Inside Toyota's Production System for Agents | Interrupt 26

Ravi Chandu Ummadisetti and Kordel France from Toyota's enterprise AI team explain how they built ToyotaGPT — a platform that reduced agent delivery from six months and six engineers to four days and one engineer, with over 50 agents now in production. Ravi walks through the architecture: dynamic LangGraph-based graph generation, auto-synthesized skills from unstructured data, and an MCP-compatible tool layer secured by TMNA's cybersecurity team from day one. Kordel then draws a striking parallel between TPS — Toyota's 100-year-old manufacturing philosophy — and LangChain's ecosystem, mapping Andon boards to LangSmith, Jidoka to LangGraph, and Genchi Gembutsu to trace-level debugging. Chapters: 0:00 Introduction and the 2023 AI chaos inside Toyota 1:40 Why one RAG app used to take 6 engineers and 6 months 2:09 Dynamic graph creation on LangGraph: 6 months becomes 4 days 2:47 The extraction problem: Toyota's brutal data reality 3:38 The ToyotaGPT stack: LangGraph, LangSmith, MCP, and TMNA security 4:18 Skills: units of intelligence generated from unstructured data 5:06 50+ agents in production — every one is a config file 5:39 GearPull: from hackathon idea to millions in manufacturing savings 6:37 R&D GPT: compressing years of paint research into seconds 7:25 KadyaGPT: AI embedded inside the designer's canvas 8:58 Kordel France: TPS meets LangChain 10:14 Andon board = LangSmith observability 11:47 Kaizen = continuous improvement at macro and micro scale 13:04 Jidoka = LangGraph's human-in-the-loop design 14:11 Genchi Gembutsu = LangSmith trace-level debugging 15:21 LangChain as the TPS for the AI era Resources: → LangGraph: https://www.langchain.com/langgraph → LangSmith: https://www.langchain.com/langsmith → LangChain Academy: https://academy.langchain.com