Skip to main content
This document will give a walkthrough of how the Ekyam’s Retail Knowledge Graph (RKG) is a powerful tool for AI agents. It provides the agents with the structured context and relationships to understand complex queries and generate relevant responses. Use of RKG in Agent Reasoning A Retail Knowledge Graph basically serves as an “Authoritative Source of Truth” for AI agents to provide structured and validated data, which agents can rely on for generating accurate responses. Furthermore, it acts as the “Retrieval” component  in a Retrieval-Augmented Generation (RAG) system for retail applications. By acting as a Retrieval component, RKG stores interconnected retail entities (products, customers, transactions) that agents can query to augment their knowledge before generating responses.  How do Agents Query the Graph The most crucial step of an AI agent is to retrieve relevant information before generating a response. When it involves the use of RKG, then the retrieval process becomes highly structured as it leverages the data from the graph.  Primarily, the agents use Cypher queries to traverse the graph, which helps in retrieving specific facts like product relationships, customer purchase history and sales patterns before formulating answers.  How RKG powers AI Agent Reasoning The RKG’s graph structure allows agents to follow relationships across multiple hops. Below is the step-by-step explanation of how an agent uses the RKG for multi-step reasoning: Customer → Purchase → Product → Category → Supplier This process is a traversal of the graph. The agent is not performing multiple look-ups rather it is navigating a pre-existing, pre-computed web of relationships. This relationship allows to: 
  • Connect Disparate Data: The RKG links products, customers and transactions, and eliminates the need for complex connect. 
  • Increase Efficiency: To traverse a relationship in a graph is more efficient for complex, interconnected queries than connecting joints in a relational database.
Use case An agent can answer “Which customers bought this shirt in the last quarter and what were the total sales?”  By traversing: Product → Sales Transactions → Customer Profiles → Aggregating sales data across time periods, all in a single graph query. With this semantic, interconnected data model, RKG transforms an AI agent from a data retriever to an intelligent responder.