This project is an educational curriculum and architectural framework for building autonomous AI agents and multi-agent systems. It provides a structured learning path focused on the development of independent software components capable of planning, executing tasks, and utilizing external tools to achieve high-level goals.
The framework emphasizes multi-agent system orchestration through distributed architectures where specialized agents collaborate using standardized communication protocols. It details specific design patterns such as dual-memory systems for maintaining short-term plans and long-term history, as well as evaluator-optimizer loops for iterative output refinement.
The project covers a broad range of technical capabilities, including retrieval augmented generation with knowledge graph grounding, the implementation of safety guardrails and human-in-the-loop oversight, and the use of stateful actor models. It also addresses the operational side of AI, including containerized deployment via Kubernetes and the management of machine-to-machine payments.
The materials are delivered as a series of technical guides and courses, utilizing Jupyter Notebooks for practical implementation.
