Your AI agent handles complex workflows with multiple tools. But ask "What do I usually prefer?", blank stare. Tell it "Remember, I only want pet-friendly options", it acknowledges, then forgets by next session. It has no structured way to build a profile of who the user is. The problem: Even agents with persistent state store preferences implicitly, buried in conversation history or tool results. There's no mechanism for the agent to actively manage what it knows. It can't decide "this preference is important, I should save it" or "this user's diet changed, I should update the food section." Research shows this is solvable: MIRIX (2025) achieves SOTA 85.4% on the LOCOMO benchmark with structured memory types. I will cover the Core Memory Pattern (4 tools the agent uses to manage its own memory: read, write, update, list), memory sections inspired by MIRIX's 6 types (persona, preferences, history, instructions), autonomous memory management where the agent decides WHEN to store and WHAT to remember, memory evolution as preferences change over time, and cross-session persistence where memory survives restarts with FileSessionManager. You'll walk away with: • Core memory tool implementations (read, write, update, list) for any agent framework • Memory section design (persona, preferences, history, instructions) for your domain • Cross-session persistence so user profiles survive across sessions • MIRIX and MemGPT research concepts applied to practical, production code • Open-source code adaptable to any agent domain (support, e-commerce, education, healthcare) Most agent talks give the LLM a system prompt with user context. Core Memory flips this: the AGENT decides what to remember, like a human managing a personal notebook. The pattern is domain-agnostic and works in any agent framework with tool support.

Outline: • The Missing Memory • Core Memory Architecture • Scenario 1-2: Empty to Populated • Scenario 3-4: Evolution and Persistence • Production Patterns • Resources + Q&A