Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Here's Operator organizing a Box account by moving content into relevant folders it created. The magic though is that it understands multiple names for the same company and organizes them correctly. You'll be able to just offload large annoying work to AI Agents.

Aaron Levie

3,066,252 subscribers

88,096 просмотров • 1 год назад •via X (Twitter)

Образование Наука и технологии Новости и политика

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля Chirag Modi

Chirag Modi1 год назад

RIP first year investment banking analysts

Фото профиля Fast Company

Fast Company1 год назад

#AI agents: from tools to teammates. Software that can act, react, and interact with both humans and other agents will transform how businesses operate—and how they should invest in technology. This @EY_US article explains more. #ad

Фото профиля Stefan Behrendt

Stefan Behrendt1 год назад

Shouldn’t that already have been done by box itself 😀

Фото профиля Todd Saunders

Todd Saunders1 год назад

Why does it even have to show you the UI, why not just do the task. I feel like showing the UI is just marketing

Фото профиля Taimur

Taimur1 год назад

The future is agentic! We are doing just this with @doksihq but for support content!

Фото профиля Ali Heib

Ali Heib1 год назад

AI Agents like Operator are seriously changing the game! They can handle the heavy lifting of organizing files effortlessly.

Фото профиля Dwayne Samuels

Dwayne Samuels1 год назад

i know you can’t wait on the api

Фото профиля Spencer Schiff

Spencer Schiff1 год назад

@TheRealAdamG Not just annoying work — also highly creative work

Фото профиля korben

korben1 год назад

actually such a good use case since it can also analyze images and other relevant metadata to further improve organization.

Фото профиля IIl7623

IIl76231 год назад

Box didn’t have the ability to understand names and organize folders before? LLMs have been around years. What have you been doing?

Фото профиля Rufus

Rufus1 год назад

Impressive, I will have to try it out

Похожие видео

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 просмотров • 10 месяцев назад

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

419,956 просмотров • 2 месяцев назад

Demis Hassabis says the Nano Banana Pro system is moving toward AGI for imaging It doesn't just make images, it understands what's in them The goal is to merge this with world models like Genie and SIMA into one system "that might be a candidate for proto-AGI"

Demis Hassabis says the Nano Banana Pro system is moving toward AGI for imaging It doesn't just make images, it understands what's in them The goal is to merge this with world models like Genie and SIMA into one system "that might be a candidate for proto-AGI"

Haider.

50,740 просмотров • 7 месяцев назад

It’s wild watching AI Agents work with software. Here’s ChatGPT connected to Box running a deep research task on market analysis documents. The future is just giving AI Agents the data and systems to comb through and having them work for you in the background.

It’s wild watching AI Agents work with software. Here’s ChatGPT connected to Box running a deep research task on market analysis documents. The future is just giving AI Agents the data and systems to comb through and having them work for you in the background.

Aaron Levie

235,980 просмотров • 1 год назад

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

81,696 просмотров • 10 месяцев назад

AI agents with full tool use is going to be quite insane. Here's Claude using the Box and Linear MCP servers to take product roadmap docs from Box and turns them into issues to track. This is a small example of what the future of AI agent interoperability looks like.

AI agents with full tool use is going to be quite insane. Here's Claude using the Box and Linear MCP servers to take product roadmap docs from Box and turns them into issues to track. This is a small example of what the future of AI agent interoperability looks like.

Aaron Levie

87,935 просмотров • 11 месяцев назад

OpenAI just launched Operator. This got me thinking about the potential impact of AI agents on how buyers will discover domain names in the future, so I decided to give Operator a test run. The results were eye-opening. I started with a simple prompt: Help me find a great domain for my pet food brand from It should be a cute name and ideally under $5000." Operator took it from there. It accessed searched for pet food-related domains, applied the appropriate pricing filter, and shortlisted names that exuded cuteness. It didn’t stop there—it even assisted me in negotiating a better price through live chat. The implications of this technology are huge. For marketplaces, it will become critical to optimize the way domains are merchandised to ensure AI agents can easily discover relevant options. This means having in-depth filters, a highly effective search, and real time communication tools will matter more than ever. It is time to start adapting for this shift

Darpan

11,133 просмотров • 1 год назад

.Aaron Levie shared with CNBC why the rapid rise of AI agents is good news for enterprises that have the right foundation in place. "If you want to be able to include them in your workflow, have them augment your work, they need access to your critical enterprise data. And they need to access it in a secure way, in a way that's governed."

.Aaron Levie shared with CNBC why the rapid rise of AI agents is good news for enterprises that have the right foundation in place. "If you want to be able to include them in your workflow, have them augment your work, they need access to your critical enterprise data. And they need to access it in a secure way, in a way that's governed."

