Architecting Agent Memory: Principles, Patterns, and Best Practices — Richmond Alake, MongoDB

In the rapidly evolving landscape of agentic systems, memory management has emerged as a key pillar for building intelligent, context-aware AI Agents. Inspired by the complexity of human memory systems—such as episodic, working, semantic, and procedural memory—this talk unpacks how AI agents can achieve believability, reliability, and capability by retaining and reasoning over past experiences. We’ll begin by establishing a conceptual framework based on real-world implementations from memory management libraries and system architectures: Memory Components representing various structured memory types (e.g., conversation, workflow, episodic, persona) Memory Modes reflecting operational strategies for short-term, long-term, and dynamic memory handling Next, the talk transitions to practical implementation patterns critical for effective memory lifecycle management: Maintaining rich conversation history and contextual awareness Persistence strategies leveraging vector databases and hybrid search Memory augmentation using embeddings, relevance scoring, and semantic retrieval Production-ready practices for scaling memory in multi-agent ecosystems We’ll also examine advanced memory strategies within agentic systems: Memory cascading and selective deletion Integration of tool use and persona memory Optimizing performance around memory retrieval and LLM context window limits Whether you're developing autonomous agents, chatbots, or complex workflow orchestration systems, this talk offers knowledge and tactical insights for building AI that can remember, adapt, and improve over time. This session is ideal for: AI engineers and agent framework developers Architects designing Agentic RAG or multi-agent systems Practitioners building contextual, personalized AI experiences By the end of the session, you’ll understand how to leverage memory as a strategic asset in agentic design—and walk away ready to build agents that not only act and reason but also remember. ---related links--- https://www.linkedin.com/in/richmondalake/