Mar 13
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The era of the simple AI wrapper is officially dead, and the entire software infrastructure layer is being completely rebuilt. Live from the Daytona COMPUTE Conference in San Francisco, Harrison Chase, co-founder and CEO of LangChain, joins the MAD Podcast to explain why this massive shift is happening. As agents evolve from simple prompt-based systems into software that can plan, use tools, write code, manage files, and remember things over time, the real frontier is shifting from the model itself to the stack around the model. In this conversation, we go deep under the hood of this new, post-cloud architecture to deconstruct harnesses, sub-agents, context compaction, observability, memory, and the critical need for secure compute sandboxes. For anyone building in AI today, this episode cuts through the noise to reveal the new infrastructure required to make autonomous agents work in the real world. Harrison Chase LinkedIn - https://www.linkedin.com/in/harrison-chase-961287118 X/Twitter - https://x.com/hwchase17 LangChain Website - https://www.langchain.com X/Twitter - https://x.com/LangChain Matt Turck (Managing Director) Blog - https://mattturck.com LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck FirstMark Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Listen on: Spotify - https://open.spotify.com/show/7yLATDSaFvgJG80ACcRJtq Apple - https://podcasts.apple.com/us/podcast/the-mad-podcast-with-matt-turck/id1686238724 00:00 Intro - meet Harrison Chase 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 The core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 Has MCP won? 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives