Sail(Agentic AI)
Agentic AI represents a paradigm shift in artificial intelligence, moving beyond content creation towards autonomous systems that can proactively solve complex problems and execute sophisticated tasks.
Course Description:
This course is designed to equip learners with the practical skills to implement complete agentic AI workflows from concept to deployment. Learners will gain hands-on experience constructing intelligent agents that can autonomously tackle complex, real-world problems. We will explore the entire pipeline of agentic systems, covering essential components such as task planning and decomposition, memory and context management, tool and API integration for extended capabilities, execution and interaction strategies, and reflective learning for continuous improvement. Learners will learn to leverage cutting-edge generative AI models and agentic frameworks to build agents capable of diverse tasks, including automated research, dynamic content generation, and proactive problem-solving. Our goal is for learners to become proficient in designing, building, and evaluating their own agentic AI systems across a range of applications. Course topics include agent architectures, planning algorithms, memory mechanisms, tool integration techniques, interaction design principles, evaluation methodologies, and ethical considerations for responsible agent development. Project-based learning will focus on implementing agentic workflows for applications like autonomous research assistants, intelligent content creators, and self-managing task automation systems.
Prerequisites:
- AI User
Duration:
- 8 weeks per quarter
- 15 weeks per semester
Learning Objectives
Learners who complete the Agentic AI course should be able to:
- Articulate the core principles and architectural patterns underlying agentic AI systems.
- Design and implement each stage of a functional agentic workflow, from planning to reflection.
- Integrate generative AI models, external APIs, and specialized tools to enhance agent capabilities.
- Develop agentic systems capable of autonomous operation and complex task execution in realistic scenarios.
- Evaluate the performance, robustness, and ethical implications of agentic AI implementations.
- Apply agentic AI principles to develop innovative solutions for real-world problems.