Box

1,904,830 просмотров • 3 месяцев назад

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 просмотров • 10 месяцев назад

AI agents need a structured place to do their work and access relevant content. A filesystem provides a clear, navigable way to manage that context. Here’s how Box can back that abstraction in LangChain Deep Agents — preserving the simplicity of the filesystem interface while adding enterprise-grade security, permission controls, version history, and collaboration at the storage layer. Explore our implementation:

AI agents need a structured place to do their work and access relevant content. A filesystem provides a clear, navigable way to manage that context. Here’s how Box can back that abstraction in LangChain Deep Agents — preserving the simplicity of the filesystem interface while adding enterprise-grade security, permission controls, version history, and collaboration at the storage layer. Explore our implementation:

Box

1,018,021 просмотров • 4 месяцев назад

Box is a launch partner for Microsoft’s Apps in Copilot launch. The Box AI Agent for Microsoft365 Copilot will be able to surface rich visual previews like slides, graphs, and images right in the conversation and have access to all of the Box MCP server tools. Coming soon to the Box AI Agent in Microsoft 365 Copilot: 🔹 MCP-powered workflows — metadata extraction, document generation, and more 🔹 Visual-first search — view complicated graphs or rich visuals that are buried in your Box content, without having to open files. 🔹 Fewer clarifications, faster decisions - move from chat to action Enable agents to bring apps into the flow of work, while keeping IT in control:

Box is a launch partner for Microsoft’s Apps in Copilot launch. The Box AI Agent for Microsoft365 Copilot will be able to surface rich visual previews like slides, graphs, and images right in the conversation and have access to all of the Box MCP server tools. Coming soon to the Box AI Agent in Microsoft 365 Copilot: 🔹 MCP-powered workflows — metadata extraction, document generation, and more 🔹 Visual-first search — view complicated graphs or rich visuals that are buried in your Box content, without having to open files. 🔹 Fewer clarifications, faster decisions - move from chat to action Enable agents to bring apps into the flow of work, while keeping IT in control:

Box

335,591 просмотров • 4 месяцев назад

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

349,977 просмотров • 2 лет назад

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.

Chris Long

13,185 просмотров • 2 лет назад

I'm feeding a team of 15 AI agents 10,000+ pieces of content about $WORTHLESS coin and giving it the objective to take it to a billion market cap. Here's the whole team and how they work together: AdsT8339RRmJbYCJo6d75cAbTguC7yMk4pft4e19pump

I'm feeding a team of 15 AI agents 10,000+ pieces of content about $WORTHLESS coin and giving it the objective to take it to a billion market cap. Here's the whole team and how they work together: AdsT8339RRmJbYCJo6d75cAbTguC7yMk4pft4e19pump

Nikolai

15,478 просмотров • 4 месяцев назад

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 просмотров • 9 месяцев назад

Soon you'll be able to use a Tesla without buying one. Today most people don't have experience with Tesla, since it requires a large upfront payment to buy one. But soon you'll be able to use Tesla AI by just downloading an app, much like Grok or ChatGPT.

Soon you'll be able to use a Tesla without buying one. Today most people don't have experience with Tesla, since it requires a large upfront payment to buy one. But soon you'll be able to use Tesla AI by just downloading an app, much like Grok or ChatGPT.

Whole Mars Catalog

138,386 просмотров • 1 год назад

What if AI could actually do your work instead of just helping with it? Today, we’re introducing Retool Agents. Build an AI coworker that connects directly to your databases and APIs right out of the box, and performs real work in your company autonomously. Watch the video 👇🧵

What if AI could actually do your work instead of just helping with it? Today, we’re introducing Retool Agents. Build an AI coworker that connects directly to your databases and APIs right out of the box, and performs real work in your company autonomously. Watch the video 👇🧵

Retool

138,877 просмотров • 1 год назад

It's time to make AI Agents useful. I'm thrilled to announce that the HyperWrite Agent Studio is now live! Just show it a task once by doing it yourself, and the AI can repeat it.

It's time to make AI Agents useful. I'm thrilled to announce that the HyperWrite Agent Studio is now live! Just show it a task once by doing it yourself, and the AI can repeat it.

Matt Shumer

102,316 просмотров • 2 лет назад

We built AI Agents your team can work on together. Check it out if you're: 1. a small team doing the work of 20 ppl 2. wanting to run multiple agents 3. looking for something super easy to set up. Here's a little demo of how it works:

We built AI Agents your team can work on together. Check it out if you're: 1. a small team doing the work of 20 ppl 2. wanting to run multiple agents 3. looking for something super easy to set up. Here's a little demo of how it works:

Nebula

17,409 просмотров • 1 месяц назад