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Course guide

AI Agents Explained: From Simple RAG to Agentic AI Systems

AI agents go beyond basic RAG by reasoning, using tools, and executing multi-step plans. Learn agentic RAG, tool calling, memory systems, and LangGraph orchestration.

Basic RAG retrieves documents and generates answers. Agentic RAG goes further - the AI decides WHAT to retrieve, WHEN to use tools, and HOW to break complex questions into steps.

Agent Capabilities

  • Tool calling: Agents can query databases, call APIs, run code, search the web
  • Multi-step reasoning: Break complex questions into sub-tasks
  • Memory: Remember context across conversations
  • Multi-agent: Specialized agents collaborating on complex tasks

Learn AI Agent Engineering

Module 8 covers agent architectures, LangGraph, tool calling, and multi-agent orchestration.

Learning path

Continue from concept to implementation

These course modules place this topic in context, connect it to adjacent decisions, and provide the practical next step.

Module 7

Advanced Retrieval Engineering

Hybrid search, reranking, query expansion, and Graph RAG for production-quality retrieval

4 hours | 4 learning objectives

Module 8

AI Agents & Agentic RAG

Tool calling, memory systems, multi-agent architectures, and LangGraph orchestration

4 hours | 4 learning objectives

Module 9

Production RAG Architecture

Scaling, multi-tenancy, caching, API gateways, and high-availability RAG deployments

4 hours | 4 learning objectives

Practical outcomes

What the recommended module teaches

  • Build AI agents that reason and use tools
  • Implement agentic RAG with dynamic retrieval
  • Design multi-agent systems for complex tasks
  • Use LangGraph for agent orchestration