Apr 24
Loading video player...
Florian Juengermann is the co-founder and CTO of Listen, an AI startup that turns qualitative research across hundreds of interviews, surveys, and focus groups into structured, traceable insights. Listen's agents analyze responses at scale, and Florian has rearchitected the system multiple times to get there. In this conversation, he walks through the virtual table architecture at the core of their Research Agent, how small models run map-reduce classification across thousands of open-ended responses, and the self-reviewing feedback subagent that catches errors during long async runs. We also discuss: • The three agents inside Listen's platform • How Listen rearchitected from a simple RAG bot to a multi-agent system multiple times • Why the PowerPoint subagent was completely rebuilt using Claude's code SDK • Contextual prompt engineering as an alternative to skills • How Listen keeps report numbers live as new interview responses come in • When to trigger the long-running agent vs. showing early results • What Florian looks for when hiring agent engineers References: • Anthropic: https://www.anthropic.com/ • ChatGPT: https://chatgpt.com/ • Claude: https://claude.ai/ • Claude Code SDK: https://docs.anthropic.com/en/docs/claude-code/sdk • E2B: https://e2b.dev/ • Emotional Intelligence: https://listenlabs.ai/features/emotional-intelligence • GPT Mini: https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/ • Haiku: https://www.anthropic.com/claude/haiku • Listen: https://listenlabs.ai/ • OpenAI: https://openai.com/ • Pandas: https://pandas.pydata.org/ • Postgres: https://www.postgresql.org/ • Python: https://www.python.org/ • Research Agent: https://listenlabs.ai/features/research-agent • Render: https://render.com/ • Zoom: https://zoom.us/ Where to find Florian: • LinkedIn: https://www.linkedin.com/in/juengermann/ • Twitter/X: https://x.com/florian_jue Where to find Harrison: • LinkedIn: https://www.linkedin.com/in/harrison-chase-961287118/ • Twitter/X: https://x.com/hwchase17 Where to find LangChain: • Website: http://langchain.com • Docs: https://docs.langchain.com/ Send feedback or questions to maxagency@langchain.dev Timestamps: 00:00 Introduction 01:25 The three agents inside Listen's platform 03:15 Live chat vs. long async runs, and how Listen tunes for each 05:33 Under the hood of the Research Agent 06:37 Listen's virtual table architecture 07:34 How small models classify thousands of open-ended responses 10:05 Running code in a sandbox: how E2B fits in 11:52 Why Listen rebuilt the PowerPoint subagent from scratch 14:11 Contextual prompt engineering instead of skills 16:32 The feedback subagent that reviews its own reports 18:14 How Listen runs evals in production 19:47 Unexpected ways users push the agent to its limits 21:42 How many times Listen has rearchitected, and why 24:59 Trace observability: depth over breadth 26:10 Lessons from running Claude Code SDK inside E2B 27:42 Memory: what's solved and what isn't 29:10 The Composer agent UX: co-editing a document with AI 35:50 How Listen keeps report numbers live as new responses come in 43:47 What Listen looks for when hiring agent engineers