Context Engineering: Sessions, Memory
from Best AI papers explained Hosted by Enoch H. Kang
This whitepaper by Google titled **"Context Engineering: Sessions & Memory,"** authored by Kimberly Milam and Antonio Gulli in November 2025, which provides a detailed guide to building stateful, intelligent Large Language Model (LLM) agents. The document defines **Context Engineering** as the process of dynamically managing information within an LLM's context window, emphasizing two core, interconnected components: **Sessions** and **Memory**. **Sessions** manage the immediate, chronological dialogue and working state of a single conversation, while **Memory** is a decoupled system for long-term persistence, capturing and consolidating key information across multiple sessions to enable personalization. The paper extensively covers architectural considerations for both sessions (e.g., compaction strategies for managing long context) and memory (e.g., types of memory, storage architectures, and the LLM-driven process of extraction and consolidation), contrasting the dynamic, user-specific role of memory managers with the static, factual role of Retrieval-Augmented Generation (RAG) engines. Finally, it outlines critical production requirements, including **privacy**, **security**, and **asynchronous processing**, to ensure robust and efficient deployment of these state-aware agents.
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