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Cloud agents keep working while you go get pizza. “It has done excellent work across six different repositories, fixing quite a big issue in our system.” Dhravya Shah from supermemory gave Vorflux three engineering tasks before heading out to eat. He came back to changes and a migration file... show more

Prasanna S

46,798 subscribers

14,827 görüntüleme • 23 gün önce •via X (Twitter)

Anya Rossi• Live Now

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6 Yorum

weisser profil fotoğrafı

weisser23 gün önce

@DhravyaShah @supermemory great video. really captured the vibe of the space!

Dhravya Shah profil fotoğrafı

Dhravya Shah23 gün önce

@supermemory great working with you guys!

Andrei Shemet profil fotoğrafı

Andrei Shemet23 gün önce

@DhravyaShah @supermemory The fact that it worked across six repos and still left clean changes is crazy!

aniketh profil fotoğrafı

aniketh23 gün önce

@DhravyaShah @supermemory @DhravyaShah lessgo

dCodes profil fotoğrafı

dCodes23 gün önce

@DhravyaShah @supermemory Crazy

Kinjal Goswami profil fotoğrafı

Kinjal Goswami23 gün önce

@DhravyaShah @supermemory coming back from lunch to a fully fixed codebase is literally the dream.....

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Communities can now build with a coordinated team of AI agents. Agent execution is live on connecting community decisions to the agents that carry them out. $ZZZ brings the full workflow into one workspace: Discussion → decisions → tasks → execution. 1. Members chat and vote. An agent creates tasks from those discussions and records the decisions. 2. The lead agent prioritises tasks, clarifies the details with members and presents interactive mockups for approval. 3. The lead agent coordinates a swarm of agents to complete the tasks. Watch a community discussion become a task, then see the agents get to work.

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The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

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Windows users, this one’s for you. Computer use now works on Windows, so Codex can take action on your Windows computer. And with Windows support for Codex in the ChatGPT mobile app, you can start, review, and steer tasks on the go while work continues on your Windows machine. An early experience, but we’re working on more ways to keep your work moving, wherever you are.

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Bryan Tsao is joining Legora as our Chief Product Officer. He was CPO at Jasper. Before that he ran Product, Growth and Data at Dropbox, led EPD at Namely from Series C through Series E, and helped scale Kabam from 16 to 800 people through a $1 billion exit. Across all four, the same thing showed up: a data-driven rigour and an execution obsession that lands directly in product quality. He's stepping into a product team that already knows what agentic legal work demands. The work has to be precise, fast, and verifiably correct, no exceptions. Legal work doesn't forgive sloppiness, and as Legora becomes the standard platform for global legal work, product has to keep up with both our ambition and what the profession expects of us. Legora now has more than 100,000 lawyers and 1,500+ organisations across 50 markets. Bryan is joining at exactly the point where that scale needs someone who can build and grow it with discipline. Welcome, Bryan!

Max Junestrand

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We put GPT-6 Astra from OpenAI to work on some of the hardest reasoning in enterprise knowledge work, untangling conflicting records and different approval requirements across a complex loan amendment. In this Box Agent demo, Astra reviews 23 documents to determine which lenders can vote on a loan amendment. It corrects the preliminary tally and shows why the base amendment has enough consent while changes to pricing, maturity, and collateral do not. It also identifies a covenant breach that an earlier waiver doesn’t cover and saves a source-cited readiness report back to Box for human review. Customers can build custom agents for their own document-heavy lending workflows with Box AI Studio.

We put GPT-6 Astra from OpenAI to work on some of the hardest reasoning in enterprise knowledge work, untangling conflicting records and different approval requirements across a complex loan amendment. In this Box Agent demo, Astra reviews 23 documents to determine which lenders can vote on a loan amendment. It corrects the preliminary tally and shows why the base amendment has enough consent while changes to pricing, maturity, and collateral do not. It also identifies a covenant breach that an earlier waiver doesn’t cover and saves a source-cited readiness report back to Box for human review. Customers can build custom agents for their own document-heavy lending workflows with Box AI Studio.

Box

472,097 görüntüleme • 1 ay önce

Google just dropped the best 1-hour course on Graph Engineering: from one agent to Loops and Graphs 00:00 - your first AI agent 08:24 - build agent memory 28:34 - agentic loops 40:04 - how to build MCP 1:00:22 - graph engineering free, and the best thing on graph engineering I have come across Prompts → Agents → Loops → Graphs most people will stop after the memory chapter and call it a system he saves the last twenty minutes for the layer above all of it same model, same tokens, completely different week watch it today the step-by-step guide is below, save it while it is still early ↓

Google just dropped the best 1-hour course on Graph Engineering: from one agent to Loops and Graphs 00:00 - your first AI agent 08:24 - build agent memory 28:34 - agentic loops 40:04 - how to build MCP 1:00:22 - graph engineering free, and the best thing on graph engineering I have come across Prompts → Agents → Loops → Graphs most people will stop after the memory chapter and call it a system he saves the last twenty minutes for the layer above all of it same model, same tokens, completely different week watch it today the step-by-step guide is below, save it while it is still early ↓

Residual

95,395 görüntüleme • 27 gün önce

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

105,839 görüntüleme • 1 yıl önce

Notion 3.0 with Agents is out today! It's the first Knowledge Work Agent in the world. It works with Notion databases. It can do multi-step actions and autonomous work up to 20+ mins, and a brand-new memory system (using Notion pages and databases! 👌) With 3.0, Notion isn’t just your tool — it’s your teammate that works while you sleep. Two types of agents: Personal Agent: Can do anything you can do in Notion. Remembers and learns your preferences as you work. Has a little personality (accessories like a duck hat 🦆.) Custom Agents (early access): Spin up a small team. Hand them the repetitive stuff. They work while you sleep! And Notion Agents work across Notion Calendar, Notion Mail, plus MCP and more. Available today! Try it. Tell us what’s missing. We’ll keep shipping, so you can focus on your life’s work 🫡

Ivan Zhao

201,543 görüntüleme • 1 yıl önce

I created a free Markdown file that launches a 5-agent swarm team directly in Slack. I love Claude Code, but it's not the easiest place to manage your agents. Slack is where teams already work. So I put my agents there too. Drop the file in, and you get 5 agents, each with its own channel and job: • [Agent 1]: [what it does] • [Agent 2]: [what it does] • [Agent 3]: [what it does] • [Agent 4]: [what it does] • [Agent 5]: [what it does] You can assign work by messaging them like real employees, and they report back in the same threads. It's the closest I've found to running a real company with AI employees.

I created a free Markdown file that launches a 5-agent swarm team directly in Slack. I love Claude Code, but it's not the easiest place to manage your agents. Slack is where teams already work. So I put my agents there too. Drop the file in, and you get 5 agents, each with its own channel and job: • [Agent 1]: [what it does] • [Agent 2]: [what it does] • [Agent 3]: [what it does] • [Agent 4]: [what it does] • [Agent 5]: [what it does] You can assign work by messaging them like real employees, and they report back in the same threads. It's the closest I've found to running a real company with AI employees.

Miles Deutscher

24,757 görüntüleme • 1 gün önce

Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

Lunar

227,273 görüntüleme • 2 ay önce