Memory and context management involves AI agents retaining, updating, and using relevant information during interactions to maintain coherent, personalized responses over time.
AI agents use various techniques like token windowing, embeddings, and external databases to store and retrieve past interactions. This enables agents to recall user preferences and conversation history dynamically, ensuring contextually relevant outputs without overwhelming processing limits.
Effective memory and context management improve user experience by making AI more consistent and personalized. It reduces redundant processing, lowering latency and infrastructure costs. For PMs, this translates to scalable solutions with enhanced business value through higher engagement and retention.