What everyone is getting wrong about context graphs

The concept of context graphs is explored as a coordination layer that enables sophisticated data selection and integration across various systems to provide relevant, timely, and user-specific information. Unlike traditional knowledge graphs, which prioritize correctness and durability, context graphs focus on operational constraints such as timeliness, audience-specific information, cost-efficiency, and traceability, making them vital for real-time applications. The article explains the practical implementation of context graphs, detailing how they manage data from various sources through live API calls, local caches, and derived contexts, to provide accurate and efficient responses while maintaining provenance and permission integrity. This approach ensures that every user request is met with disciplined data selection, balancing latency, cost, and trust to create a flexible and reliable system. The importance of provenance and traceability is emphasized for debugging, auditing, and maintaining trust, as it helps in understanding the origins and transformations of the data used in responses.