PERSISTENT MEMORY MANAGEMENT FOR STATEFUL AI CONVERSATIONS IN PRODUCTION SYSTEMS

Abstract

The widespread deployment of Large Language Models (LLMs) in enterprise and customer service role has necessitated the creation of interactive systems that maintain history or context for several conversations. It is imperative that there is retention of conversation context within an AI conversational agent, which can store that history and use it as new conversations are being held while at still promoting personalization and privacy compliance. Memory in context-aware systems in operational environments has to strike a distinct balance between ephemerality of memory supplied in the form of context, stability over time of preference changes of users, memory for retrieval-based tasks, and real-time requirements. This article gives an overview of the most sophisticated strategies to ensure that conversational AI systems can handle more complex dialog tasks capitalizing on knowledge-based memory enhancement, namely, vector memory, knowledge graphs, RAG (retrieval-augmented generation), memory networks and more distributed memory. Also, it reviews some of the key challenges pertaining to memory coherence and history recall, security and data policies, as well as, performance degradation arising from excessively long queues. The work presented reveals the present status of development of smart memory management modules, the applications of which concern self-defensive memory concepts, the notion of pruning memory actively and the automation of both the learning mechanisms and the intent switching in a dialogue. The findings indicate clearly that the adoption of intelligent memory management and architecting techniques require the development of both long term strategies that are aimed at enabling the operationalization of the present security technology and short term efforts that concentrate on operational enhancement until 2026.