正在加载视频...

视频加载失败

Jev will be super helpful for agents to make split second decisions in workflows, data classification, judgment calls, and hundreds of other use-cases in the enterprise. Here's a quick demo with Box and Jev to make that real. The demo pulls an incident report from Box, asks whether it's...

109,716 次观看 • 2 天前 •via X (Twitter)

31 条评论

serx · fireply.ai 的头像
serx · fireply.ai2 天前

olga stefaniuk is going to open box one day and find the whole thing already sorted

ghxst 的头像
ghxst2 天前

i can think of hundreds of interesting use cases for it, pretty interesting model

NASMA ELLIS 的头像
NASMA ELLIS2 天前

Great use case — fast, cheap classification is exactly what unlocks AI at scale for workflows like this. The Box integration makes it concrete. Excited to see where this goes 🚀

Laks Dondeti 🇺🇸 的头像
Laks Dondeti 🇺🇸2 天前

Absolutely. Makes it possible to get the first decision or first response out super fast and super cheap.

HoloRolnik.eth 🧷🦧 🇩🇪🇺🇦 Stop The War 的头像
HoloRolnik.eth 🧷🦧 🇩🇪🇺🇦 Stop The War2 天前

I was telling this morning one of your sales guys from Germany about JEV and what an awesome match that would be for @Box

Josh R Barry 的头像
Josh R Barry2 天前

Split-second judgment is the easy demo and the expensive bug.

Abdul 的头像
Abdul2 天前

Hers what I am cooking with jev:

sunil mallya 的头像
sunil mallya2 天前

What about data protection in insurance claims?

Gareth ⌥ Agentik {OS} 的头像
Gareth ⌥ Agentik {OS}2 天前

Great demo, Aaron. Logged it on Jev Radar, the free place where every Jev build gets collected and explained in plain English. Yours is here 👇

Jane Sun 的头像
Jane Sun2 天前

Split second judgment calls with no calendar invites. This is what enterprise software was supposed to feel like all along

Marc Baumann 🌔 的头像
Marc Baumann 🌔2 天前

agents don't die on smarts. they die on permissions and slow judgment. if it can't classify a ticket before it acts, i won't trust it near production data.

Alex Ung 的头像
Alex Ung2 天前

compliance and records workflows have needed this for years. software handling the routine paper trails lets actual operators focus on real decisions.

creedants 的头像
creedants2 天前

Incident report out of Box, severity call, then escalate / monitor / review with the metadata already filled. Nearly free and nearly instant. I will steal that flow for my own queues first.

Marc Baumann 🌔 的头像
Marc Baumann 🌔2 天前

enterprise agents do not fail on model iq. they fail on judgment latency and permissioning. jev-style split-second classification is the boring layer that makes agent workflows underwritable. without it you get a demo that cannot touch production data. i will not underwrite process that cannot classify an incident report before it acts on it.

Emon Datta 的头像
Emon Datta1 天前

Fast triage matters when the decision can happen inline instead of waiting in a queue.

AI Mastery Guide 的头像
AI Mastery Guide1 天前

split second decisions in workflows, box demo looks solid

大瀧 達生 / 株式会社◯ / AI研究 / 地方創生 / 的头像
大瀧 達生 / 株式会社◯ / AI研究 / 地方創生 /2 天前

This is exactly what enterprise AI needs right now. The ability to make instant, low-cost judgment calls at scale will completely transform document routing. Great integration with Box!

Keep Your Head 的头像
Keep Your Head2 天前

Escalate versus monitor is cheap to get right and expensive to get wrong. The value of that classifier is set by the rare miss, not the average case.

Mahesh Lambe 的头像
Mahesh Lambe2 天前

so, can u please share the telemetry/evals/outcomes?

Stephen unfair.so 的头像
Stephen unfair.so2 天前

the review folder is doing important work here

MartinOL 的头像
MartinOL2 天前

The interesting leap is not the demo. It is giving agents a bounded judgment call with an audit trail. What is the failure budget?

