I Built an AI Agent That Remembers Why Customers Leave (And I'm Building My Way Into AI Development)

Simbyheart — Mon, 06 Jul 2026 00:21:03 +0000

With over 5 years in customer support and retention, I've lost count of how many times I've seen the same pattern: a customer explains an issue, gets it "resolved," and then has to explain the same problem again weeks later, as if the first conversation never happened. Support systems forget. Customers don't.
That frustration, seen over years on the support side, is what led me to this hackathon project.
Most support systems and most AI chatbots treat every interaction as isolated. They don't remember. So patterns that should be obvious (repeated complaints, dropping usage, unresolved issues) never get connected until a customer just leaves.
That became the seed for my project: the Retention Risk Agent.
The Problem With "Forgetful" AI
Most AI tools answer questions in the moment, then forget everything. Ask a chatbot about a customer's history, and it only knows what's in that single message, not what happened last week, last month, or across five different support tickets.
For churn prediction, that's a fatal flaw. Churn isn't a single event. It's a pattern, a series of small signals that only make sense when viewed together over time. This is something I understand deeply from years of watching it happen firsthand.

Cognee is an open-source memory layer for AI agents. Instead of treating each interaction as isolated, it builds a knowledge graph, connecting facts, relationships, and context across everything you feed it. That's exactly what churn detection needed.

What I Built
I created a Python script that:

Ingests customer records (support tickets, usage patterns, plan changes)
Uses Cognee to build a memory graph connecting these signals
Asks a simple question: "Which customers show signs of churn risk, and why?"

The result wasn't a keyword match; it was reasoning. The agent correctly flagged a customer whose usage dropped 80% and who'd ignored two check-in emails. It flagged another who'd complained twice about slow support and mentioned a competitor. And critically, it correctly ignored healthy, engaged customers; it wasn't just flagging everyone; it was actually connecting patterns.

My Path Into This

I started learning to build with AI tools around mid-2025, coming from a customer support and retention background rather than a traditional coding one. I'm not a formally trained developer, but over the past year I've been steadily learning by building real projects with AI-assisted development, using tools like Claude to guide the technical implementation while I bring the domain understanding.
This project is the clearest example yet of that combination working: years of understanding why customers churn, paired with new technical ability to actually build something that detects it.

What I Learned

Memory is the missing piece in most AI applications people build today. Without it, every interaction starts from zero. With it, the same AI model can reason across time, spot patterns, and give context-aware answers that isolated queries never could.

I also learned that persistence matters, technically and personally. I hit rate limits, misconfigured environment files, and typos more times than I can count. Each one felt like a wall until it wasn't.

Check It out!

The full code is on GitHub: https://github.com/Simbyheart/Retention-Risk-Agent
Live demo on Replit: https://replit.com/@EmmyB/Python-Script-Runner

Built for the Cognee "Where's My Context?" hackathon, because agents shouldn't forget the people they're supposed to help.

DEV Community: Simbyheart

I Built an AI Agent That Remembers Why Customers Leave (And I'm Building My Way Into AI Development)