In this challenge, you will build a complete agent from scratch, configure tools, and test with real questions.
Goal
Build a Northwind Analyst agent that can answer structured queries, semantic searches, and multi-hop relationship questions.
Before You Start
Make sure you have:
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Northwind loaded with embeddings and a vector index (Module 2, Lesson 0)
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Tool authentication enabled for your instance
Steps
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Create a new agent in Aura Console → Data Services → Agents → Create Agent
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Configure basics: Set name to "Northwind Analyst", add a description, and connect to your Northwind instance
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Write instructions: Tell the agent its role and what it can do. For example: "You are a retail analyst with access to Northwind data. Answer questions about customers, products, orders, and suppliers."
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Add Cypher Template tools: Create tools for common queries like Get Customer, Products by Category, Top Customers by Order Count
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Add a Similarity Search tool: Connect it to your
product_text_embeddingsindex -
Enable GraphRAG if available for community summaries
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Save and test with the questions below
See Aura Agent documentation for tool and GraphRAG configuration details.
Test Questions
Run these questions against your agent:
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"Which customers have placed the most orders?"
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"What are the top 5 products by revenue?"
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"List products in the Beverages category"
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"Find customers who ordered products from more than 2 different suppliers"
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"Summarize what you know about customer ALFKI and their order history"
If you have a vector index:
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"Find products similar to spicy condiments"
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"Find products similar to hot sauce"
What to Check
Verify that your agent:
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Selects the right tool for each question
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Returns accurate results that match what you would get from a direct Cypher query
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Handles relationship-heavy questions using GraphRAG context when enabled
Summary
In this challenge, you built a knowledge retrieval agent with Cypher tools, Similarity Search, and GraphRAG.