#SWE-agent

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

The instructor lineup signals the course will treat agent scaffolding as an engineering discipline rather than a prompt trick, with evaluation given equal weight to construction. Builders designing agent systems can use the free slides as a structured curriculum that maps design patterns like ReAct and plan-and-execute to concrete trade-offs in cost and latency. Stanford making materials openly available means anyone outside campus can follow along week by week.

shao__meng@shao__meng2 sourcesSource

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

shao__meng@shao__mengSource