In this engaging discussion, Shengran Hu, a PhD student at the University of British Columbia, delves into Automated Design of Agentic Systems (ADAS). He shares insights on the spectrum of agentic behaviors and how LLMs can be used for creating novel agent architectures. The conversation highlights the iterative nature of ADAS and its role in revealing emergent behaviors, particularly in complex tasks like the ARC challenge. Shengran also explores practical applications of ADAS in real-world system optimization, emphasizing the balance between innovation and stability.
