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"AI Agents are cron jobs", but they can also be complex step-function workflows. You need durability, retries, steps, realtime feedback… We announced Vercel Fluid compute which makes it cost-efficient to stream long responses that wait for LLMs. Upstash offers a great workflow orchestration solution that natively integrates with Next.js... show more

Guillermo Rauch

391,541 subscribers

113,420 次观看 • 1 年前 •via X (Twitter)

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6 条评论

Guillermo Rauch 的头像

Guillermo Rauch1 年前

You can install Qstash (which their Workflow SDK depends on) directly from Vercel Marketplace (Storage → Create Database):

Sabine VanderLinden 的头像

Sabine VanderLinden1 年前

AI isn’t a 'set it and forget it' deal. 🤖 It’s a workflow glow-up. @EY_US gets it: adapting processes is key to unlocking AI’s full power. So, are you transforming your operations—or just hoping for magic? ✨ #AIAdoption #BusinessTransformation #FutureOfWork

Lucas La Tour 的头像

Lucas La Tour1 年前

@vercel @upstash Dope!

ben shafii🍊 — oss/acc 的头像

ben shafii🍊 — oss/acc1 年前

@vercel @upstash we moved from manual processes to scripts to automate speed, and now from scripts to agents to automate resilience and adaptability

Bailey Simrell 的头像

Bailey Simrell1 年前

@vercel @upstash This is exactly how my lease abstract generator works, built on AI SDK

Guillermo Rauch 的头像

Guillermo Rauch1 年前

@vercel @upstash Woah, cool!

相关视频

The Vercel fluid × Upstash agents framework just solved one of the trickiest issues in chatbots/agents and one of my biggest pet peeves 😆 Notice that right after submitting my message, it immediately assigns an uuid (`/wfr_KgW5x…`) to the job. At this point, my internet might flake out, I might shut my laptop lid, I might go to my phone and open the chat from there… What you're looking at is a resumable, durable agentic workflow that's fault tolerant on the server and on the client. On the server side, Fluid functions on Vercel will be invoked for each step, and they can fail and get retried. On the client, polling for changes ensures eventual consistency even in the presence of very long network partitions. Shoutout to yunus for collaborating with me on shipping these improvements. Check out the OSS Next.js repo to learn these patterns and the demo at: ps: it's actually impressive what a wide range of questions this simple demo agent can answer by choosing between Exa / Wikipedia / Wolfram

The Vercel fluid × Upstash agents framework just solved one of the trickiest issues in chatbots/agents and one of my biggest pet peeves 😆 Notice that right after submitting my message, it immediately assigns an uuid (`/wfr_KgW5x…`) to the job. At this point, my internet might flake out, I might shut my laptop lid, I might go to my phone and open the chat from there… What you're looking at is a resumable, durable agentic workflow that's fault tolerant on the server and on the client. On the server side, Fluid functions on Vercel will be invoked for each step, and they can fail and get retried. On the client, polling for changes ensures eventual consistency even in the presence of very long network partitions. Shoutout to yunus for collaborating with me on shipping these improvements. Check out the OSS Next.js repo to learn these patterns and the demo at: ps: it's actually impressive what a wide range of questions this simple demo agent can answer by choosing between Exa / Wikipedia / Wolfram

Guillermo Rauch

56,799 次观看 • 1 年前

We're cooking up a mechanism to seamlessly resume streams and share state across Fluid functions. AI apps pose an interesting challenge to developers: they need to stream feedback in realtime, they have multiple agents behind the scenes, the conversation needs to be shareable, resumable, survive network disconnections. Even with this kind of stuff was non-trivial. You always need a durability source (in this demo below, you can see the 'stream id' in the URL), so a stateful bidirectional transport like WebSocket is *not enough*. Not only is it not enough, it's also not necessary (the demo below uses good ol' HTTP streaming). Also: what happens if the stateful Node WebSocket server was re-deployed mid-stream? or if it crashed? how do the streams recover? I'm excited for Vercel to give you all the primitives to execute on applications that raise the bar on not just performance, but also on fault tolerance. h/t Malte Ubl for sweating the details here.

We're cooking up a mechanism to seamlessly resume streams and share state across Fluid functions. AI apps pose an interesting challenge to developers: they need to stream feedback in realtime, they have multiple agents behind the scenes, the conversation needs to be shareable, resumable, survive network disconnections. Even with this kind of stuff was non-trivial. You always need a durability source (in this demo below, you can see the 'stream id' in the URL), so a stateful bidirectional transport like WebSocket is not enough. Not only is it not enough, it's also not necessary (the demo below uses good ol' HTTP streaming). Also: what happens if the stateful Node WebSocket server was re-deployed mid-stream? or if it crashed? how do the streams recover? I'm excited for Vercel to give you all the primitives to execute on applications that raise the bar on not just performance, but also on fault tolerance. h/t Malte Ubl for sweating the details here.

