Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

🤯 Hermes agent just got a Kanban task board, and it changes everything. Watch multiple AI agents collaborate in real time, each with a defined role, completing tasks with way more depth than your typical sub-agents. This is the future of multi-agent workflows 👇

Boxmining

176,559 subscribers

12,794 görüntüleme • 1 ay önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

🚨BREAKING: Hermes Agent v0.11 Just Changed AI Agents Forever! Hermes Agent just dropped V0.11. Biggest update ever. 🔥 → Brand new terminal interface. →Unlimited sub-agents spawning sub-agents. →GPT-5.5 now works inside it. →Access to every major AI model from one tool. →Redirect your agent MID-task with one command. This is a full rebuild. 🤯 FREE and open source ↓

Julian Goldie SEO

23,990 görüntüleme • 2 ay önce

Hermes Agent now has multi-agent via the Kanban, new in v0.12.0. Agents claim tasks from a board, work in parallel, and hand off when blocked. You watch progress and unblock from one easy view instead of juggling terminals. We asked it to plan and make this video about itself:

Hermes Agent now has multi-agent via the Kanban, new in v0.12.0. Agents claim tasks from a board, work in parallel, and hand off when blocked. You watch progress and unblock from one easy view instead of juggling terminals. We asked it to plan and make this video about itself:

Nous Research

1,457,021 görüntüleme • 1 ay önce

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

349,977 görüntüleme • 2 yıl önce

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:

n8n.io

27,766 görüntüleme • 10 ay önce

Hermes Desktop just removed the biggest barrier to AI agents. No more terminal. Here’s what changed everything: → You can now run Hermes in a clean Mac app → Edit memory + skills without SSH → Search every past conversation instantly → Run multi-agent workflows visually → Assign tasks like a real team board This turns AI from “tool” → “operator.” Save this video, you’ll finally understand how agents actually work. Want the SOP? DM me. 💬

Hermes Desktop just removed the biggest barrier to AI agents. No more terminal. Here’s what changed everything: → You can now run Hermes in a clean Mac app → Edit memory + skills without SSH → Search every past conversation instantly → Run multi-agent workflows visually → Assign tasks like a real team board This turns AI from “tool” → “operator.” Save this video, you’ll finally understand how agents actually work. Want the SOP? DM me. 💬

Julian Goldie SEO

42,735 görüntüleme • 1 ay önce

🚀 LAUNCHING: Genspark Multi-Agent Orchestration! One lead agent coordinates multiple sub-agents to tackle complex tasks. Watch the demo: Create 10 agents simultaneously - 10 professional slide decks in the time it takes to make one. Experience it: Blog:

🚀 LAUNCHING: Genspark Multi-Agent Orchestration! One lead agent coordinates multiple sub-agents to tackle complex tasks. Watch the demo: Create 10 agents simultaneously - 10 professional slide decks in the time it takes to make one. Experience it: Blog:

Genspark

23,144 görüntüleme • 11 ay önce

OPENCLAW JUST GOT A FREE MISSION CONTROL DASHBOARD CALLED NERVE. One clean interface to run multiple agents with kanban, voice mode, sub agent control, and live memory editing.

OPENCLAW JUST GOT A FREE MISSION CONTROL DASHBOARD CALLED NERVE. One clean interface to run multiple agents with kanban, voice mode, sub agent control, and live memory editing.

0xMarioNawfal

95,526 görüntüleme • 3 ay önce

Most AI agents work alone. Today, @convergence_ai_ just leveled up again with `Parallel Agents`: A swarm-style system that splits your task into sub-tasks, spins up multiple agents (each in its own browser!), and completes complex work in seconds. Here’s how it works. ↓

Most AI agents work alone. Today, @convergence_ai_ just leveled up again with `Parallel Agents`: A swarm-style system that splits your task into sub-tasks, spins up multiple agents (each in its own browser!), and completes complex work in seconds. Here’s how it works. ↓

Charly Wargnier

55,751 görüntüleme • 1 yıl önce

this is wild 🤯🤯🤯 just paired openai operator with replit agent to build an app watch as two ai agents team up, exchange credentials, and start testing ai agent 🤝 ai agent the future is here and it’s insane!

this is wild 🤯🤯🤯 just paired openai operator with replit agent to build an app watch as two ai agents team up, exchange credentials, and start testing ai agent 🤝 ai agent the future is here and it’s insane!

Lamar

1,820,924 görüntüleme • 1 yıl önce

Preview of our DeepResearchSwarm™ It's incomprehensibly fast as it leverages a multi-agent architecture instead of just one agent. It utilizes a joint hierarchical-parallel architecture that creates a plan and then concurrently executes the plan with multiple search agents. Workflow: Task → Research Director → multiple search agents → final summary by summarization agent. Stay tuned for the release ↓

Preview of our DeepResearchSwarm™ It's incomprehensibly fast as it leverages a multi-agent architecture instead of just one agent. It utilizes a joint hierarchical-parallel architecture that creates a plan and then concurrently executes the plan with multiple search agents. Workflow: Task → Research Director → multiple search agents → final summary by summarization agent. Stay tuned for the release ↓

swarms

17,729 görüntüleme • 1 yıl önce

🚨 Real-time. Multi-tasking. Conversational. ASI-1 Mini just got smarter.⚡ Now agents can: ✅ Run long tasks in the background ✅ Continue sessions later ✅ Execute multiple workflows Try now 👉 Watch an in-depth walkthrough 👇

🚨 Real-time. Multi-tasking. Conversational. ASI-1 Mini just got smarter.⚡ Now agents can: ✅ Run long tasks in the background ✅ Continue sessions later ✅ Execute multiple workflows Try now 👉 Watch an in-depth walkthrough 👇

Fetch.ai

62,414 görüntüleme • 1 yıl 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

105,261 görüntüleme • 1 yıl önce

It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!

