Build reliable AI with Aura Agents
Chapters 9
- Play from What is a knowledge graph?
- Play from Introduction to Aura Agent capabilities
- Play from HR talent agent use case walkthrough
- Play from Demonstration of failed results using the standard vector RAG
- Play from Fixing AI hallucinations using Neo4j and Aura Agent
- Play from Achieving accurate results with graph-grounded agent tools
- Play from Advanced multihop pattern matching and extending data models
- Play from Comparison: Knowledge graphs vs. relational databases
- Play from Resources for getting started with Aura Agent
Highlights:
-Knowledge Graph Fundamentals: Explanation of how nodes (people, places, things) and relationships (interactions) create interconnected data.
-The Problem with Standard RAG: Demonstrates how a standard agentic stack fails to accurately count specific talent or analyze distribution from PDF resumes.
-Aura Agent Workflow: Shows how to automate the extraction of entities from resumes into a Neo4j graph model to ground the agent with connected facts.
-Deployment and Tools: Features a low-code UI for drafting, testing, and deploying agents to secure REST and MCP endpoints.
-Graph vs. Relational Databases: A comparison highlighting the performance advantages of multi-hop querying and the flexibility to easily extend schemas without complex join tables
Resources mentioned
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