Designing Knowledge Graphs with Multi-Agent Systems

Unstructured data is expanding rapidly, but turning it into actionable, connected intelligence remains a major challenge. By combining the power of multi-agent AI systems with knowledge graphs, you can automate the extraction, organization, and validation of complex data relationships. This text-based course guides you through the entire process of planning, designing, and building an automated knowledge graph construction pipeline. You will transition from understanding basic graph concepts to orchestrating a team of specialized AI agents that collaborate to turn raw text into a structured, queryable knowledge base. What you'll learn: - Understand the foundational architecture of knowledge graphs, schemas, and triple stores - Design specialized AI agents with distinct roles for planning, extraction, and validation - Implement agentic workflows that collaboratively resolve entity ambiguity and link data points - Apply prompt engineering techniques to guide LLMs in generating accurate graph structures - Explore hybrid retrieval patterns by combining vector search with graph queries for advanced RAG - Practice constructing and querying a knowledge graph through written step-by-step code walkthroughs We begin with the core definitions of graph databases and agentic systems, ensuring you have a solid theoretical foundation. From there, you will explore the written code patterns and logical steps required to set up agent communication, extract semantic relationships, and assemble a robust knowledge network. This course is designed for software developers, data enthusiasts, and AI beginners who want to learn modern data structuring techniques. No prior experience with graph databases or multi-agent frameworks is required. Start reading today to master the intersection of multi-agent coordination and knowledge representation.