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CS329A Self-Improving AI Agents - mklabgr - 09-28-2026
Stanford CS329A: Self-Improving AI Agents focuses on developing artificial intelligence agents capable of continuously improving their performance through interaction with their environment. It explores techniques such as reinforcement learning (RL), test-time computational scaling, automated verification, search algorithms and feedback-based learning.
The course also examines how large language models (LLMs) can be enhanced with memory, external tools, code execution and multimodal capabilities. Particular emphasis is placed on enabling AI systems to perform complex, multi-step reasoning, planning and autonomous tasks, rather than simply answering individual questions.
The course combines research papers, practical assignments, invited lectures and original research projects. Students investigate applications such as autonomous coding agents, scientific research assistants and robotic systems. Topics include self-improving AI, automated software engineering, long-term memory, deep research agents and the evaluation of autonomous systems.
Students work in teams of two to four to develop original research projects, supported by API credits. Assessment consists of three homework assignments, presentations and a final research project. Overall, the course explores how AI can move beyond static models toward autonomous systems that learn, reason, use tools and improve themselves.
COURSE