Abstract: A computing system implements an interaction interface, a client, and the memory system comprising short-term memory, working memory, and long-term memory. The client is configured to receive a request including a message having natural language input from the interaction interface, compile the message into a contextual bundle, input the contextual bundle into the generative model to generate a response, and output the response. The message is appended to the short-term memory, the short-term memory is aggregated into the working memory, and the working memory is stored into the long-term memory based on predetermined criteria. The contextual bundle is compiled to include one or more relevant entries of the long-term memory.

Abstract: A computing system is provided, including one or more memory devices and one or more processing devices. In an initial interaction session, the one or more processing devices execute an initial instance of a scaffolded machine learning (ML) system that interacts with an initial user over a user interface. The scaffolded ML system includes scaffolding code configured to call one or more ML models. The one or more processing devices store, in the one or more memory devices, an ML system state of the scaffolded ML system during the initial interaction session. In each of a plurality of subsequent interaction sessions, the one or more processing devices retrieve the ML system state, initialize a subsequent instance of the scaffolded ML system with the ML system state, and execute the subsequent instance of the scaffolded ML system.