How We Build Pipelines to Detect Product Issues and Fix Them with Coding Agents

I’ll walk through how we built a pipeline that detects product issues from multiple data sources (session recordings, error logs, support tickets, LLM traces) and routes them to coding agents that generate PRs.

• Step 1: Signal. Render videos from session recordings, transcribe them, and analyze, searching for issues.
• Step 2: Cluster. Group signals from different sources and decide what’s worth fixing.
• Step 3: Code. Preview of Twig ADE (AI Development Environment), where confirmed tasks get handed off to coding agents.