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What’s next for AI agents, and how will they change the way we work? In this conversation, Stanislas Polu (CEO of Dust, formerly research at OpenAI) and Harrison Chase (CEO of LangChain, one of the most influential open-source AI frameworks) unpack the current state and future of AI agents. They reflect on their early conversations in the pre-ChatGPT days, how the landscape has evolved, and where it's headed next.
Stan and Harrison share lessons from building today’s agent infrastructure—from chat interfaces to the future of ambient, autonomous systems—and discuss the challenges of operating in the chaotic "fog of AI." We dig into the open questions, early insights, and messy realities of building in today’s fast-moving AI landscape.
In this conversation, we explore:
What sparked Stan and Harrison’s early interest in LLMs
The pre-ChatGPT era and how the AI landscape has evolved since late 2022
High-leverage use cases for agents inside Dust and LangChain today
The critical differences between AI workflows and true agents—and why agents may unlock more powerful, long-term solutions
Why reliability is the main blocker to ambient agents
Real-world enterprise use cases for AI agents across customer support, sales, and engineering
How to build in the “fog of AI” and the challenge of maintaining product vision when foundations shift every six months
Strategies for creating defensibility in a world where tech giants can quickly replicate features
The future of multi-agent systems and how they could transform enterprise productivity
The current state of the AI talent market
And much more
Explore the episode
Timestamps
(00:00) Intro
(02:33) Brief overviews of Dust and LangChain
(03:30) The early days of LLM product development
(11:02) Harrison's journey to founding LangChain
(14:35) Dust's evolution and focus on enterprise productivity
(17:15) Tobi’s AI memo
(18:42) An overview of AI agents and how they differ from AI workflows
(26:43) High-leverage use cases for agents at Dust and LangChain
(30:41) How to interact with agents and an explanation of ambient agents
(36:21) What the future of agents may look like
(40:52) Current limitations of AI agents and reliability challenges
(45:40) Will we converge to one agent or many specialized ones?
(51:32) How to solve the sycophant problem
(56:04) The challenges of building AI companies in a rapidly changing landscape
(01:03:06) Recent AI talent acquisitions and market dynamics
(01:05:28) How Dust and LangChain attract talent
(01:09:12) How far off AGI may be
(01:12:52) Final meditations
Follow Stanislas Polu
LinkedIn: https://www.linkedin.com/in/spolu/
Follow Harrison Chase
LinkedIn: https://www.linkedin.com/in/harrison-chase-961287118/
Resources and episode mentions
Books
Permutation City: https://www.amazon.com/Permutation-City-Novel-Greg-Egan/dp/1597805394
Range: Why Generalists Triumph in a Specialized World: https://www.amazon.com/Range-Generalists-Triumph-Specialized-World/dp/0735214484
People
Anton Troynikov on LinkedIn: https://www.linkedin.com/in/antontroynikov/
Gabriel Hubert on LinkedIn: https://www.linkedin.com/in/gabhubert/
Andrew Ng on LinkedIn: https://www.linkedin.com/in/andrewyng/
Other resources
Dust: https://dust.tt/
LangChain: https://www.langchain.com/
OpenAI: https://openai.com/
Stable Diffusion: https://stablediffusionweb.com/
CharacterAI: https://character.ai/
LangSmith: https://www.langchain.com/langsmith
Tobias Lütke’s memo reposted on X: https://x.com/tobi/status/1909251946235437514
ReAct: Synergizing Reasoning and Acting in Language Models: https://arxiv.org/abs/2210.03629
Introducing ambient agents: https://blog.langchain.com/introducing-ambient-agents/
Claude task master: https://github.com/eyaltoledano/claude-task-master
Google Jules: https://jules.google/
Stripe: https://stripe.com/
Windsurf: https://windsurf.com/
Cognition: https://cognition.ai/
Scale: https://scale.com/
Inflection: https://inflection.ai/
Adept: https://www.adept.ai/
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