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AI Agents will completely transform how we interact with corporate knowledge by applying reasoning across multiple data sources. Here's an example of ChatGPT's new knowledge feature to pull information from Box and Gmail to combine it an answer a question.

Aaron Levie

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73,669 views • 8 months ago •via X (Twitter)

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As AI agents get better at computer and tool use, or writing code on the fly for a task, we're going to be able to solve much broader domains of knowledge work. Here's an example of Box AI with the new Claude Skills to generate a clean powerpoint file from existing data.

As AI agents get better at computer and tool use, or writing code on the fly for a task, we're going to be able to solve much broader domains of knowledge work. Here's an example of Box AI with the new Claude Skills to generate a clean powerpoint file from existing data.

Aaron Levie

30,235 views • 8 months ago

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,266 views • 2 years ago

Another example of the power of headless software with agents. With Claude Tag, you can give Claude access to any corporate files in Box that you can interact with from Slack. Now all of your enterprise content becomes a portable knowledge base.

Another example of the power of headless software with agents. With Claude Tag, you can give Claude access to any corporate files in Box that you can interact with from Slack. Now all of your enterprise content becomes a portable knowledge base.

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Here's a glimpse into what AI agents in knowledge work will look like. With the Box MCP server, you can tell Claude to create a folder structure in Box for an M&A deal and the data checklist. In the future you'll just have background agents automating anything.

Here's a glimpse into what AI agents in knowledge work will look like. With the Box MCP server, you can tell Claude to create a folder structure in Box for an M&A deal and the data checklist. In the future you'll just have background agents automating anything.

Aaron Levie

124,977 views • 10 months ago

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

Aaron Levie

57,745 views • 1 year ago

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

Aaron Levie

419,863 views • 2 months ago

Lets build `Auto-RAG` where we let the LLM pull the data it needs from different sources. 🔎 The user asks a question. 🤔 LLM decides whether to search its knowledge, memory, internet or make an API call. ✍️ LLM answers with the context. Code:

Lets build `Auto-RAG` where we let the LLM pull the data it needs from different sources. 🔎 The user asks a question. 🤔 LLM decides whether to search its knowledge, memory, internet or make an API call. ✍️ LLM answers with the context. Code:

Ashpreet Bedi

174,580 views • 2 years ago

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

155,514 views • 1 year ago

Data ➡️ information ➡️ knowledge andrew hill | re/acc, co-founder of Tableland, is on a mission to use Web3 databases to transform collective data into new knowledge. Learn about his path from biologist to Web3 founder in the latest “Exploring...” episode 👇

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Protocol Labs

2,569,679 views • 2 years ago

OpenAI's new o3 model is crazy good at math and logic. Here's an example of a financial analysis question using Box AI and o3 on an earnings document where advanced math and reasoning are required. This wouldn't have been possible even 3 months ago in AI.

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Aaron Levie

85,873 views • 1 year ago

OXFORD AI PROFESSOR FED 100,000 MANUSCRIPTS DATING BACK TO 3000 BCE INTO A SINGLE KNOWLEDGE GRAPH - AND THEN ASKED IT HOW MUCH TRUTH IS IN MORMONISM the answer came back at 2% this is what an AI brain looks like when you've trained it on over 100,000 historical manuscripts going back to 3000 BCE every node in the graph is a concept and every connection shows how ideas relate across 5,000 years of human knowledge the system didn't just give an answer - it traced its reasoning across thousands of interconnected sources and cross-referenced historical records before producing a single number he's now taking questions on what to ask it next the list of topics people want tested is exactly what you'd expect.

OXFORD AI PROFESSOR FED 100,000 MANUSCRIPTS DATING BACK TO 3000 BCE INTO A SINGLE KNOWLEDGE GRAPH - AND THEN ASKED IT HOW MUCH TRUTH IS IN MORMONISM the answer came back at 2% this is what an AI brain looks like when you've trained it on over 100,000 historical manuscripts going back to 3000 BCE every node in the graph is a concept and every connection shows how ideas relate across 5,000 years of human knowledge the system didn't just give an answer - it traced its reasoning across thousands of interconnected sources and cross-referenced historical records before producing a single number he's now taking questions on what to ask it next the list of topics people want tested is exactly what you'd expect.

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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 graphs as structured AI memory 16:09 – How combining graphs improves trust

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We spoke with Laura Ruis from Cohere For AI and UCL about her paper "Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models" where she demonstrated an interesting gap between retrieval and reasoning queries in LLMs indicating the presence of synthesised procedural knowledge generation.

We spoke with Laura Ruis from Cohere For AI and UCL about her paper "Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models" where she demonstrated an interesting gap between retrieval and reasoning queries in LLMs indicating the presence of synthesised procedural knowledge generation.

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Bismillah. Introducing ScribeTools 2.0 : THE platform to transform your documents (Arabic/Urdu/English/etc) into actual usable knowledge! It unlocks centuries of knowledge and allows you to interact with them in any way possible: search it, copy/paste it, throw it into an LLM, or whatever else! Link is below

Bismillah. Introducing ScribeTools 2.0 : THE platform to transform your documents (Arabic/Urdu/English/etc) into actual usable knowledge! It unlocks centuries of knowledge and allows you to interact with them in any way possible: search it, copy/paste it, throw it into an LLM, or whatever else! Link is below

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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 views • 9 months ago

NEW: DevRev raises a $100M Series A to build enterprise AI apps & agents - Valued at $1.1B 🦄 The core of their platform is an enterprise knowledge graph that powers AI apps & agents for customer service, product, software engineering, etc. Here's how DevRev works:

NEW: DevRev raises a $100M Series A to build enterprise AI apps & agents - Valued at $1.1B 🦄 The core of their platform is an enterprise knowledge graph that powers AI apps & agents for customer service, product, software engineering, etc. Here's how DevRev works:

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11,222 views • 1 year ago

AI agents are getting upgraded capabilities at a rapid rate. Here's the Box MCP server connected to Claude to generate a near-perfect powerpoint presentation based on an existing document. This is a small peek into what the future of knowledge work will look like.

AI agents are getting upgraded capabilities at a rapid rate. Here's the Box MCP server connected to Claude to generate a near-perfect powerpoint presentation based on an existing document. This is a small peek into what the future of knowledge work will look like.

Aaron Levie

105,006 views • 9 months ago

What would it look like to combine Google search (shallow Knowledge Graph reasoning over an ultra-ultra-wide index) with LLM in-context learning (highly intelligent operations on a tiny index)?

What would it look like to combine Google search (shallow Knowledge Graph reasoning over an ultra-ultra-wide index) with LLM in-context learning (highly intelligent operations on a tiny index)?

Dwarkesh Patel

100,546 views • 1 year ago

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

17,750 views • 4 months ago