Guillermo Rauch

116,906 次观看 • 1 年前

Vercel AI Gateway now supports video generation. Grok Imagine Video & Image are 🆓 until tomorrow. We used v0 to create an open source Creative Studio powered by xAI Grok. Create images, videos, or make your own design tool! – it's quite fast. Some cool technical tidbits: ▪️ Vercel Workflows for reliable generation Video generation can take long. Users might restart their browsers or their wifi / LTE might drop. We smooth over that automatically. ▪️ Instant vector search Try searching the public generations. Now back to me. We used Mixedbread to index the content visually, so you can search 𝚋𝚕𝚊𝚌𝚔 𝚏𝚎𝚕𝚒𝚗𝚎 and get a cat (try it!) ▪️ Vercel AI Gateway All existing and future Grok models are accessible via our gateway without extra setup. When you deploy to Vercel, you get access to hundreds of models with a single account and unified billing It's obviously built with Next.js (RSC). Open it in v0 to make it your own! h/t Esteban Suárez for shipping this.

Vercel AI Gateway now supports video generation. Grok Imagine Video & Image are 🆓 until tomorrow. We used v0 to create an open source Creative Studio powered by xAI Grok. Create images, videos, or make your own design tool! – it's quite fast. Some cool technical tidbits: ▪️ Vercel Workflows for reliable generation Video generation can take long. Users might restart their browsers or their wifi / LTE might drop. We smooth over that automatically. ▪️ Instant vector search Try searching the public generations. Now back to me. We used Mixedbread to index the content visually, so you can search 𝚋𝚕𝚊𝚌𝚔 𝚏𝚎𝚕𝚒𝚗𝚎 and get a cat (try it!) ▪️ Vercel AI Gateway All existing and future Grok models are accessible via our gateway without extra setup. When you deploy to Vercel, you get access to hundreds of models with a single account and unified billing It's obviously built with Next.js (RSC). Open it in v0 to make it your own! h/t Esteban Suárez for shipping this.

Guillermo Rauch

15,873,246 次观看 • 4 个月前

I built a money printer in two days🤯 Im 100% sure that you can copy this repo and start making money today ✨ Sell this to real estate agents and use AI to make enhanced property images and videos 🏠 → Add property image → Use AI to enhance it → Send this over to a real estate agent (they will buy) → Profit Me and Walgtech 👨🏻‍💻 talked and asked ourself how fast we could build a product that sells. I can tell you it took two days 🔥 And as always Open Source ✨ Tech Stack: Vercel shadcn Supabase Drizzle ORM Next.js #opensource #buildinpublic

I built a money printer in two days🤯 Im 100% sure that you can copy this repo and start making money today ✨ Sell this to real estate agents and use AI to make enhanced property images and videos 🏠 → Add property image → Use AI to enhance it → Send this over to a real estate agent (they will buy) → Profit Me and Walgtech 👨🏻‍💻 talked and asked ourself how fast we could build a product that sells. I can tell you it took two days 🔥 And as always Open Source ✨ Tech Stack: Vercel shadcn Supabase Drizzle ORM Next.js #opensource #buildinpublic

CodeHagen

69,369 次观看 • 6 个月前

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 次观看 • 10 个月前

We are super excited to make Restate Cloud publicly available today. Durable Execution, observability, and production-grade availability for workflows or AI agents. This release makes very smooth to work with serverless compute platforms like Cloudflare, Vercel, or Deno. And of course, your own infra on AWS, GCP, or Kubernetes. Signup is free and a few clicks with your GitHub account. A new built-in tutorial let's you run a first durable agent running in seconds. 👇

We are super excited to make Restate Cloud publicly available today. Durable Execution, observability, and production-grade availability for workflows or AI agents. This release makes very smooth to work with serverless compute platforms like Cloudflare, Vercel, or Deno. And of course, your own infra on AWS, GCP, or Kubernetes. Signup is free and a few clicks with your GitHub account. A new built-in tutorial let's you run a first durable agent running in seconds. 👇

Stephan Ewen

30,681 次观看 • 9 个月前

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Jerry Liu

20,413 次观看 • 11 个月前

When data agents fail, they often fail silently - giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure. "Building and Evaluating Data Agents" is a new short course created with Snowflake and taught by Anupam Datta and Josh Reini that teaches you to build data agents with comprehensive evaluation built in. Skills you'll gain: - Build reliable LLM data agents using the Goal-Plan-Action framework and runtime evaluations that catch failures mid-execution - Use OpenTelemetry tracing and evaluation infrastructure to diagnose exactly where agents fail and systematically improve performance - Orchestrate multi-step workflows across web search, SQL, and document retrieval in LangGraph-based agents The result: visibility into every step of your agent's reasoning, so if something breaks, you have a systematic approach to fix it. Sign up to get started:

