Your AI agent helps a user pick a premium option. Next interaction, it suggests the cheapest alternative. It forgot everything, preferences, history, instructions. Every conversation starts from zero. Users repeat themselves, get frustrated, and abandon the product. Three types of memory loss that affect every AI agent: 1. Memory decay: The agent forgets preferences between turns and sessions. Tools return results but never store what they learn from user actions. 2. No structured profile: Even with state, the agent has no mechanism to build, update, or query a structured user profile over time. It can't answer "what do I usually prefer?" 3. Memory overload: When memory grows to dozens of sections, dumping everything into context wastes tokens and degrades response quality. Loading 8 memory sections when only 1 is relevant. I will cover persistent state with agent.state and FileSessionManager so preferences survive across sessions, the Core Memory Pattern (MIRIX/MemGPT) that gives the agent tools to manage its own memory (read, write, update, list), semantic retrieval over memory that retrieves only relevant sections per query (60-98% fewer tokens), and how these three progressive patterns build on each other and apply to any agent domain. You'll walk away with: • Persistent state with cross-session survival in any agent framework • Core Memory tools (read/write/update/list) so agents manage their own profiles • Semantic search over memory sections using embeddings • A decision framework for which memory pattern fits which use case • Open-source code adaptable to e-commerce, support, education, healthcare, or any domain Most memory talks focus on RAG or vector databases for external knowledge. This focuses on agent self-memory, how agents remember users across conversations. The patterns are domain-agnostic and apply to any AI agent that needs personalization.

Outline: • Why Agents Forget • Fix 1: Persistent State • Fix 2: Core Memory Pattern • Fix 3: Semantic Memory Retrieval • Decision Framework + Resources