Steve Gallion 的头像
Steve Gallion2 天前

Have you explored trying this with more complex web application work flows or simply using stand alone docs from a CMS like box?

Amit Vyas ❖ 的头像
Amit Vyas ❖2 天前

This shifts the enterprise paradigm from latency-bound human coordination to low-cost, event-driven reasoning. The 10-year implication is the total decoupling of business process execution from human cognitive throughput.

Yokush 的头像
Yokush2 天前

Classification is the easy half; the demo is compelling precisely because the output is a recommendation, not an action. The moment that same agent gets to act on needs_escalation: true (freeze the account, notify the customer, open the ticket), it stops being a classifier and becomes delegated authority. Then the design surface moves off the model and onto the mandate: what ceiling the run has, who is liable if the call is wrong, and who can revoke it mid-flight. Every use-case on your list has that shape; the ones that ship will be the ones where the accountability was designed first.

Denys Voroshylov 的头像
Denys Voroshylov2 天前

This is where agents get much more useful in enterprise: fast judgment is valuable, but auditable judgment is what lets teams delegate real workflows. The decision trail matters as much as the decision.

Jon Kraayenbrink 的头像
Jon Kraayenbrink2 天前

with the speed of jev there are some many use cases opening. i was all day browsing what people are building with it and saved it to

Matías Matthews ✈️ Backplane 的头像
Matías Matthews ✈️ Backplane2 天前

If someone updates the incident report, does the routing get revisited too? That's the part I'd want to see next.

AI Apps API 的头像
AI Apps API2 天前

The routing example is the one worth copying. Escalate, monitor, review is three buckets, and the hard part was never the classification, it was doing it fast enough to sit inline in the workflow instead of in a nightly batch. The piece I would watch is what happens on a low confidence call. A wrong folder is cheap, a wrong severity on an incident is not.

viet york ᵐᵒˡˡʸ·ᶜᵒᵐ 的头像
viet york ᵐᵒˡˡʸ·ᶜᵒᵐ2 天前

yeah agents stall on the judgment call not the fetch

Renzo 的头像
Renzo2 天前

This is where AI starts becoming infrastructure rather than a chatbot. Fast, cheap judgment calls embedded directly into workflows could unlock thousands of small decisions that were never economical to automate before.

dailycontent 的头像
dailycontent2 天前

Most enterprise 'automation' is just a glorified flowchart. Adding real-time judgment is how we actually move from copilots to autonomous agents. Speed is the feature.

相关视频

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 次观看 • 1 年前

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Aaron Levie

44,624 次观看 • 5 个月前

Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.

Santiago

234,829 次观看 • 3 年前

I've built it for you!! It's an automated AI system that analyzes AI case studies (you can change the use case) to identify and document enterprise-level AI implementations. It starts by reading URLs from a CSV file and uses web scraping (either through WebLoader or Firecrawl) to extract the content from each case study. The extracted content is then sent to Claude 3.5 Sonnet, which analyzes whether the case study represents a genuine enterprise AI implementation based on specific criteria like company maturity, implementation scale, and measurable business outcomes. For each URL, the system first saves the raw content and then performs this initial qualification analysis. If Claude determines that a case study qualifies as an enterprise AI implementation, the system proceeds to generate a detailed analysis. It creates three types of reports: - an individual case study report with sections like Executive Summary, AI Strategy Analysis, and Business Impact Assessment - a cross-case analysis that identifies patterns and trends across multiple case studies - and an executive dashboard summarizing key metrics and insights. All of these reports are saved in structured formats (markdown for individual reports, JSON for cross-case analysis and dashboard) in their respective directories. If a case study doesn't qualify as an enterprise AI implementation, the system logs the reason and moves on to the next URL. The entire process is asynchronous and provides detailed terminal feedback about its progress and decisions.

Muratcan Koylan

85,353 次观看 • 1 年前