AI agents often generate answers without true understanding or explainability.
This course will teach you how to design agent-driven knowledge graphs that enable structured reasoning, contextual understanding, and explainable decision-making.

What you'll learn
Many AI agents can retrieve information but still fail to reason effectively or explain how they reach their conclusions.
In this course, Agentic Knowledge Graphs, you'll learn to design and build agent-driven knowledge graphs that enhance reasoning and explainability in AI systems.
First, you'll explore the foundations of knowledge graphs, including entities, relationships, and schemas that support agentic reasoning.
Next, you'll discover how to construct and integrate knowledge graphs using agents that extract and connect data from structured and unstructured sources.
Finally, you'll learn how to apply knowledge graphs in real-world agentic workflows using tools like Neo4j and LangChain to enable contextual querying and explainable decision-making.
When you're finished with this course, you'll have the skills and knowledge of agentic knowledge graphs needed to build intelligent, structured, and transparent AI agents that provide reasoning.