This episode explores knowledge graphs and contextual artificial intelligence for robotics and agentic workflows. Jordi Spranger of Spranic, founder and chief executive officer, appears on the Neo4j AI Luminary Series to discuss knowledge graphs, semantic maps and physical AI. Spranger explains how semantic maps and knowledge graphs supply the contextual intelligence robots and agentic systems need to operate reliably in unstructured environments. They emphasize graph grounding, provenance and the transition from constrained factory settings to free-roaming real-world deployments. The episode is produced by theCUBE Research with hosts John Furrier and Gabe Olave guiding the discussion. Spranger states that graphs provide the metadata and provenance that ground AI and reduce hallucinations, enabling safer, auditable decision-making for digital agents and physical robots. They recommend beginning knowledge-layer work at the proof of concept level and prioritizing open standards and connectivity with existing platforms such as Databricks and Snowflake. This conversation highlights graph grounding, provenance, knowledge layers and data infrastructure considerations for deploying autonomous systems in complex real-world environments.