Build AI Agents with Intelligent MCP Workflows

45 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 for a 45-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


SPEAKERS

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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