The pea protein vs. whey example really nails the core problem—traditional databases store outcomes but not the reasoning chain that led there. Context graphs sound like a game-changer for agent decision-making, especially the idea of making relationships explicit at the source instead of hoping RAG can pieece together implicit connections from scattered docs. The human-in-the-loop design tip is solid too—collecting rich decision data now (not just approve/reject) sets you up for autonomy later. I wonder how you'd handle conflicting contexts though, like when different stakeholders remember differnt reasons for the same decision.
The pea protein vs. whey example really nails the core problem—traditional databases store outcomes but not the reasoning chain that led there. Context graphs sound like a game-changer for agent decision-making, especially the idea of making relationships explicit at the source instead of hoping RAG can pieece together implicit connections from scattered docs. The human-in-the-loop design tip is solid too—collecting rich decision data now (not just approve/reject) sets you up for autonomy later. I wonder how you'd handle conflicting contexts though, like when different stakeholders remember differnt reasons for the same decision.
Thanks for the thought provoking question. I need to think over it.