How do AI Agents solve the memory problem in multi-turn conversations

AI Agents address the multi-turn conversation memory challenge through short-term memory buffers designed to retain and utilize relevant context. This mechanism enables them to track the ongoing dialogue flow.

These agents primarily use context windows within their language models to hold recent interactions. Token limits constrain the memory's raw length, prompting strategies like truncating the oldest tokens first or employing more sophisticated summarization techniques. Techniques such as summarizing previous exchanges into concise statements and selectively retaining key information (e.g., user preferences, named entities) optimize memory usage. Session persistence mechanisms also store essential context beyond a single interaction window where applicable.

To maintain coherence and efficiency, agents actively manage context windows by applying truncation strategies when token limits approach. They create relevant summaries of past discussions to feed forward critical context into the new prompt. Finally, strategic embedding or storage of vital long-term facts from the conversation history ensures continuity, enabling more natural and consistent multi-turn interactions despite the inherent constraints of model context windows.