AI agents are moving beyond simple prompt–response interactions toward architectures that reason over connected data. This session explores how Aura Agents leverage knowledge graphs to ground agent workflows in structured relationships, improving context integration, reasoning transparency, and response consistency. You’ll also see how Neo4j MCP tools enable agents to query graph data directly within their reasoning loop, turning connected data into an active cognition layer. Key takeaways include practical patterns for integrating graph-native context, using MCP-based tooling with agents, and building more reliable, context-aware AI systems that evolve beyond stateless interactions.