Architecting Agent Memory - Principles, Patterns, and Best Practices

Explore the fundamental principles and practical implementation of memory management in AI agent systems through this 18-minute conference talk. Discover how to build intelligent, context-aware AI agents by drawing inspiration from human memory systems including episodic, working, semantic, and procedural memory types. Learn to establish a conceptual framework based on real-world implementations, covering memory components that represent structured memory types such as conversation, workflow, episodic, and persona memory, alongside memory modes that reflect operational strategies for short-term, long-term, and dynamic memory handling. Master practical implementation patterns for effective memory lifecycle management, including maintaining rich conversation history and contextual awareness, implementing persistence strategies with vector databases and hybrid search, and applying memory augmentation through embeddings, relevance scoring, and semantic retrieval. Gain insights into production-ready practices for scaling memory in multi-agent ecosystems and examine advanced memory strategies including memory cascading, selective deletion, tool use integration, and persona memory optimization. Understand how to optimize performance around memory retrieval and LLM context window limits while building autonomous agents, chatbots, and complex workflow orchestration systems that can remember, adapt, and improve over time.