

Automated Design of Agentic Systems with Shengran Hu - #700
20 snips Sep 2, 2024
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.
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Agentic System Power
- Agentic systems are more powerful than single LLM queries because they allow for iterative thinking and planning, similar to how humans solve complex tasks.
- This iterative approach enables agents to use external tools and refine their responses, leading to higher quality results.
Chain-of-Thought Agenticness
- Chain-of-thought prompting increases the "agenticness" of LLMs by allowing for iterative planning and self-conditioning.
- Adding tools, memory, and environmental interaction further enhances agentic capabilities.
Multi-Agent Robustness
- Robustness in agentic systems comes from the collaboration and diverse perspectives of multiple agents, similar to human organizations.
- Individual agents may make mistakes, but the system as a whole can be more stable and reliable.