Agent Memory Patterns: How Machines Remember and Forget

Modern AI systems don’t just respond, they remember. But what does “memory” actually mean for AI agents, and how should it be designed from the start? This foundational session introduces Agent Memory Systems, the architectural patterns that allow agents to retain context, preferences, and state across interactions. Designed for builders early in their agent journey, it reframes memory as a system design challenge, not simply a model feature. We’ll break down key memory types, including short-term context, session memory, and long-term recall, and examine how they differ in purpose, lifetime, and risk. The discussion will explore what agents should remember versus forget, how to prevent stale or incorrect recall, and how to structure identity and task memory responsibly. Attendees will leave with a practical mental model and design checklist for building intelligent, stateful agents that feel consistent, trustworthy, and production-ready. NOTE: Masterclass ticket required.