From Knowledge Graph Assistant to Agentic AI with LangGraph

Artificial Intelligence is rapidly evolving from traditional question-answering systems to intelligent, autonomous agents capable of planning, reasoning, collaborating, and making context-aware decisions. While Knowledge Graph Assistants provide a strong foundation for enterprise AI, modern applications require production-ready Agentic AI systems that can orchestrate specialized agents, maintain memory, enforce security, and generate explainable, trustworthy responses. In this course, you will transform a Knowledge Graph Assistant into a complete Enterprise Agentic AI system using LangGraph.

You will progressively build a multi-agent architecture capable of task planning, enterprise knowledge retrieval, response validation, human-in-the-loop workflows, memory management, and scalable execution. You will develop an end-to-end enterprise application using LangGraph, Neo4j Knowledge Graphs, Retrieval-Augmented Generation (RAG), hybrid retrieval, vector search, Large Language Models, and modern AI engineering practices. You will also learn how to design and orchestrate specialized agents, including Planner, Ontology, Query, Validation, Execution, Response, Memory, Audit, and Evaluation Agents using stateful graph execution.

The course also covers enterprise AI concepts such as AgentState, graph execution, conditional routing, guardrails, observability, evaluation frameworks, asynchronous execution, streaming responses, Role-Based Access Control (RBAC), multi-tenancy, and cost monitoring. Practical healthcare-inspired examples are used throughout to demonstrate real-world implementation while following sound software engineering principles. This advanced, project-based course is designed for software engineers, AI engineers, solution architects, knowledge engineers, and developers who want to build enterprise-scale Agentic AI applications.

By the end of the course, you will have implemented a production-ready Agentic AI platform and gained practical experience with the architectural patterns used in modern enterprise AI systems.