Engineering AI Agents

Catalog description

The shift from monolithic language models to compound AI systems - systems with multiple interacting components including LLMs, retrievers, tools, and optimizers - represents a fundamental change in how AI applications are built for people. This course teaches students how to engineer agentic systems: the full spectrum from simple LLM pipelines to compound AI systems to autonomous agents. Students will learn to pick what types of problems to focus on, decompose problems, select appropriate components, collect and curate data, build evaluations, and reason about the design tradeoffs that arise when building these systems in practice. Students first build core components (RAG, tool use, agent loops) from scratch, then learn how frameworks like DSPy abstract these patterns. Through three fully applied homework assignments and a quarter-long project, students will gain hands-on experience building, optimizing, and evaluating agentic systems.