This article explains how AI agents can evolve from reactive tools to personalized collaborators through context engineering. It covers the use of structured state objects to maintain long-term memory and adapt to user preferences, enhancing the overall interaction experience.

ReasoningBank introduces a memory framework that allows AI agents to learn from past interactions, enhancing their performance over time by distilling successful and failed experiences into generalizable reasoning strategies. It also presents memory-aware test-time scaling (MaTTS), which improves the agent's learning process by generating diverse experiences. This approach demonstrates significant improvements in effectiveness and efficiency across various benchmarks, establishing a new dimension for scaling agent capabilities.