In this episode, we decode three of the most compelling architectures in the modern AI stack: Retrieval-Augmented Generation (RAG), AI Agent-Based Systems, and the cutting-edge Agentic RAG. Based on the in-depth technical briefing Retrieval, Agents, and Agentic RAG, we break down how each system works, what problems they solve, and where they shine—or struggle.

We explore how RAG grounds LLM responses with real-world data, how AI agents bring autonomy, memory, and planning into play, and how Agentic RAG fuses the two to tackle highly complex, multi-step tasks. From simple document Q&A to dynamic, multi-agent marketing strategies, this episode maps out the design tradeoffs, implementation challenges, and best practices for deploying each of these architectures. Whether you're building smart assistants, knowledge workers, or campaign bots, this is your blueprint for intelligent, scalable AI systems.