I Built an AI Agent That Remembers What Product Teams Already Tried

A developer built TRACE (Tracking Reactions, Actions, Consequences & Evolution), a product intelligence agent that treats customer problems as persistent entities rather than isolated review text. The system links customer feedback to product decisions, interventions and outcomes, storing them as historical memory so teams can ask whether a recurring problem was seen and fixed before. The project is open-sourced on GitHub with a hosted dashboard and demo video.

Most AI systems are pretty good at answering questions about what's happening right now. But product teams don't work only with what's happening right now. A problem reported today might have appeared six months ago. Someone may have already investigated it. The team may have shipped a fix. The fix might have worked for one group of users but failed for another. Then, months later, the same problem comes back. At that point, the useful question isn't just: "What are customers complaining about?" It's: "Have we seen this before, and what happened when we tried to fix it?" That's the idea behind TRACE . TRACE stands for: Tracking Reactions, Actions, Consequences & Evolution. It's a product intelligence agent designed to understand the lifecycle of customer problems over time. Instead of treating every customer review as an isolated piece of text, TRACE treats a problem as a persistent entity . The basic lifecycle looks like this: text Customer Feedback ↓ Problem ↓ Product Decision ↓ Intervention ↓ Outcome ↓ Historical Memory ↓ New Feedback ↓ Problem Recurrence / Evolution product: https://trace-sigma-two.vercel.app/ /dashboard video : https://youtu.be/cKWxDEiGZaY github : https://github.com/SahithiGh/Trace