Giving AI Agents Memory That Learns — Jake Broekhuizen, LangChain

This post details how to imbue AI agents with learning memory by moving beyond simple observability and logs. It categorizes memory into semantic, episodic, and procedural types, and distinguishes between working and long-term memory. A core contribution is the 'read-write cycle' for memory updates: agents read context, execute tasks, filter evidence for actionable signals, and then write updated context back. Practical lessons are shared on trace retention, caching strategies for the hot path, and the importance of human review for critical procedural memory changes. The talk uses a financial services agent example to illustrate how memory improvements can fix behavioral drift.