49 min

LangChain’s Harrison Chase on Building the Orchestration Layer for AI Agents Training Data

    • Technology

Last year, AutoGPT and Baby AGI captured our imaginations—agents quickly became the buzzword of the day…and then things went quiet. AutoGPT and Baby AGI may have marked a peak in the hype cycle, but this year has seen a wave of agentic breakouts on the product side, from Klarna’s customer support AI to Cognition’s Devin, etc.

Harrison Chase of LangChain is focused on enabling the orchestration layer for agents. In this conversation, he explains what’s changed that’s allowing agents to improve performance and find traction. 

Harrison shares what he’s optimistic about, where he sees promise for agents vs. what he thinks will be trained into models themselves, and discusses novel kinds of UX that he imagines might transform how we experience agents in the future.     

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned: 


ReAct: Synergizing Reasoning and Acting in Language Models, the first cognitive architecture for agents


SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent from researchers at Princeton


Devin, autonomous software engineering from Cognition


V0: Generative UI agent from Vercel


GPT Researcher, a research agent 


Language Model Cascades: 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain


Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/

00:00 Introduction
01:21 What are agents? 
05:00 What is LangChain’s role in the agent ecosystem?
11:13 What is a cognitive architecture? 
13:20 Is bespoke and hard coded the way the world is going, or a stop gap?
18:48 Focus on what makes your beer taste better
20:37 So what? 
22:20 Where are agents getting traction?
25:35 Reflection, chain of thought, other techniques?
30:42 UX can influence the effectiveness of the architecture
35:30 What’s out of scope?
38:04 Fine tuning vs prompting?
42:17 Existing observability tools for LLMs vs needing a new architecture/approach
45:38 Lightning round

Last year, AutoGPT and Baby AGI captured our imaginations—agents quickly became the buzzword of the day…and then things went quiet. AutoGPT and Baby AGI may have marked a peak in the hype cycle, but this year has seen a wave of agentic breakouts on the product side, from Klarna’s customer support AI to Cognition’s Devin, etc.

Harrison Chase of LangChain is focused on enabling the orchestration layer for agents. In this conversation, he explains what’s changed that’s allowing agents to improve performance and find traction. 

Harrison shares what he’s optimistic about, where he sees promise for agents vs. what he thinks will be trained into models themselves, and discusses novel kinds of UX that he imagines might transform how we experience agents in the future.     

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned: 


ReAct: Synergizing Reasoning and Acting in Language Models, the first cognitive architecture for agents


SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent from researchers at Princeton


Devin, autonomous software engineering from Cognition


V0: Generative UI agent from Vercel


GPT Researcher, a research agent 


Language Model Cascades: 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain


Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/

00:00 Introduction
01:21 What are agents? 
05:00 What is LangChain’s role in the agent ecosystem?
11:13 What is a cognitive architecture? 
13:20 Is bespoke and hard coded the way the world is going, or a stop gap?
18:48 Focus on what makes your beer taste better
20:37 So what? 
22:20 Where are agents getting traction?
25:35 Reflection, chain of thought, other techniques?
30:42 UX can influence the effectiveness of the architecture
35:30 What’s out of scope?
38:04 Fine tuning vs prompting?
42:17 Existing observability tools for LLMs vs needing a new architecture/approach
45:38 Lightning round

49 min

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