Building Smarter AI with Context Graphs — Ben Roodman

Ben Roodman shares why he’s so excited about the growing community around context graphs. For Ben, one of the biggest challenges with traditional CRM and transactional data is that it doesn’t really capture relationships—the people involved, why decisions were made, or what happened afterward. A context graph brings all of that together, giving us a much richer picture of how people, decisions, and outcomes connect.

Ben also dives into what the next generation of AI agents will need to actually be useful. It’s not just about giving agents more data or a perfectly defined schema. They need memory, context, relationships, and the right signals to understand how organizations really work. That’s where Ben sees the potential of context graphs: helping AI understand the deeper connections within our systems and giving agents the context they need to make better, more informed decisions.

What really comes through in Ben’s conversation is how much there is still to learn—and how important it is to learn together. From startups to large enterprises, teams are figuring out how to put agentic AI, multi-agent systems, and context graphs into practice while keeping human relationships and judgment at the center. Ben talks about the energy of bringing this community together and hearing what others are building. There are plenty of ideas to explore, and he’s clearly ready to keep the conversation—and the learning—going.