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# Make Your AI Agents Predictable With Graph Engineering - Zachary Blumenfeld, Neo4j

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 20 min read · 0 upvotes · 0 comments

## Summary

Graph engineering is a new name for an old idea: give agents a map of the logic they should follow, not just a pile of text. Zachary Blumenfeld of Neo4j explains how graphs make agents more predictable, governable and portable. He shows how to compile skills and past decision traces into graph-shaped skills, then demos a loan-approval agent that follows them.

0:00 Intro: what is graph engineering?
1:42 Three types of graphs
3:04 Predictability, governance, portability
4:42 Neo4j's knowledge layer
6:03 Context graphs and agent memory
6:53 Compiling skills into graphs
8:08 Distilling skills from memory graphs
9:22 Customer use cases
10:47 Demo: a loan-approval agent
14:30 Compiling runs into a graph-shaped skill
18:20 Observability on graph branches
20:01 Resources

Zachary Blumenfeld, Neo4j: https://neo4j.com

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## Full article

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