GraphContext: Connecting the Dots with Graphs

AI systems need more than intelligence. They need context that is connected, structured, and durable. Without it, even strong models lose track of relationships, misinterpret intent, or produce answers that are difficult to explain. GraphContext puts graphs at the center of context engineering, turning fragmented data and memory into a coherent substrate for reasoning.

This talk explores how GraphContext enables reliable AI reasoning through connected memory, contextual retrieval, and graph-based knowledge representation. We will show how modeling context as a graph supports multi-hop reasoning, preserves provenance, and assembles only the most relevant context for a given task rather than overloading the model with unstructured text.

Attendees will leave with a clear understanding of how to design GraphContext pipelines that align AI systems with real-world knowledge and user intent, and why graph-driven context is becoming essential for building trustworthy, explainable AI beyond basic RAG approaches.