Language Agents: From Reasoning to Acting — with Shunyu Yao of OpenAI, Harrison Chase of LangGraph

In this episode of the Latent Space podcast, Alessio and Swyx host Harrison Chase and Shunyu Yao to discuss the evolution and future of AI agents. The conversation begins with Shunyu's journey into language models and the development of ReAct, a method for enabling agents to interact with external environments. They explore the shift from reinforcement learning to zero-gradient approaches and the importance of "thinking" as a tool for AI. The discussion covers Reflection, Tree of Thoughts, and the Cognitive Architectures for Language Agents (CoALA), emphasizing the balance between simplicity and complexity in prompting strategies. They also discuss the significance of benchmarks like SWE-Bench and TauBench, and the need for better coding and web agent benchmarks. The episode further explores the design of agent-computer interfaces (ACI), the role of memory, and the potential for omni-models. Finally, they touch on successful applications of AI agents, such as customer support and research-style agents, and the evolving UX around these technologies.