AI agents need memory to maintain context across sessions, learn from experience, and handle long-running tasks. The challenge? Deciding what to remember, where to store it, and how to retrieve it when it matters. In this workshop, you'll learn a practical framework for architecting memory systems that actually work in production. We'll cover:
- Types of memory in agentic systems
- Storage patterns: Where to persist memories and how to structure them for retrieval
- Retrieval strategies: Combining vector search with metadata, recency, and other signals
- Memory lifecycle: When to create, update, or prune memories to keep your system performant
You'll apply this framework by building memory into an AI agent and seeing how different design choices impact behavior.
