Generative AI and Multi-Agent Systems Foundations

As AI evolves beyond simple chatbots, the ability to coordinate multiple specialized agents is becoming the standard for solving complex real-world problems. This course provides a clear, text-based path into the world of autonomous systems and advanced AI architectures, focusing on how different AI entities can work together to achieve a common goal. You will transition from understanding basic prompts to designing collaborative agent networks that can search, reason, and act on data autonomously. By the end of this program, you will have the knowledge to structure sophisticated AI workflows that go far beyond simple text generation. What you'll learn: - Understand the core principles of Generative AI and modern language model architectures. - Implement Retrieval-Augmented Generation (RAG) to connect AI models with external data sources. - Apply semantic search techniques and vector database concepts to improve information retrieval. - Design autonomous agents capable of planning tasks and interacting with external tools. - Build multi-agent workflows where specialized entities collaborate to solve complex problems. - Practice prompt engineering patterns specifically for agentic reasoning and decision-making. The course begins with essential terminology and foundational AI concepts before moving into the mechanics of RAG pipelines and the logic required to orchestrate multiple autonomous agents. You will progress through written explanations and code examples that demonstrate how to manage state and memory in complex AI systems. This course is designed for technical learners and developers who are new to multi-agent systems and want a structured introduction to the field without any prerequisites. Start building the next generation of collaborative AI systems today.