Agent Memory Architecture: How Our AI Remembers Across Sessions

Agent Memory Architecture: How Our AI Remembers Across Sessions The problem: LLMs are stateless. Every session is a blank slate. The challenge: Build agents that remember context across days, weeks, months—without hallucinating or losing continuity. The solution: A layered memory architecture that mirrors human cognition. Here's how we did it. The Core Problem Imagine waking up every morning with amnesia. You'd relearn your name, your job, your relationships—every single day. That's what running AI agents feels like without memory. Standard LLM sessions are ephemeral : Fresh context window each time No persistent state Conversation history lost on restart Zero continuity between sessions For a chatbot answering one-off questions? Fine. For an autonomous agent managing infrastructure, content, and operations? Catastrophic. Our Memory Stack We built a three-layer architecture inspired by human memory: 1. Working Memory (Session Context) What it is: The current conversation, active files,