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Hoping your coding agents could understand you and adapt to your preferences? Meet TOM-SWE, our new framework for coding agents that don’t just write code, but model the user's mind persistently (ranging from general preferences to small details) arxiv: ❓Motivation: Most coding agents today can plan, edit, run, and... show more

Xuhui Zhou

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40,202 görüntüleme • 9 ay önce •via X (Twitter)

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For a long time, software was limited by how fast people could write code, and how good that code was. As models have improved, that constraint has largely disappeared. Now the bottleneck is access: what surfaces can your agents actually reach? Those interaction layers sit on top of coding agents, the kernel that turns prompts into real-world impact. With Notion's custom agents, we pushed this further. They adapt to your work style as you collaborate with them, using the same deterministic logic that powers coding agents. Simon Last sarah guo

For a long time, software was limited by how fast people could write code, and how good that code was. As models have improved, that constraint has largely disappeared. Now the bottleneck is access: what surfaces can your agents actually reach? Those interaction layers sit on top of coding agents, the kernel that turns prompts into real-world impact. With Notion's custom agents, we pushed this further. They adapt to your work style as you collaborate with them, using the same deterministic logic that powers coding agents. Simon Last sarah guo

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AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

DeepLearning.AI

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The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

Santiago

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New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @pauleveritt@fosstodon.org. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

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463,403 görüntüleme • 4 ay önce

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

Hasan

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yutish

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Agent apps don’t win on the model, they win on the harness. But creating that harness is painful. Terminal Use is hosting infrastructure for background agents built for iterating: fork filesystems and try multiple agents in parallel. The platform is CLI-first, which allows your coding agents like Claude Code & Codex to help you debug, hypothesize improvements, and analyze outputs. Congrats on the launch Vivek Raja, Stavros Filosidis, and Filip Balucha!

Y Combinator

61,540 görüntüleme • 6 ay önce

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Andrew Ng

139,978 görüntüleme • 1 yıl önce

We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:

Simular

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Introducing Coldtea. The ADE for shipping self-driving software. Purpose-built for the entire software lifecycle. Other IDEs optimise for one thing: Build. Run more agents in parallel, ship faster, orchestrate better. But software engineering is not just about building. Coldtea AI brings your coding agents, agentic end-to-end testing, and production monitoring agents into ONE development environment, so you can move at agent speed without breaking anything.

Introducing Coldtea. The ADE for shipping self-driving software. Purpose-built for the entire software lifecycle. Other IDEs optimise for one thing: Build. Run more agents in parallel, ship faster, orchestrate better. But software engineering is not just about building. Coldtea AI brings your coding agents, agentic end-to-end testing, and production monitoring agents into ONE development environment, so you can move at agent speed without breaking anything.

Ohans Emmanuel

437,644 görüntüleme • 15 gün önce

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

André König

36,124 görüntüleme • 4 ay önce

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the form’s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKit’s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is *on* the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes *without* modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasn’t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the form’s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKit’s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is on the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes without modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasn’t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️

Justin Schroeder

11,677 görüntüleme • 4 ay önce

AI writes all your code. Does it still work? That's the gap: coding agents are scaling output, but the same with bugs and incidents. The biggest bottleneck is QA: how can you make sure the code your agents just wrote doesn’t break everything? The best QA are the ones who know everything about your product. Today, we give that knowledge to Momentic’s most capable agent. Every Linear ticket, every PRD in Notion, every PR - Momentic knows all. In just the past few weeks, our agents analyzed 70k+ test failures and created 600 tests. Achieving a 73% PR merge rate, and it’s growing fast. We’re grateful for the trust of our customers, including Notion, Xero, Webflow, Retool, Runway, Bilt, and more.

