No more guessing for your #AI: Today, we are announcing Neo4j Context Engine. Neo4j Context Engine enables enterprises to define, connect, govern, and continuously enrich business and data context, making it available to AI agents at the point of decision and action. Through our six-layer interconnected ontology, agents can give governed, reliable, and explainable answers. And the more you use it, the better it gets. Neo4j Context Engine is currently in private preview. Follow the link below to become an early design partner; bit.ly/4zyWC3a

Instead of adding a message broker next to your database, Julian Busch lets the graph write it itself: Change Data Capture turns a task moving from PENDING to ACTIVE into the signal that wakes the next agent. State, handoffs and the audit trail stay connected in a single graph and even failed events become nodes you can triage with Cypher. Look: bit.ly/4BvE3Pk

#NODES2026 is THE graph + AI online conference you must attend. Through live sessions, you’ll see how teams are designing #AI that can reason better, work with agents, and run in production. Join 100+ speakers from around the globe, hear about context engineering with Andrew Ng and Emil's keynote, learn about our products from Sudhir Hasbe, and ask questions to Emil Eifrem during our live Q&A. All in 24 hours :) Register: lnkd.in/ebfSsy5J

🕵️‍♂️ The case: You are a compliance team member at a major financial institution, and you received a stack of onboarding paperwork for a new corporate client. But...one line in the loan application looks suspicious. In this Demo by Corydon, you'll turn 20 files into a graph without writing an extraction rule with Document Intelligence. Then Cypher and Louvain community detection do the rest. Find the Ultimate Beneficial Owner in a Pile of KYC Documents with Document Intelligence and send your feedback! bit.ly/4i9z2D2

Michael Hunger, @steveonjava, and @BarrasaDV will soon release a book, "GraphRAG: the definitive guide," but we are excited to share that an early release is now available for you to download for FREE. Start today, gaining the skills to integrate #GraphRAG into your AI stack: bit.ly/4AcR3In

Have you received an Aura activation or coupon code at an event? Take a look at this guide and redeem your credits! It's time to build!🚀 bit.ly/4yDx7gn

What graph embeddings capture? a node and everything around it, compressed into a vector. Fraud teams can flag accounts that look like known fraudsters, and retailers can recommend what similar customers bought. Getting started used to mean choosing models and tuning parameters. Mahdi Karabiben’s post introduces a new Embeddings API for Aura Graph Analytics, now in preview. It’s one line: gds.embedding.create(G) ✨ It’s available on the AuraDB Free tier, so you can try it at no cost. Check it out - feedback is expected! bit.ly/4yFlNAt

Learn to automate with the Aura CLI !bit.ly/4sigeGD psst: As one of the first 500 participants to complete this course, you are eligible to receive $100 in free Aura credits to use on Neo4j Aura - check out the details in the link above!

Which skill is your biggest gap right now? @AndrewYNg's team analyzed 10,000+ job postings to identify the skills that matter. Hear about what they found in the fireside chat with @emileifrem during #NODES2026. Join! The coolest online 24-hour conference with 100 speakers from around the globe. Join for free! bit.ly/4pXd3Tq

“In fraud detection, knowledge gives us the context to help understand not only what’s going on currently, but also how an entity has evolved their business transactions historically. That will reveal signals of fraud, waste, and abuse within the current data. That’s important because we’re trying to detect fraud proactively.” Thomas Luu, Director, Anti-Counterfeiting Team, Gilead Sciences Just released: The MIT Technology Review Insights survey of 300 senior data and technology executives to find out why only approximately 34% of agentic AI projects reach production. The answer isn’t missing data but the missing knowledge. The report you needed is here. Download today: bit.ly/4822whw #Neo4j #MIT #AIAgents #knowledgelayer #contextengineering

Vector Graph or GraphRAG? In a study by the UK’s National Innovation Centre for Data, GraphRAG scored roughly 80% higher on truthfulness than vector-only RAG. It answered 65.3% of 510 complex questions, against 28.9% for vector-only. It doesn’t replace vector search, though. For single-fact lookups and semantic matching, vector RAG is still the right tool. The blog shows how to tell which one you need: look at how your retrieval is failing, build a test set from your real traffic, and see whether GraphRAG moves the numbers. Happy weekend reading! bit.ly/4ynCCzu

The Connected Startup, Episode 5: lcmd xitter.cf/i/broadcasts/1OxwbnyyY…

Road to NODES 2026 - Build Your First Knowledge Layer xitter.cf/i/broadcasts/1RKjpbnMN…