#CS 329Z

The course is structured around three engineering pillars—decomposition, data, and evaluation—giving practitioners a curriculum-level map of what production agent work actually demands beyond prompt tinkering. Developers and ML engineers building agents can use the free lecture notes as a self-study path, especially the coverage of DSPy, MCP and memory architectures. Its critical reading list, including 'Why Do Multi-Agent LLM Systems Fail?', offers a rare sceptical counterweight to agent hype.

shao__meng@shao__mengSource

This course bridges the gap between theoretical AI models and practical system engineering, addressing three core challenges: decomposition, data, and evaluation. Developers and engineers in Southeast Asia can access these materials to build more robust AI systems, with particular focus on frameworks like DSPy and tools like MCP that are becoming industry standards.

shao__meng@shao__mengSource

The course addresses practical engineering challenges for building AI systems, relevant to developers in ASEAN working on agent-based applications.

shao__meng@shao__mengSource