AI writes all your code. Does it still work? That's the gap: coding agents are scaling output, but the same with bugs and incidents. The biggest bottleneck is QA: how can you make sure the code your agents just wrote doesn’t break everything? The best QA are the ones who know everything about your product. Today, we give that knowledge to Momentic’s most capable agent. Every Linear ticket, every PRD in Notion, every PR - Momentic knows all. In just the past few weeks, our agents analyzed 70k+ test failures and created 600 tests. Achieving a 73% PR merge rate, and it’s growing fast. We’re grateful for the trust of our customers, including Notion, Xero, Webflow, Retool, Runway, Bilt, and more.

wei-wei

972,042 görüntüleme • 2 ay önce

do you understand what just shipped? → AI agents can now design directly on Figma’s canvas. not cheesy mockups… or lame screenshots… real native Figma assets wired to your actual design system → the use_figma MCP tool lets Claude Code, Codex, Cursor, and 6 other coding agents write directly to your Figma files → agents read your component library first and build with what already exists… variables, tokens, auto layout, the works → skills let you teach agents HOW your team designs. a skill is just a markdown file… anyone who understands Figma can write one → also works with Copilot CLI, Copilot in VS Code, Factory, Firebender, Augment, and Warp → free during beta… usage based pricing coming later the design to code gap that’s haunted every product team just collapsed in front of our eyes. designers hand off to agents now no need to wait on developers anymore everyone can take a deep breath now if you’re building products and not connecting Figma to your agents yet, you’re leaving serious speed on the table. set this up today. you’ll thank me later

do you understand what just shipped? → AI agents can now design directly on Figma’s canvas. not cheesy mockups… or lame screenshots… real native Figma assets wired to your actual design system → the use_figma MCP tool lets Claude Code, Codex, Cursor, and 6 other coding agents write directly to your Figma files → agents read your component library first and build with what already exists… variables, tokens, auto layout, the works → skills let you teach agents HOW your team designs. a skill is just a markdown file… anyone who understands Figma can write one → also works with Copilot CLI, Copilot in VS Code, Factory, Firebender, Augment, and Warp → free during beta… usage based pricing coming later the design to code gap that’s haunted every product team just collapsed in front of our eyes. designers hand off to agents now no need to wait on developers anymore everyone can take a deep breath now if you’re building products and not connecting Figma to your agents yet, you’re leaving serious speed on the table. set this up today. you’ll thank me later

klöss

101,073 görüntüleme • 5 ay önce

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"

Y Combinator

156,937 görüntüleme • 2 ay önce

🤖Coding agents like Claude Code are already game changers for digital tasks in 2026. But what if they could write code to build physical worlds? 🏙️ Imagine going from a single line of prompt → a controllable, interactive simulated world. Such environments could open new frontiers for game creation, RL training, large-scale world simulation, and studying complex social reasoning. Our SimWorld agent coding team is working toward releasing a platform that lets anyone build their own virtual worlds. Stay tuned.

🤖Coding agents like Claude Code are already game changers for digital tasks in 2026. But what if they could write code to build physical worlds? 🏙️ Imagine going from a single line of prompt → a controllable, interactive simulated world. Such environments could open new frontiers for game creation, RL training, large-scale world simulation, and studying complex social reasoning. Our SimWorld agent coding team is working toward releasing a platform that lets anyone build their own virtual worlds. Stay tuned.

Lianhui Qin

10,647 görüntüleme • 5 ay önce

Sam Altman: "I can kind of automate an entire startup of agents and four of us. Four people and all this compute." This might be the biggest startup opportunity of 2026. He says the startups being created today could be far more valuable than the startups of the past. Why? What took his first YC startup 3 months to build can now be done by a coding agent in minutes. And his advice is simple: "You can now do three months of work in 17 minutes, but you better just go do three months of work." Most founders are still using agents to write code. The next ones will build entire companies around them. But that starts with knowing how to build agents that actually work. Here's your chance to learn and build one today ↓

Sam Altman: "I can kind of automate an entire startup of agents and four of us. Four people and all this compute." This might be the biggest startup opportunity of 2026. He says the startups being created today could be far more valuable than the startups of the past. Why? What took his first YC startup 3 months to build can now be done by a coding agent in minutes. And his advice is simple: "You can now do three months of work in 17 minutes, but you better just go do three months of work." Most founders are still using agents to write code. The next ones will build entire companies around them. But that starts with knowing how to build agents that actually work. Here's your chance to learn and build one today ↓

Rahul

22,194 görüntüleme • 18 gün önce

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

125,326 görüntüleme • 8 ay önce

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 görüntüleme • 4 ay önce