Unified storage solution for lifelong context engineering

Develop a unified storage architecture for lifelong context engineering that preserves as much user context as possible without loss, specifies the infrastructure or interfaces needed to record context to the maximum extent, and enables storage systems that simultaneously support high-compression storage, high-precision retrieval, and low-latency access at scale.

Background

The paper argues that as AI systems move toward lifelong context engineering—where an individual’s interactions, preferences, and task histories persist over long time horizons—the storage layer becomes a critical bottleneck. Existing approaches rely on fragmented mechanisms (e.g., ad hoc databases, caches, and logs) and fail to guarantee both completeness of retention and efficient access under real-world constraints.

Within the Lifelong Context Preservation and Update subsection, the authors explicitly state that a unified solution is currently lacking and pose concrete questions about how to design storage and interfaces that can retain maximal context, while also supporting high-compression, high-precision retrieval, and low-latency access at scale. Addressing this would provide the foundational memory substrate required for robust, long-horizon reasoning and stable agent behavior.