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Bio, Work & Ideas

Ryan Knight

Conference affiliation: Neo4j · 2026

Ryan Knight is a Senior Partner Architect at Neo4j, building enterprise AI agents that use knowledge graphs to understand relationships across organizational data and retain context between interactions. His work with AWS, Databricks, Microsoft’s agent framework, and GitHub Copilot makes graph intelligence accessible within existing enterprise platforms.

Knight entered Java consulting at the Sun Java Center in 1999 and subsequently held architecture and advisory roles involving DataStax, Lightbend, Starbucks, and Capital One. By 2017, he was an enterprise architect at Starbucks. He later led Grand Cloud, a consultancy delivering cloud, data, and generative-AI systems for organizations ranging from startups to Fortune 500 companies.

At Neo4j, he works with cloud and data-platform partners on knowledge-graph-grounded enterprise agents. His projects address several specific architectural problems:

  • Connected context beyond similarity search: His co-authored Microsoft Agent Framework integration combines vector retrieval with graph traversal, allowing agents to follow relationships among companies, products, geographic exposures, and risk factors.
  • Persistent agent memory across sessions: The same integration retrieves relevant conversational history before model execution and stores messages, extracted entities, user preferences, and reasoning traces afterward, giving agents continuity across interactions.
  • Graph intelligence inside the lakehouse: His Databricks fraud-investigation work makes graph-derived account scores and fraud-community signals available to Databricks Genie as queryable lakehouse dimensions.
  • Agent-ready access to real graph schemas: A GitHub Copilot integration uses Neo4j’s Model Context Protocol tooling to inspect a database schema and generate Python code grounded in its actual nodes and relationships.

At AI Engineer World’s Fair 2026, Knight joined Zach Blumenfeld and Ben Squire for a hands-on lakehouse workshop on applying graph-shaped context to structured warehouse records and unstructured documents. His role centered on helping participants put those shared techniques into practice.

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1 conference talk

Key ideas

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A repair copilot needs more than matching passages and valid SQL. Warehouse connections, navigable documents, and link communities give it different ways to assemble the context a question requires.

  • The repair history and the manual are only the beginning
    2:08 ↗
  • A graph, an agent, and a runnable workspace
    7:37 ↗
  • Connections: use a graph to choose the SQL
    21:57 ↗
  • When metadata is enough—and when to import records
    34:26 ↗
  • Table of Contents: preserve the structure documents already have
    43:40 ↗
  • Build an outline that can stop at the right depth
    52:48 ↗
  • Keep the vocabulary useful to the query author
    1:03:09 ↗
  • Search the text, then restrict it to a subtree
    1:08:47 ↗
  • Names, freshness, and references are part of retrieval quality
    1:14:38 ↗
  • Themes: discover groups through document links
    1:21:43 ↗
  • Community descriptions, changing data, and trust
    1:33:23 ↗
  • From a diagnostic code to a repair recommendation
    1:39:19 ↗
  • Find the mismatch between the library and the field
    1:45:31 ↗
  • Theme coverage and the join the agent still performs
    1:52:10 ↗

References