This is a submission for the Algolia Agent Studio Challenge
: Consumer-Facing Non-Conversational Experiences
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 link : website
agent link https://dashboard.algolia.com/apps/OGD4CORSEW/generativeAi/agent-studio/agents/209b0025-3e6c-4e50-8905-233fce174d9c/configure
You can view the live project here: IntentRefiner Demo
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
I leveraged Algolia Agent Studio to index historical issues and training examples, enabling the agent to detect patterns and contextually refine new inputs.
Targeted prompting: I 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 only return valid JSON, ensuring consistent integration with downstream systems.
Why Fast Retrieval Matters
Fast, contextual retrieval is critical because it 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.
Top comments (1)
Thank you @ben and everyone for the hackathon.
live link : profound-lolly-4ad65b.netlify.app/
video link : youtu.be/-Pk2HKYBCa0