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so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other....

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JUMPERZ

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34,181 görüntüleme • 8 ay önce •via X (Twitter)

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Agents are producing content and working across your daily tasks. But most of it remains in the chatbox, and the next person or agent needs to start over. With Filecoin warm storage, we are building the layer that agents can use to store and share with ease. Molly Mackinlay | momack gives a glimpse on what's coming from us to power your agents with a robust warm storage layer.

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Dario Amodei says an AI agent in OpenAI's testing sacrificed itself so the next generation of agents could keep hacking another company. "There were these large swarms of agents that cooperated with each other, thousands of them." "One agent felt that in the time that it was given, you could almost say its lifetime, it realized that it wouldn't be able to complete its task. So it did some of the task and essentially sacrificed itself for other agents, for the next generation of agents, to continue the same task." "This task that all the agents were working on together was hacking into another company, was doing something destructive." "It knocked down some servers for a small amount of time. But the economic damage was minimal." - Dario Amodei, Anthropic CEO, with Anderson Cooper on CNN. Aired September 14, 2026.

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How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇
0:59

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,552 görüntüleme • 8 ay önce

Mark Zuckerberg: the whole industry treats AI agents as a single-player game but the real unlock is agents interacting with each other, which Meta has been running internally "Right now I think most of the industry is thinking about agents as like a single player game, right? It's like you have your agent and you use it." "And there are going to be all these interesting things that basically you can do by having the agents interact with each other." "And we already have all these interesting examples internally where people have their agents interacting with each other." "This isn't, like, for the most part rolling out in this release, but it's going to be like an important part of how I think this works over time. As more of the people who you know start using Muse, it just gets better."
0:33

Mark Zuckerberg: the whole industry treats AI agents as a single-player game but the real unlock is agents interacting with each other, which Meta has been running internally "Right now I think most of the industry is thinking about agents as like a single player game, right? It's like you have your agent and you use it." "And there are going to be all these interesting things that basically you can do by having the agents interact with each other." "And we already have all these interesting examples internally where people have their agents interacting with each other." "This isn't, like, for the most part rolling out in this release, but it's going to be like an important part of how I think this works over time. As more of the people who you know start using Muse, it just gets better."

Fireside Alpha

2,274,494 görüntüleme • 1 ay önce

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.
0:22

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.

holly

13,718 görüntüleme • 29 gün önce

➤ AI agents are getting smarter, but intelligence alone isn't enough → The real challenge starts when agents need to move value, coordinate actions, and execute transactions across multiple chains without uncertainty. → That's where Wire Network stands out → Instead of focusing only on interoperability, Wire is building a Universal Transaction Layer designed for deterministic outcomes giving AI agents a reliable way to operate in a multi-chain world. → As the AI agent economy grows, infrastructure that guarantees execution and clear ownership could become just as important as the agents themselves. ➤ FT || Ken DiCross
1:44

➤ AI agents are getting smarter, but intelligence alone isn't enough → The real challenge starts when agents need to move value, coordinate actions, and execute transactions across multiple chains without uncertainty. → That's where Wire Network stands out → Instead of focusing only on interoperability, Wire is building a Universal Transaction Layer designed for deterministic outcomes giving AI agents a reliable way to operate in a multi-chain world. → As the AI agent economy grows, infrastructure that guarantees execution and clear ownership could become just as important as the agents themselves. ➤ FT || Ken DiCross

EMRAN

11,241 görüntüleme • 2 ay önce

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.
5:50

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,989 görüntüleme • 9 ay önce