Build reliable AI with Aura Agents

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

0:07 – What is a knowledge graph?
0:33 – Introduction to Aura Agent capabilities
1:34 – HR talent agent use case walkthrough
2:40 – Demonstration of failed results using the standard vector RAG
3:04 – Fixing AI hallucinations using Neo4j and Aura Agent
3:39 – Achieving accurate results with graph-grounded agent tools
4:10 – Advanced multihop pattern matching and extending data models
4:42 – Comparison: Knowledge graphs vs. relational databases
6:53 – Resources for getting started with Aura Agent