Curated materials to support your learning journey
We've compiled a comprehensive collection of resources to support your learning journey in agentic AI systems architecture. These materials complement the course content and provide additional perspectives, deeper dives into specific topics, and practical tools for implementation.
The resources are organized by type and relevance to specific modules. We recommend exploring these materials alongside the course to enhance your understanding and broaden your knowledge base.
This collection is regularly updated to include the latest research, tools, and best practices in the rapidly evolving field of agentic AI systems.
By Stuart Russell and Peter Norvig
The definitive textbook on AI, with comprehensive coverage of agent architectures, search algorithms, knowledge representation, and planning.
By Maria Chen and David Johnson
A practical guide to designing and implementing autonomous AI agents, with case studies and implementation examples.
By Yoav Shoham and Kevin Leyton-Brown
Explores the theoretical foundations of multi-agent systems, including coordination, communication, and emergent behaviors.
By Richard S. Sutton and Andrew G. Barto
The definitive guide to reinforcement learning, a key technique for agent decision-making.
By Yao et al. (2023)
Introduces a framework for integrating reasoning and acting in language models, a key approach for modern agentic systems.
By Significant Gravitas (2023)
Technical report on the development of AutoGPT, an early implementation of autonomous agents using large language models.
By Chase et al. (2023)
Describes the architecture and design principles of LangChain, a popular framework for building agentic applications.
By Park et al. (2023)
Explores the design of generative agents that simulate human behavior through memory, planning, and reflection.
A framework for developing applications powered by language models, with components for agents, memory, and tool integration.
A framework for building applications using multiple conversational agents that can work together to solve tasks.
A data framework for building LLM applications with structured data and knowledge retrieval capabilities.