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Agentic Rag

Agentic RAG

Learn to build intelligent, self-reflective retrieval agents with LlamaIndex while mastering reasoning-driven RAG, evaluation methods, and the deployment of scalable agentic systems.

Intermediate

13 Lessons

4h

Certificate of Completion

Learn to build intelligent, self-reflective retrieval agents with LlamaIndex while mastering reasoning-driven RAG, evaluation methods, and the deployment of scalable agentic systems.

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AI-POWERED

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This course includes

12 AI Feedbacks
9 Playgrounds

This course includes

12 AI Feedbacks
9 Playgrounds
Course Overview
What You'll Learn
Course Content

Course Overview

RAG systems have changed how AI accesses external knowledge, but agentic RAG transforms retrieval into a reasoning-driven process. This course unpacks the fundamentals of agentic intelligence and combines reasoning with retrieval to achieve higher factual accuracy and autonomy. You’ll explore the anatomy of an agent, from its memory and tools to the orchestration logic that drives self-directed behavior. Through hands-on lessons, you’ll build an AI research assistant using LlamaIndex by assembling its tool...Show More

What You'll Learn

The ability to design reasoning-driven RAG systems that plan, reflect, and refine autonomously

An understanding of how to build and orchestrate multi-tool agents using LlamaIndex

The ability to debug, evaluate, and optimize retrieval workflows with structured metrics

What You'll Learn

The ability to design reasoning-driven RAG systems that plan, reflect, and refine autonomously

Show more

Course Content

1.

Foundations of Agentic RAG

Explore the evolution of RAG into agentic systems for enhanced problem-solving.
From RAG to Agentic RAG
The Anatomy of an Agent: Components and Core Logic
Agentic Reasoning Strategies: From ReAct to Multi-Agent Systems
2.

Implementation with LlamaIndex

Explore the development of a dynamic agentic RAG system for AI research assistance.
Basic RAG vs. Agentic RAG Implementation with LlamaIndexDesigning Our Agentic RAG System: The AI Research AssistantThe AI Research Assistant: Implementing the Agent’s Core ToolsThe AI Research Assistant: Agent Assembly and Orchestration
3.

Refining and Evaluating Agents

Enhance AI agents’ reliability and evaluate performance through advanced metrics and techniques.
Debugging, Refining, and Fortifying Our AgentEvaluation Metrics and Libraries for Agentic RAG Workflow
4.

Advanced Concepts and Deployment

Explore scalable architectures, deployment challenges, and future trends in agentic AI.
The Dependency Graph of Tools: A Scalable Architecture for AgentsFrom Prototype to Production: The Challenges of Deploying AgentsFuture Trends and Conclusion
5.

Assessment

Apply knowledge through practical exercises in AI design and coding.
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