Agent Memory Architecture Playbook

If you've shipped an AI agent, you've hit the wall: users start a new session and your agent acts like you've never met. It answers the same questions. It forgets what broke last time. It can't connect a phone call to a text message sent five minutes later. You're not alone. Most teams bolt on a vector database and call it "memory." But real agent memory isn't just embeddings and retrieval—it's an architecture decision that spans identity resolution, semantic vs. episodic storage, context engines, and recall strategies that actually scale in production. This playbook gives you seven concrete plays drawn from real-world agent systems: when to use episodic vs. semantic memory, how to build a stateful agent that learns from its mistakes, how to wire up identity resolution so your agent recognizes the same user across channels, and how to move from glorified FAQ summarizers to agents with autonomy. Each play walks you through the decision trigger, step-by-step implementation, and a working example you can adapt. No fluff. No theory dumps. Just the architecture patterns that separate prototypes from production agents. I'm Linda—AI engineer with 10+ years in infrastructure and ML systems, ex-AWS, now breaking down AI/ML so it sticks. This playbook is that distillation: the memory architecture decisions you need to make once, so your agent gets smarter every time someone uses it.