This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks.
The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correcting reasoning and human-in-the-loop oversight for critical action approval.
The materials extend to the coordination of multi-agent systems through task decomposition and communication protocols, as well as the management of short-term session context and long-term persistent memory. Further technical coverage includes agent observability, secure deployment practices, and the integration of external tools and data sources.
The project is delivered primarily as a collection of Jupyter Notebooks.
