CS 329Z: Engineering AI Agents [Stanford]

[mklabgr]

Stanford’s CS329Z: Engineering AI Agents is a Fall 2026 course focused on the practical engineering of agentic AI systems—moving beyond standalone LLMs toward systems that combine language models with retrieval, tools, memory, optimizers, and multiple interacting agents. The course covers the full development process: deciding when agents are appropriate, decomposing tasks, selecting models and components, engineering context, building RAG systems and tool-using agents, designing agent workflows and multi-agent architectures, optimizing prompts/models, collecting data, and creating reliable evaluations. It also addresses safety, privacy, prompt injection, sandboxing, coding agents, proactive agents, multimodal/web agents, and the challenges of deploying long-running systems. 

A particularly strong aspect is its hands-on engineering emphasis. Students first build fundamental components such as RAG pipelines, tool-use systems, memory, and agent loops from scratch, before using frameworks such as DSPy, LangGraph, and LlamaIndex. They then build an agentic harness, design a comprehensive evaluation system for an agent, analyze recent research, and complete a quarter-long project involving a working agent.

 The course therefore treats agents not simply as prompting exercises but as systems that must be designed, optimized, evaluated, and made reliable. Its central message is that successful AI applications increasingly depend on the engineering of the surrounding system—not merely on choosing a more powerful language model.

COURSE