Build AI Agents with Intelligent MCP Workflows

Tuesday, July 29
9:00 a.m. IST | 11:30 a.m. SGT/HKT/CST | 12:30 p.m. JST | 1:30 p.m. AEST
30 Minutes

Hallucinations and inconsistencies make an AI agent unreliable. AI-ready data provides context that helps an agent more reliably plan, reason, and carry out tasks. You can combine a knowledge graph with RAG to create a GraphRAG architecture to make your data AI ready and provide the context it needs.

Where does this data come from?

The Model Context Protocol (MCP) serves as a single open protocol that seamlessly connects your LLM to external data sources, data APIs, infrastructure, and tools. The context you need to make your agent reliable flows in through MCP.

Join us on July 29th for a 30-minute tutorial on how to build reliable Agentic AI using intelligent MCP for customer 360, HR intelligence, enterprise search, and other use cases.

You’ll learn how to:
  • Overcome implementation obstacles, such as using federated tool access alone
  • Improve tool and data access for agents
  • Provide context for agents to use and chain tools correctly with GraphRAG
Register ASAP. You’ll receive access to an on-demand recording of the event whether you attend or not.


SPEAKERS

Michael Hunger Image

Michael Hunger
Head of Product Innovation, Neo4j

Michael Hunger has been passionate about software development for more than 30 years. For the last 12 years, he has been working on the open source Neo4j graph database filling many roles, most recently leading the Developer Relations Neo4j team.
Zach Blumenfeld Image

Zach Blumenfeld
AI/ML Product Specialist, Neo4j

Zach is an AI/ML graph enthusiast who helps engineers, data scientists, and business leaders leverage graph technology for analytics and AI applications. His expertise spans several dynamic fields, including criminal fraud detection, identity resolution, and recommendation systems.

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