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Traces, knowledge, and reasoning paths form a reliable source of truth for AI agents. 🤝 Charles Ivie, former Senior Graph Architect at Amazon Web Services, on why knowledge-backed traces are essential for trustworthy AI. 03:34 – Why data origin matters 07:17 – Interoperability and trusted knowledge 11:12 – Knowledge... show more

OriginTrail

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12,528 次观看 • 7 个月前 •via X (Twitter)

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Building AI agents? 🚧 Make sure they actually know where their answers come from. As Brana Rakic demonstrates, scalable AI requires verifiable knowledge, rule-based reasoning, and LLMs grounded in trusted memory. Key highlights: 03:25 - From human data to era of experience 07:42 - Agent architecture: memory + models 15:18 - Knowledge graphs 101: triples explained 34:53 - Symbolic reasoning vs hallucination 39:03 - Knowledge Asset, UAL & ownership

Building AI agents? 🚧 Make sure they actually know where their answers come from. As Brana Rakic demonstrates, scalable AI requires verifiable knowledge, rule-based reasoning, and LLMs grounded in trusted memory. Key highlights: 03:25 - From human data to era of experience 07:42 - Agent architecture: memory + models 15:18 - Knowledge graphs 101: triples explained 34:53 - Symbolic reasoning vs hallucination 39:03 - Knowledge Asset, UAL & ownership

OriginTrail

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When data origins are unclear, trust becomes a core challenge in AI today. Transparency and authenticity play a central role, as Charles Ivie, former Senior Graph Architect at Amazon Web Services, explains. 🤝

When data origins are unclear, trust becomes a core challenge in AI today. Transparency and authenticity play a central role, as Charles Ivie, former Senior Graph Architect at Amazon Web Services, explains. 🤝

OriginTrail

14,555 次观看 • 7 个月前

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.

knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.

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57,466 次观看 • 11 个月前

Knowledge graphs are insanely good at giving agents human-like memory! Today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. It's built using a real-time knowledge graph. 100% open-source and self-hosted.

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Akshay 🚀

156,483 次观看 • 1 年前

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

Santiago

398,185 次观看 • 1 年前

Andrew Ng just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to agentic knowledge Graphs • 03:07 - Construction of agentic Graphs • 14:00 - Architecture of multi-agent systems • 23:00 - Building agentic graphs with Google ADK • 01:06:03 - Why Graphsare the future of agentic AI Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

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Codez

463,178 次观看 • 28 天前

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

155,647 次观看 • 1 年前

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OriginTrail

14,974 次观看 • 7 个月前

Still the best hour on graph engineering ever recorded, Andrew Ng breaking down how to build agentic knowledge graphs from scratch: 00:00 - what agentic knowledge graphs actually are 03:05 - building a graph from scratch 13:58 - the architecture behind multi-agent systems 22:57 - building a real one with Google ADK 01:06:02 - why graphs are the future of AI agents I've seen $500 courses that teach less. Watch it, then take it further with my step-by-step guide on graph engineering below.

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261,639 次观看 • 18 天前

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:

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Morlex

72,329 次观看 • 6 天前

Stop prompt-stuffing yesterday’s context into your agent. Give it verifiable memory. 🕸️ When memory has provenance, agents can reuse it, audit it, and carry it across tools. Brana Rakic, Shaw (spirit/acc) (elizaOS), Ortecha | data made human & JoeVezz (LunarCrush 🌕™) break it down. 00:57 – What agents need to scale 05:45 – Social data, signals & real-time context 13:42 – Knowledge graphs as shared memory for AI agents 29:13 – Interoperability, trust & coordination

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OriginTrail

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New course added! We’re launching “Knowledge Graphs for AI Agent API Discovery,” built in collaboration with SAP and taught by Pavithra G K and Lars Heling. When agents call APIs in the wrong sequence, entire workflows collapse. The challenge? Most agents lack the contextual understanding of which endpoints depend on others and why order matters in real business processes. This course teaches you to: ✅Construct a knowledge graph from API specifications ✅ Extend it with business-process data so dependencies and order are explicit ✅ Use semantic retrieval and process edges to find required APIs and their sequence, then build an agent to execute them Enroll now!

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🎓 Why can’t rational people just agree to disagree? In this episode, Steven Pinker unpacks how common knowledge shapes truth, trust, and the invisible rules holding civilization together—from markets and morality to AI and belief itself. Timestamp: 00:00 Intro 00:58 When common knowledge is wrong 02:37 The role of common knowledge in social coordination 08:48 Free will and determinism 14:48 Judging a book by its cover 21:08 Government suppression and common knowledge 30:08 The agree to disagree theorem 34:54 Generosity and charity in human affairs 41:12 The role of common knowledge in religious beliefs 48:31 LLMs and common knowledge 50:39 Outro

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INTO THE IMPOSSIBLE Podcast

14,321 次观看 • 10 个月前

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

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