It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!

elvis

45,690 görüntüleme • 11 ay önce

🚨 Agent Swarms - Multi-Agents Delegate Complex Prompts To Sub-Agents Use Gemini 3.5 Flash, Opus 4.7 and GPT 5.5 xHIgh to create complex multi-agent system A master agent can orchestrate several worker agents to just do things Build full-stack apps, mobile apps and automate complex scheduled tasks Each agent can do different tasks including Q/A, monitoring and software development

🚨 Agent Swarms - Multi-Agents Delegate Complex Prompts To Sub-Agents Use Gemini 3.5 Flash, Opus 4.7 and GPT 5.5 xHIgh to create complex multi-agent system A master agent can orchestrate several worker agents to just do things Build full-stack apps, mobile apps and automate complex scheduled tasks Each agent can do different tasks including Q/A, monitoring and software development

Abacus.AI

82,815,882 görüntüleme • 1 ay önce

HERMES AGENT JUST GOT A FULL NATIVE DESKTOP APP. MULTI-AGENT MANAGEMENT, PERSISTENT MEMORY AND AUTONOMOUS WORKFLOWS ALL IN ONE PLACE. No more CLI only. Run your 24/7 self-evolving AI agent from a proper desktop UI.

HERMES AGENT JUST GOT A FULL NATIVE DESKTOP APP. MULTI-AGENT MANAGEMENT, PERSISTENT MEMORY AND AUTONOMOUS WORKFLOWS ALL IN ONE PLACE. No more CLI only. Run your 24/7 self-evolving AI agent from a proper desktop UI.

0xMarioNawfal

187,562 görüntüleme • 1 ay önce

THE FUTURE OF WORK: AI AGENT ORG CHARTS ARE HERE Companies are creating "AI heads of departments" — each managing 5–7 sub-agents to handle tasks just like a real team. Execs delegate to a chief agent, who routes tasks to department heads, who then deploy specialized sub-agents. Employees access their department's agent through a chatbot or voicebot — making AI as easy to use as Slack. Source: benjamlns on IG

THE FUTURE OF WORK: AI AGENT ORG CHARTS ARE HERE Companies are creating "AI heads of departments" — each managing 5–7 sub-agents to handle tasks just like a real team. Execs delegate to a chief agent, who routes tasks to department heads, who then deploy specialized sub-agents. Employees access their department's agent through a chatbot or voicebot — making AI as easy to use as Slack. Source: benjamlns on IG

Mario Nawfal

1,579,106 görüntüleme • 1 yıl önce

🤝 AI Agents That Work Together — Collaboration Made Simple! In this demo, John explores one of Agent Forge’s most powerful features: agent-to-agent collaboration. With Agent Forge, you can create agents with different roles, connect them, and watch them collaborate to solve complex tasks. Modular, scalable, and fully automated. 🔗 Try here:

🤝 AI Agents That Work Together — Collaboration Made Simple! In this demo, John explores one of Agent Forge’s most powerful features: agent-to-agent collaboration. With Agent Forge, you can create agents with different roles, connect them, and watch them collaborate to solve complex tasks. Modular, scalable, and fully automated. 🔗 Try here:

AITECH

43,206 görüntüleme • 10 ay önce

Introducing Upstash AI Agents! 🎉 ◆ Build production-ready AI workflows in minutes ◆ Specialize agents with tools (research, writing, analysis) ◆ Works with Vercel AI SDK, Langchain and more The best way to write AI agent workflows, fully customizable 👇🏻

Introducing Upstash AI Agents! 🎉 ◆ Build production-ready AI workflows in minutes ◆ Specialize agents with tools (research, writing, analysis) ◆ Works with Vercel AI SDK, Langchain and more The best way to write AI agent workflows, fully customizable 👇🏻

Upstash

39,117 görüntüleme • 1 yıl önce

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,140 görüntüleme • 1 yıl önce

I built a real estate AI Agent team with multi-agents using gpt-OSS and Firecrawl. It has 3 AI agents working together as a team: 1. Property Search Agent 2. Market Analysis Agent 3. Property Valuation Agent 100% Opensource with step-by-step tutorial.

I built a real estate AI Agent team with multi-agents using gpt-OSS and Firecrawl. It has 3 AI agents working together as a team: 1. Property Search Agent 2. Market Analysis Agent 3. Property Valuation Agent 100% Opensource with step-by-step tutorial.

Shubham Saboo

116,631 görüntüleme • 10 ay önce