Shifting the work of context engineering to the AI platform

What this post added

This post details the evolution of context engineering within Glean's AI platform, shifting the burden from individual engineers to a unified, continuously learned, and consistently delivered platform capability. It introduces specialized indexes for different data types (calendar, documents, structured data), emphasizes the importance of connectors and data modeling for heterogeneous enterprise data, and highlights the role of knowledge graphs for multi-hop reasoning. The post also describes advancements in tool search and enterprise memory for optimizing agent actions, including storing and learning from agent run traces to improve tool selection, parameterization, and action sequencing.