Lecture Three part 01 From RAG to Agentic RAG / Retrieval Systems Evolve into Decision-Capable AI

Описание: In this lecture, we move beyond standard Retrieval-Augmented Generation and examine how modern AI systems evolve into agentic architectures that can reason, decide, retrieve, use tools, and act across multiple steps.

We begin with the limits of traditional RAG and explain why one-step retrieval is often not enough for real engineering and business tasks. From there, we introduce Agentic RAG as a loop-based system built around observation, reasoning, action, memory, and dynamic decision-making.

Topics covered in this lecture include:

traditional RAG versus agentic RAG

why one-pass retrieval breaks down in complex tasks

the role of tools such as SQL, Python, APIs, and code execution

why memory matters in multi-step AI systems

agentic RAG as a decision and control system

benefits, costs, risks, and evaluation challenges

when to use traditional RAG and when to escalate to agentic RAG

practical examples of multi-step retrieval, tool use, and workflow design

This lecture is part of the AI Systems Architecture Course and is designed with an engineering-first perspective: practical, structured, and focused on how real systems are built.

If you are interested in modern AI architecture, retrieval systems, agents, tool-enabled language models, or production AI design, this lecture will give you a clear conceptual and technical foundation.

Instructor: Dr. Mohamad Aoude

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