emotional, or underspecified user complaints into clear, actionable intents for internal workflows. It silently processes inputs, identifies recurring failure patterns, and outputs structured, internally actionable data without requiring back‑and‑forth conversation. This improves routing, resolution speed, and decision‑making for support teams.

Demo: IntentRefiner Demo
Agent configuration:

The demo shows how the agent takes unstructured user complaints and outputs a refined intent, confidence score, category, matched issues, and suggested tags.

How I Used Algolia Agent Studio

• Targeted prompting: Engineered prompts to emphasize pattern recognition and actionable outputs.
• Indexed data retrieval: Historical issues are queried dynamically to provide probabilistic signals for recurring problems.
• Structured output enforcement: The agent is instructed to return only valid JSON, ensuring consistent integration with downstream systems.
• Historical indexing: Leveraged Algolia Agent Studio to index historical issues and training examples, enabling the agent to detect patterns and contextually refine new inputs.

Why Fast Retrieval Matters

Fast, contextual retrieval allows the agent to quickly reference historical issues and generate high‑confidence refined intents in real time. This reduces manual triage, accelerates response times, and ensures that insights from past issues are immediately actionable. Algolia’s speed and relevance directly enhance the accuracy and usability of the AI agent.