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Reducto CLI is the best way for agents to interact with document data in any workflow, and I’m really excited to see the range of use cases that are possible with this. It uses our frontier models for anything from parsing to editing, and can help automate full workflows... show more

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16,917 просмотров • 7 месяцев назад •via X (Twitter)

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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 просмотров • 10 месяцев назад

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 просмотров • 3 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

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

Jerry Liu

24,245 просмотров • 1 год назад

custom fee sharing is now LIVE on BAGS launch a coin and share fees with any account on X CUSTOMIZE the royalty split however you'd like. This feature unlocks a lot of new use cases that can only be done on Bags. We're excited to see how you use it.

custom fee sharing is now LIVE on BAGS launch a coin and share fees with any account on X CUSTOMIZE the royalty split however you'd like. This feature unlocks a lot of new use cases that can only be done on Bags. We're excited to see how you use it.

FINN

124,200 просмотров • 11 месяцев назад

In the past, it wasn't possible for companies to tap into their unstructured data. But that's now changing with AI. On Greylock Partners Change Agents, Aaron Levie shared "We see a future where you can actually begin to bring agents to all that data and answer any kind of question the customer has, and then eventually be able to automate any kind of workflow that the customer has."

In the past, it wasn't possible for companies to tap into their unstructured data. But that's now changing with AI. On Greylock Partners Change Agents, Aaron Levie shared "We see a future where you can actually begin to bring agents to all that data and answer any kind of question the customer has, and then eventually be able to automate any kind of workflow that the customer has."

Box

315,675 просмотров • 7 месяцев назад

Databricks launches Free Edition! Explore and learn the latest data and AI technologies for free. Users can experiment with the full range of use cases – from building AI agents and applications to analyzing data with SQL – on the same platform used by millions of professionals. We’re proud to help today’s students and enthusiasts build real skills on real tools in today's competitive AI and ML job market.

Databricks launches Free Edition! Explore and learn the latest data and AI technologies for free. Users can experiment with the full range of use cases – from building AI agents and applications to analyzing data with SQL – on the same platform used by millions of professionals. We’re proud to help today’s students and enthusiasts build real skills on real tools in today's competitive AI and ML job market.

Databricks

25,085 просмотров • 1 год назад

What does it actually look like to use AI agents for real ops work? See how agentic workflows can help with personalization, incident management, cost analysis, and reporting, plus catch a demo of Azure SRE Agent in the latest episode of #AzureFriday:

What does it actually look like to use AI agents for real ops work? See how agentic workflows can help with personalization, incident management, cost analysis, and reporting, plus catch a demo of Azure SRE Agent in the latest episode of #AzureFriday:

Microsoft Azure

14,973 просмотров • 2 месяцев назад

it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it

it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it

joyce

14,133 просмотров • 4 месяцев назад

AIxBlock – A Decentralized Platform for End-to-End AI Development & Workflow Automation What can you actually build with AIxBlock? Here’s the full flow — fully customizable and self-hostable: [Curate Data] → [Label] → [Train] → [Deploy] → [Automate] → [Monetize] Pull models from Hugging Face, bring in your own data, train on decentralized GPUs, and automate everything with low-code builders. 🧪 Explore the platform: 🎥 Want to see a real use-case demo? Check our full gallery here:

AIxBlock – A Decentralized Platform for End-to-End AI Development & Workflow Automation What can you actually build with AIxBlock? Here’s the full flow — fully customizable and self-hostable: [Curate Data] → [Label] → [Train] → [Deploy] → [Automate] → [Monetize] Pull models from Hugging Face, bring in your own data, train on decentralized GPUs, and automate everything with low-code builders. 🧪 Explore the platform: 🎥 Want to see a real use-case demo? Check our full gallery here:

AIxBlock

14,328 просмотров • 1 год назад

Openclaw wrappers are the best businesses to start in 2026. The reason you can't find a good use case for open claw is that the use cases are boring and live inside legacy businesses with manual workflows. They're repetitive tasks that you can solve by deploying open claw and Computer-Use Agents.

Openclaw wrappers are the best businesses to start in 2026. The reason you can't find a good use case for open claw is that the use cases are boring and live inside legacy businesses with manual workflows. They're repetitive tasks that you can solve by deploying open claw and Computer-Use Agents.

nick vasilescu

147,638 просмотров • 4 месяцев назад

Kicking off our Great American Comeback Tour in the GREAT state of Iowa! Florida and Iowa have led the way in the fight for our way of life — and I’m running for president to take that fight to the White House. We are building a movement to restore America. This is what it looks like:

Kicking off our Great American Comeback Tour in the GREAT state of Iowa! Florida and Iowa have led the way in the fight for our way of life — and I’m running for president to take that fight to the White House. We are building a movement to restore America. This is what it looks like:

Ron DeSantis

2,114,986 просмотров • 3 лет назад

“I mean, hey, I’m all for [Mohabbot], it’s great that people are loving the show and loving our characters. I love working with Shawn, he’s really great, I’m really excited to have him direct in a few episodes, actually. I’m really excited to see where our characters go.”

