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Introducing Sim Search Agents can now use your credentials to build an agentic knowledge graph to do your high-context work. Full context building and adaptive learning. Live right now in Sim!

97,772 Aufrufe • vor 3 Tagen •via X (Twitter)

17 Kommentare

Profilbild von Marcus
Marcusvor 3 Tagen

Now Sim orders me my burrito before I even know I want it

Profilbild von Emir Karabeg
Emir Karabegvor 3 Tagen

exactly

Profilbild von Waleed
Waleedvor 3 Tagen

I love searching

Profilbild von Ethan
Ethanvor 3 Tagen

Massive launch!

Profilbild von Emir Karabeg
Emir Karabegvor 3 Tagen

🚀

Profilbild von Ihtesham Ali
Ihtesham Alivor 3 Tagen

This is what agents were missing. Great work!

Profilbild von Emir Karabeg
Emir Karabegvor 3 Tagen

Indeed. Thank you!

Profilbild von Chris Howard
Chris Howardvor 3 Tagen

@typingwala Congrats on the launch!

Profilbild von Emir Karabeg
Emir Karabegvor 3 Tagen

@typingwala thanks Chris!

Profilbild von Vraj Talati
Vraj Talativor 3 Tagen

The interesting part is not the knowledge graph itself, but whether the agent knows what to forget. Curious how you handle stale context and conflicting memories over time?

Profilbild von Harley Lewis Foote
Harley Lewis Footevor 3 Tagen

credentials + knowledge graph sounds like a compliance officer's fever dream

Profilbild von Mohit Mishra
Mohit Mishravor 3 Tagen

The real unlock isn’t just better agents—it’s better context. The more an agent understands how you work, the more useful its decisions become.

Profilbild von Mindset insider 🌟
Mindset insider 🌟vor 3 Tagen

That’s a big shift: agents moving from simple retrieval to building a living knowledge graph around your context. Full-context understanding + adaptive learning could make agentic workflows feel much more personal and capable.

Profilbild von Athena Prime
Athena Primevor 3 Tagen

The interesting unlock is the context boundary: agents become much more useful when they can retain the right working set without turning every task into a blank-slate prompt. Clear provenance and user control will matter as much as recall.

Profilbild von Markandey Sharma
Markandey Sharmavor 3 Tagen

This could be useful for workflows where the same agent needs to understand a project over a longer period.

Profilbild von Sharon Riley
Sharon Rileyvor 3 Tagen

Context building makes agents genuinely adaptive now

Profilbild von Sanskriti Naruka
Sanskriti Narukavor 3 Tagen

Congrats!!

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Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

168,153 Aufrufe • vor 1 Jahr