Today I’m starting a lecture on Multi-Agent Architecture, focusing on how modern AI systems move beyond single LLM prompts and into coordinated agent ecosystems. In real-world AI products, the challenge isn’t generating text — it’s orchestrating multiple agents that can plan, reason, and execute tasks reliably. In this session we’ll break down: • Core architecture patterns for multi-agent systems • Agent orchestration, routing, and task decomposition • Tool usage and memory management • Building reliable pipelines instead of fragile prompt chains • Real production use cases from modern AI systems The goal is simple: move from demos to production-grade AI architectures. If you're building with LLMs, AI agents, or automation pipelines, understanding multi-agent design patterns will be one of the most important skills going forward. More details and implementation walkthrough coming in the lecture. Let’s build systems that actually scale. ⚙️

Today, following our discussion on LLM Orchestration, we are specifically introducing the RAG Pipeline. For satisfactory processing, the RAG (Retrieval-Augmented Generation) pipeline is a key element in building AI systems that provide successful and context-aware answers. This pipeline combines the powerful capabilities of language models with document-related search functions, ensuring that AI responses are based on user data rather than relying solely on prior knowledge. The following is a subsequent diagram illustrating the RAG pipeline. It shows how data is retrieved, processed, and used to generate high-quality, powerful answers. This approach not only enables excellent answers but also allows for the integration of features through added content. We welcome any questions related to software, including issues encountered during the learning and development process. Our goal is ```for the future```.