This module explores how large language models (LLMs) can be extended with external tools and agents to solve complex tasks. Learners will examine single- and multi-agent system architectures, practical implementations like HuggingGPT and ChemCrow, and compare cloud-based service paradigms such as SaaS, MaaS, DaaS, and RaaS. By the end, you'll understand how to orchestrate multiple models and services to address real-world challenges.

This module guides learners through building interactive AI agent applications using Python and Streamlit, covering essential topics such as frontend development, caching, and integrating multi-agent systems. Learners will also explore model development, training, and deployment best practices, including asynchronous programming, error handling, and containerization with Docker. By the end, participants will be equipped to create, optimize, and securely deploy AI-powered web applications.

This module explores the evolving landscape of AI agents, including their applications in biomedical, industrial, and web domains. Learners will examine the reasoning and creativity capabilities of large language models, the challenges of mechanistic interpretability, and the ethical considerations surrounding autonomous AI. The module also discusses the path toward artificial general intelligence and the limitations of current multi-agent systems.