How Listen is building a system of AI Agents & subagents for specialized tasks | Florian Juengermann, CTO

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
• ChatGPT
• Claude
• Claude Code SDK
• E2B
• Emotional Intelligence
• GPT Mini
• Haiku
• Listen
• OpenAI
• Pandas
• Postgres
• Python
• Research Agent
• Render
• Zoom

Where to find Florian:

• LinkedIn
• Twitter/X

Where to find Harrison:

• LinkedIn
• Twitter/X

Where to find LangChain:

• Website
• Docs

Send feedback or questions to [email protected]

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