Architecting Agent Memory: Principles, Patterns, and Best Practices — Richmond Alake, MongoDB
Jun 27, 2025 · 17:37
Richmond Alake from MongoDB presents memory management as the key pillar for building believable, capable, and reliable AI agents, arguing that agentic systems require structured memory types—persona, toolbox, conversational, workflow, episodic, and entity—to achieve statefulness and reduce reliance on prompt engineering. He introduces MemoRiz, an open-source library implementing these memory design patterns, and positions MongoDB as a flexible memory provider with its document model and hybrid retrieval capabilities (vector, text, graph). Alake details practical patterns: storing tool schemas for scalable tool use, persisting conversation history with timestamps and recall signals, leveraging workflow memory to learn from failures, and using MongoDB's upcoming integration of Voyage AI embedding models to simplify chunking and retrieval. He connects these advances to neuroscience, citing how feline visual cortex research inspired CNNs and noting recent collaborations with neuroscientists to further agent memory research.
