Training Data

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

Jun 18, 2024

Harrison Chase of LangChain focuses on enabling the orchestration layer for AI agents. He discusses the evolution of agents, impact on work and creativity, challenges in AI models, and solutions in LangChain. The conversation explores the role of cognitive architectures, fine tuning, and leadership reflections in the AI field.

49:50

Episode guests

Harrison Chase

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Highlights

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Podcast summary created with Snipd AI

Quick takeaways

Agents in AI shift towards LLM-led control of application flow, breaking from preset steps.

LangChain's orchestration layer empowers agents to balance autonomy and structured decision-making effectively.

Deep dives

Definition of Agents in AI

Agents in AI are defined as instances where a large language model (LLM) takes charge of controlling the application's flow, deviating from the conventional preset steps to letting the LLM decide the sequence of actions.

The Dominance of Chat as a UX

00:49

Embrace the Spectrum of AI Reasoning

00:59

Importance of Observability and Pairwise Testing in Complex Applications

01:55

LLMs as Agents in Control Flow

01:46

Intro

4min

Evolution of LangChain and the Role of Autonomous Agents

17min

Exploring the Impacts of Autonomous Agents and AI on Work and Creativity

5min

Evolution of Cognitive Architectures and Challenges in AI Models

13min

Fine Tuning, Cognitive Architectures, and Solutions in LangChain

7min

Admiration for Key Figures and Leadership Reflections in AI Field

4min

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

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Training Data

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

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Definition of Agents in AI

Interaction between Decision-Making and Action-Taking in Agents

Distinguishing Features of Agents in Control Flow

Langchain's Role in Agent Ecosystem

The Dominance of Chat as a UX

Embrace the Spectrum of AI Reasoning

Importance of Observability and Pairwise Testing in Complex Applications

LLMs as Agents in Control Flow

Remember Everything You Learn from Podcasts