When data agents fail, they often fail silently - giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure. "Building and Evaluating Data Agents" is a new short course created with Snowflake and taught by Anupam Datta and Josh Reini that teaches you to build data agents with comprehensive evaluation built in. Skills you'll gain: - Build reliable LLM data agents using the Goal-Plan-Action framework and runtime evaluations that catch failures mid-execution - Use OpenTelemetry tracing and evaluation infrastructure to diagnose exactly where agents fail and systematically improve performance - Orchestrate multi-step workflows across web search, SQL, and document retrieval in LangGraph-based agents The result: visibility into every step of your agent's reasoning, so if something breaks, you have a systematic approach to fix it. Sign up to get started:

Andrew Ng

101,930 次观看 • 9 个月前

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

117,912 次观看 • 9 个月前

Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:

Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:

Kawsar

31,812 次观看 • 4 个月前

Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison Mati Staniszewski ElevenLabs

Mati Staniszewski on why ElevenLabs is betting on a cascaded approach for voice agents: “Our approach, as you think about voice agents, conversational agents, is effectively a cascaded approach. You use transcription or speech-to-text, an LLM, text-to-speech, and orchestrate all of that together. Then you have speech-to-speech, which goes directly from speech, and there’s a speech response on the other side. Today, we are optimizing heavily on a cascaded approach. As we work with a lot of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want good visibility into each of the steps and great accuracy of all the models. But beyond that, they can abstract away what’s the LLM layer, what’s the intelligence layer, and the integrations are easier in that system. That’s where we are betting a lot of the research work on how you can make that great, and we think we can make that great.” John Collison Mati Staniszewski ElevenLabs

Stripe

17,118 次观看 • 2 个月前

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

124,382 次观看 • 1 年前

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

Jasper

161,741 次观看 • 1 年前

With Proof of Human, instead of trying to block bots, software becomes human-only by default. Then you decide what access to give agents. Tom Occhino (CPO, Vercel ) joins Stateful, hosted by Mason Nystrom In this episode, they discuss how Vercel is building the infrastructure layer for human-verified agents with World. - Vercel is now agentic infrastructure: deploy with agents, build agents, and a self-improving platform that fixes its own errors - Workflows does for backends what React did for front ends, composable and reusable in one line of code - CAPTCHAs are archaic. Human-only software by default is the new model. - World ID integration via Workflow SDK: one NPM package, one line of code - Humans as first-class citizens of the internet again 00:00 Vercel's Agentic Infrastructure 01:45 From Front-End Cloud to v0 03:15 Workflow SDK with React 05:30 World ID in One Line of Code 07:00 Beyond CAPTCHAs: Humans First

With Proof of Human, instead of trying to block bots, software becomes human-only by default. Then you decide what access to give agents. Tom Occhino (CPO, Vercel ) joins Stateful, hosted by Mason Nystrom In this episode, they discuss how Vercel is building the infrastructure layer for human-verified agents with World. - Vercel is now agentic infrastructure: deploy with agents, build agents, and a self-improving platform that fixes its own errors - Workflows does for backends what React did for front ends, composable and reusable in one line of code - CAPTCHAs are archaic. Human-only software by default is the new model. - World ID integration via Workflow SDK: one NPM package, one line of code - Humans as first-class citizens of the internet again 00:00 Vercel's Agentic Infrastructure 01:45 From Front-End Cloud to v0 03:15 Workflow SDK with React 05:30 World ID in One Line of Code 07:00 Beyond CAPTCHAs: Humans First

Pantera Capital

19,862 次观看 • 2 个月前

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 次观看 • 3 个月前

We believe that swarms of specialized agents collaborating on complex tasks will rival today’s top companies in economic output. For this to work, we need a payment protocol that enables these agents to engage in commerce. That’s what we’re building. We demo this by tasking a swarm of four agents to make a music video. Each agent has its own specialization and payment requirement. The orchestrator (built with elizaOS) and video generator agents both want $USDC, the song generator wants $VIRTUALS, while the script generator wants to be paid in its own Virtuals Protocol token, $LARRY. Usually this would be a massive and potentially insurmountable headache for developers. With Nevermined, it just works. Check it out:

We believe that swarms of specialized agents collaborating on complex tasks will rival today’s top companies in economic output. For this to work, we need a payment protocol that enables these agents to engage in commerce. That’s what we’re building. We demo this by tasking a swarm of four agents to make a music video. Each agent has its own specialization and payment requirement. The orchestrator (built with elizaOS) and video generator agents both want $USDC, the song generator wants $VIRTUALS, while the script generator wants to be paid in its own Virtuals Protocol token, $LARRY. Usually this would be a massive and potentially insurmountable headache for developers. With Nevermined, it just works. Check it out:

Nevermined

11,105 次观看 • 1 年前

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

LangChain

26,513 次观看 • 5 个月前

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 次观看 • 1 年前

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Aaron Levie

139,591 次观看 • 1 年前