Unlocking Context Engineering with Microsoft Foundry and Neo4j

Most AI agents lose context fast. In this session, you’ll learn how to connect Neo4j with your AI agent stack to give your assistants lasting, dynamic memory.

Through guided exercises, you’ll model relationships, store conversation history, and query relevant context to make your agent smarter over time. See how graph databases provide the missing layer of cognition that enables true personalization and persistent understanding.

From Declassified Documents to Knowledge Graph

Public UFO documents are complex, unstructured, and full of hidden relationships. This session uses them as a concrete case study to show how unstructured content can be transformed into a knowledge graph and connected to an AI agent for grounded, explainable answers.

We will move from raw documents to extracted entities, relationships, provenance, and uncertainty handling. Using Document Intelligence, we generate and inspect a graph model, explore it visually, query it, and finally connect it to an an Agent for natural-language interaction.

Building World-Aware Robots with Agent Memory and Context Graphs

What if robots didn’t just react - but actually understood their world and remembered it? In this session, we explore how to build world-aware, context-driven agents using a graph-based memory layer and real-world robotics.

You’ll see how users, objects, and environments can be modeled as a living digital twin, how interactions are captured as structured memory, and how agents reason over long-term context. Through a live demo, a robot recognizes returning users, recalls preferences, and adapts its behavior across sessions.

Context Graphs: Agent Memory as a Super Power

AI agents can follow prompts and use tools, but often lack the institutional context needed to explain why a decision is made. That reasoning: policies, precedents, and past outcomes are usually scattered across systems and human memory.
Context graphs capture this missing layer by modeling decision traces over time, including causality and context. By giving agents access to just enough historical and organizational knowledge, context graphs enable more explainable, consistent, and auditable decisions.

Why AI Agents Love Knowledge Graphs?

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.

Graphs + AI: Build Connected Intelligence with Neo4j

Discover how Graphs + AI can power more contextual, explainable, and connected applications. In this live, demo-driven session, see how Neo4j helps AI systems move beyond isolated prompts by connecting entities, relationships, conversations, and evidence.

We will show how Neo4j Document Intelligence turns documents into knowledge graphs, Neo4j Agent Memory Service gives agents persistent memory, and Neo4j Aura MCP connects AI agents directly with graph data.

Designed for developers, founders, and AI builders. No prior Neo4j experience is required.