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, so users repeat themselves and abandon the product. Three types of memory loss affect every agent. Memory decay: tools never store what they learn, so preferences vanish between turns. No structured profile: even with state, the agent has no way to build or query one. Memory overload: as memory grows to dozens of sections, dumping everything wastes tokens. This talk covers three progressive fixes that build on each other: persistent state with agent.state and FileSessionManager so preferences survive sessions, the Core Memory Pattern (MIRIX, MemGPT) that gives the agent tools to manage its own memory, and semantic retrieval that loads only relevant sections per query for 60-98% fewer tokens. Each comes with a live demo and real metrics. You'll leave with a decision framework for which pattern fits which use case, plus open-source code for any domain.

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