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As AI agents evolve beyond simple prompt–response interactions, they increasingly rely on connected data to reason effectively. This session explores how Aura Agents use knowledge graphs to ground agent workflows in structured relationships, improving context integration, reasoning transparency, and response consistency. You will see how Neo4j MCP tools allow agents to query graph data directly within their reasoning loop, transforming connected data into an active cognition layer. The session presents practical integration patterns for graph-native context and MCP-based tooling to help build more reliable, context-aware AI systems that move beyond stateless interactions.
What You Will Learn
How knowledge graphs enhance context integration and reasoning transparency for AI agents
How Neo4j MCP tools enable agents to query graph data within their reasoning loop
Practical patterns for integrating graph-native context into agent architectures
Who Should Attend
Developers building AI agents
Software architects designing AI-driven systems
Data engineers working with graph technologies
Engineers exploring context-aware and multi-step reasoning systems