“I mean, hey, I’m all for [Mohabbot], it’s great that people are loving the show and loving our characters. I love working with Shawn, he’s really great, I’m really excited to have him direct in a few episodes, actually. I’m really excited to see where our characters go.”

best of supriya ganesh

53,300 просмотров • 10 месяцев назад

Open-sourcing Introspect: MIT-licensed Deep-Research for your internal data! Works with spreadsheets, databases, PDFs, and web search. Has a remarkably simple architecture – Sonnet agent armed with recursive tool calling and 3 default tools. Best for use-cases where you want to combine insights from SQL with unstructured data + data from the web. Launching a bunch of products built on top of of this soon, but wanted to open-source our core engine to give back to the community! 2 min demo below, links in comments

Open-sourcing Introspect: MIT-licensed Deep-Research for your internal data! Works with spreadsheets, databases, PDFs, and web search. Has a remarkably simple architecture – Sonnet agent armed with recursive tool calling and 3 default tools. Best for use-cases where you want to combine insights from SQL with unstructured data + data from the web. Launching a bunch of products built on top of of this soon, but wanted to open-source our core engine to give back to the community! 2 min demo below, links in comments

Rishabh Srivastava

71,512 просмотров • 1 год назад

🍊 Rewind Primus AMA for seeking WEB3 new phase Xiang Xie (Xiang Xie) said Primus is a solution that enables real-world data, which is necessary for building various applications in Web3, to be brought on-chain and verified without exposing sensitive information. Through this, Primus enables use cases such as proof of deposits in DeFi, and in the AI agents sector, it allows agents to prove the information they need and the actions they have completed in a trustless way, without relying on third parties. This expands the scope of what these sectors can do and makes broader product development possible. This kind of solution is an essential component for Web3 to move beyond degen culture or crypto-native use cases and enter a much larger arena. Beyond mainnet, Primus acts as an enabler that helps diverse dApps adopt and apply this technology stack. Discover what kind of ecosystem they are building and what plans they have through our recent podcast.

🍊 Rewind Primus AMA for seeking WEB3 new phase Xiang Xie (Xiang Xie) said Primus is a solution that enables real-world data, which is necessary for building various applications in Web3, to be brought on-chain and verified without exposing sensitive information. Through this, Primus enables use cases such as proof of deposits in DeFi, and in the AI agents sector, it allows agents to prove the information they need and the actions they have completed in a trustless way, without relying on third parties. This expands the scope of what these sectors can do and makes broader product development possible. This kind of solution is an essential component for Web3 to move beyond degen culture or crypto-native use cases and enter a much larger arena. Beyond mainnet, Primus acts as an enabler that helps diverse dApps adopt and apply this technology stack. Discover what kind of ecosystem they are building and what plans they have through our recent podcast.

Wecryptotogether (Theo/acc)

14,608 просмотров • 1 месяц назад

Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move

Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move

OpenLedger

61,662 просмотров • 1 год назад

The new Codex is another jump in what agents will look like for knowledge workers. Agents that can code, work with tools, and use computers, can begin to execute long running tasks in the background for all areas of work. This can mean drafting reports, setting up data rooms for a merger, reviewing contracts, helping onboard clients, generating marketing assets, processing invoices, and more. With the Box plugin inside of the new Codex, you can begin to automate almost any kind of work with enterprise content. And importantly, being able to work across multiple apps is a huge point of leverage because we can now far more easily connect our tools together.

The new Codex is another jump in what agents will look like for knowledge workers. Agents that can code, work with tools, and use computers, can begin to execute long running tasks in the background for all areas of work. This can mean drafting reports, setting up data rooms for a merger, reviewing contracts, helping onboard clients, generating marketing assets, processing invoices, and more. With the Box plugin inside of the new Codex, you can begin to automate almost any kind of work with enterprise content. And importantly, being able to work across multiple apps is a huge point of leverage because we can now far more easily connect our tools together.

Aaron Levie

71,589 просмотров • 3 месяцев назад

#WATCH | Delhi: In the delegation-level meeting with EAM Dr S Jaishankar, German Foreign Minister Johann Wadephul says, "...For me, it was really interesting to spend a whole day in Bangalore to see what we are really doing already together, economically and culturally. That was really astonishing for me to see how many students are learning German and that shows us that the potential is really high for our countries. Economically, you already mentioned the FTA. Germany is in full support of carrying this Agreement negotiated as soon as possible. We are a free trade nation...the EU is working on (the Agreement with) India, that's the most important to us. I hope really that we can be successful...You can be assured that we will use all arguments we have with the Commission to put this forward and to reach a level between our countries and between the EU and India on this..."

#WATCH | Delhi: In the delegation-level meeting with EAM Dr S Jaishankar, German Foreign Minister Johann Wadephul says, "...For me, it was really interesting to spend a whole day in Bangalore to see what we are really doing already together, economically and culturally. That was really astonishing for me to see how many students are learning German and that shows us that the potential is really high for our countries. Economically, you already mentioned the FTA. Germany is in full support of carrying this Agreement negotiated as soon as possible. We are a free trade nation...the EU is working on (the Agreement with) India, that's the most important to us. I hope really that we can be successful...You can be assured that we will use all arguments we have with the Commission to put this forward and to reach a level between our countries and between the EU and India on this..."

ANI

45,820 просмотров • 10 месяцев назад

UI Skills are now easier for agents. Introducing UI Skills CLI. ask Codex, Cursor, Claude Code, or any agent to use the CLI, like: "use `npx ui-skills start` to fix the motion in this dialog" it finds the best design-engineering skill for the job and uses it as context

UI Skills are now easier for agents. Introducing UI Skills CLI. ask Codex, Cursor, Claude Code, or any agent to use the CLI, like: "use `npx ui-skills start` to fix the motion in this dialog" it finds the best design-engineering skill for the job and uses it as context

Ibelick

59,045 просмотров • 1 месяц назад

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

102,878 просмотров • 1 месяц назад