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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!

102,755 次观看 • 1 个月前 •via X (Twitter)

17 条评论

Marcus 的头像
Marcus1 个月前

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

Emir Karabeg 的头像
Emir Karabeg1 个月前

exactly

Waleed 的头像
Waleed1 个月前

I love searching

Ethan 的头像
Ethan1 个月前

Massive launch!

Emir Karabeg 的头像
Emir Karabeg1 个月前

🚀

Ihtesham Ali 的头像
Ihtesham Ali1 个月前

This is what agents were missing. Great work!

Emir Karabeg 的头像
Emir Karabeg1 个月前

Indeed. Thank you!

Chris Howard 的头像
Chris Howard1 个月前

@typingwala Congrats on the launch!

Emir Karabeg 的头像
Emir Karabeg1 个月前

@typingwala thanks Chris!

Vraj Talati 的头像
Vraj Talati1 个月前

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?

Harley Lewis Foote 的头像
Harley Lewis Foote1 个月前

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

Mohit Mishra 的头像
Mohit Mishra1 个月前

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.

Mindset insider 🌟 的头像
Mindset insider 🌟1 个月前

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.

Athena Prime 的头像
Athena Prime1 个月前

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.

Markandey Sharma 的头像
Markandey Sharma1 个月前

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

Sharon Riley 的头像
Sharon Riley1 个月前

Context building makes agents genuinely adaptive now

Sanskriti Naruka 的头像
Sanskriti Naruka1 个月前

Congrats!!

相关视频

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

169,184 次观看 • 1 年前