LLMs can write code, answer questions, and execute tasks, but they have one major limitation: they forget. Every new conversation starts from scratch unless we explicitly build systems that preserve context, state, and memory.

In this talk, we'll explore what it actually takes to give AI agents memory. Rather than focusing on prompts or models, we'll focus on the infrastructure layer that sits behind them. We'll examine common memory patterns used in agentic systems, including conversation history, session state, semantic caching, user preferences, and long-term knowledge retrieval.

Using Valkey as the foundation, we'll discuss design decisions, trade-offs, and operational challenges such as memory growth, retrieval latency, cache invalidation, and observability. We'll also look at how memory architectures impact both response quality and inference costs.

The goal is not to build another chatbot. The goal is to understand how stateful AI systems are engineered and why memory is becoming one of the most important infrastructure problems in modern AI.