Stanford CS229 I Machine Learning I Building Agents That Do the Work of Human Software Engineers

The podcast explores the construction and utilization of multi-agent systems for software engineering, emphasizing a balance between simplicity and complexity. It begins with basic LLM calls and progresses to multi-agent systems, assessing the problem-solving capabilities at each stage, using a payment system failure as a case study. The discussion covers active critic systems, tool use, and agentic systems, highlighting the importance of planning loops, autonomous execution engines, and memories. Multi-agent systems address limitations like breadth versus depth, planning fragility, and tool overload. The podcast also tackles challenges like multi-agent context, statefulness, and asynchronous activities, advocating for memory and post-training strategies to facilitate learning. It concludes by addressing open questions on tail-patching models and prompting techniques.